diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7b09b58 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +**/.ipynb_checkpoints/** diff --git a/CS411-stats-exercises.ipynb b/CS411-stats-exercises.ipynb new file mode 100644 index 0000000..4b3b256 --- /dev/null +++ b/CS411-stats-exercises.ipynb @@ -0,0 +1,1146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " CS411 - Autumn 2019-2020
\n", + " Patrick Jermann

\n", + " How to use this notebook?\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The MOOC dataset\n", + "The data we use in this document to demonstrate the use of basic statistics is about students' performance in academic exams (e.g. the algebra course taken at the university) and their activitiy online in a related Massive Open Online Course (e.g. the algebra MOOC). The general question we try to answer is: what is the relation between the use of the MOOC by students and their academic achievement. \n", + "\n", + "**Question**: Does the use of MOOCs help succeed in courses ? Do only good students use the MOOCs ? Are MOOCs more helpful for better students ?\n", + "\n", + "As a general remark, we should note that because we did not conduct a controlled experiment with random assignment of subjects to experimental groups, we cannot establish any causal relationships between MOOC use and academic performance. Rather, we use statistical tools to \"explore\" data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading libraries\n", + "Some extra libraries are needed to perform some operations. We collect the commands to load the libraries in this cell. Executing this cell first ensures that all the necessary libraries are loaded. If one library is missing it can be installed via the install.packages() command. Simply add the line to the cell and re-execute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Uncomment and run once if these packages are not installed yet.\n", + "# install.packages(\"gdata\")\n", + "# install.packages(\"gplots\")\n", + "# install.packages(\"car\")\n", + "# install.packages(\"ggpubr\")\n", + "\n", + "library(gdata) # reorder\n", + "library(gplots) # plotmeans\n", + "library(plyr) # ddply\n", + "library(dplyr) # summarySE\n", + "library(ggplot2) # plotting library\n", + "library(ggpubr) # plotting\n", + "library(car) # Anova\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Loading Data\n", + "Let's load data. The first step is to read a CSV file into a dataframe, which is the data structure used by R to store data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# TODO: Adapt the path to your own installation.\n", + "# Set the working directory\n", + "setwd(\"./\")\n", + "\n", + "# Read a csv file (header = T signals that the first line of the file contains the names of the variables).\n", + "# The <- operator assigns the result of read.csv to the variable moocs.\n", + "# moocs is a dataframe, similar to an excel worksheet or a DataFrame in pandas.\n", + "moocs <- read.csv(file=\"moocs.basic.csv\", header = T)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our dataset contains variables which name starts with *EPFL* and other variables starting with *MOOC*. The variables with EPFL come from the academic database and describe student's performance in their EPFL exams. The variables starting with MOOC conern their online activity in the MOOC." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# List the names of the variables\n", + "names(moocs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Descriptive Statistics\n", + "\n", + "## Summary for variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each variable, print some information about the distribution (if the variable is quantitative) \n", + "# or about the distinct values taken by the variable (if the variable is categorical). \n", + "\n", + "summary(moocs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To get information about only one variable, use the $ sign to separate the name of \n", + "# the dataframe and the name of the variable like so: $\n", + "\n", + "# For nominal variables we get the count for the different categories \n", + "summary(moocs$EPFL_StudentSection)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For a quantitative variable we get the ususal central tendency and dispersion indicators\n", + "summary(moocs$MOOC_PercentageVideosWatched)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We can also compute these separately.\n", + "mean(moocs$MOOC_PercentageVideosWatched, na.rm=T) # na.rm removes missing values\n", + "sd(moocs$MOOC_PercentageVideosWatched, na.rm=T) # standard deviation\n", + "median(moocs$MOOC_PercentageVideosWatched, na.rm=T) # median (50% observations below and 50% observations above)\n", + "max(moocs$MOOC_PercentageVideosWatched, na.rm=T) # maximum\n", + "min(moocs$MOOC_PercentageVideosWatched, na.rm=T) # minimum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " TODO : What is the mean and the standard deviation of the EPFL_CourseGrade ?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "################\n", + "# Begin solution\n", + "\n", + "\n", + "\n", + "# End solution\n", + "###############" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining new categorical variables\n", + "It is sometimes useful to transform a quantitative variable into a categorical variable. \n", + "\n", + "For example, below we create a variable that reflects whether students used the MOOC or not based on the number of videos they watched and the number of problems they attempted in the MOOC. In R, categorical variables are created with the function `factor`. The variable `didMOOC` is given the value `NO.MOOC` whenever the values for the number of videos watched and the number of problems attempted is missin. In other cases, the value is `MOOC`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "moocs$didMOOC = factor(\n", + " ifelse(\n", + " is.na(moocs$MOOC_NVideosWatched) & \n", + " is.na(moocs$MOOC_NProblemsSolved), \n", + " \"NO.MOOC\", \n", + " \"MOOC\")\n", + ")\n", + "\n", + "summary(moocs$didMOOC)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We consider that not taking the MOOC is like not watching videos\n", + "moocs$watched_videos = factor(\n", + " ifelse(\n", + " moocs$MOOC_PercentageVideosWatched == 0 |\n", + " is.na(moocs$MOOC_PercentageVideosWatched),\n", + " \"NO.VIDEO\",\n", + " \"SOME.VIDEO\")\n", + " )\n", + "\n", + "summary(moocs$watched_videos)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We consider that not taking the MOOC is like not solving any problems\n", + "moocs$solved_problems = factor(\n", + " ifelse(\n", + " moocs$MOOC_PercentageProblemsSolved == 0 |\n", + " is.na(moocs$MOOC_PercentageProblemsSolved),\n", + " \"NO.PROBLEM\",\n", + " \"SOME.PROBLEM\")\n", + " )\n", + "\n", + "summary(moocs$solved_problems)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking activity types\n", + "Do people who solve problems also watch videos ? When using a MOOC, some students only watch videos and don't do exercices. But most of the students who do exercices also watch videos. It appears that almost nobody is *only* solving problems. \n", + "> **Problem**: If we use these variables as two factors to analyse our data set we'll have very uneven groups. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# With two arguments, the table command cross-tabulates the number of observations per category.\n", + "table(moocs$watched_videos, moocs$solved_problems)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " TODO : To fix this problem we qualify MOOC usage with three categories: \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* `NONE` corresponds to no use of MOOCs, \n", + "* `WATCH` corresponds to students mainly watching videos and \n", + "* `DO` corresponds to students doing some exercices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#################\n", + "# Begin Solution:\n", + "\n", + "moocs$MOOC <- ...\n", + "\n", + "\n", + "# End Solution \n", + "###############\n", + "\n", + "summary(moocs$MOOC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The order of the categories is a bit 'unlogical'. We'd like to have them appear in outputs and graphs sorted by increasing level of involvement: NONE, WATCH and DO. Let's fix this problem by reordering the levels of the factor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "library(gdata)\n", + "# put the levels into a convenient order (for graphing, etc.)\n", + "moocs$MOOC <- reorder(moocs$MOOC, new.order=c(\"NONE\", \"WATCH\", \"DO\"))\n", + "table(moocs$MOOC)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Do people who use the MOOC get better grades ?\n", + "\n", + "## Looking at the means.\n", + "Let's start by looking at the mean grade for the three groups of our variable `MOOC`. The `ddply` function allows to apply a function to a subset of the data defined by a categorical variable (`MOOC` in our case). The ddply returns a new data frame with the category and the result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "mu <- ddply(moocs, \"MOOC\", summarise, grp.mean=mean(EPFL_CourseGrade))\n", + "mu\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Looking at the variance ?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "sigma <- ddply(moocs, \"MOOC\", summarise, grp.sd=sd(EPFL_CourseGrade))\n", + "#head(sigma)\n", + "\n", + "info <- merge(sigma, mu)\n", + "info\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "To further investigate the difference between the ghree groups, let's plot the grade of the students `EPFL_CourseGrade` against the categorical variable `MOOC` that we just defined. We'll use three different plots: \n", + "\n", + "* boxplot, \n", + "* histogram and \n", + "* mean plots.\n", + "\n", + "### Boxplots\n", + "\n", + "Boxplots allow to visualise the distribution of the variable. The central line in the box shows the median and the edges of the boxes show the interquartiles. A simple tutorial for variations of this type of plot is available here: http://www.sthda.com/english/wiki/ggplot2-box-plot-quick-start-guide-r-software-and-data-visualization.\n", + "\n", + "From the plot below, it seems that the distribution of grades for people who did not use the MOOC (`NONE`) tends to be lower in the grade scale than the grades of people who `WATCH` videos or people who `DO` assignments. From the shape of the boxesm we also see that the distributions for `NONE` and `WATCH` are more symmetric than for `DO`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Setting the size of plots generated with ggplot below. \n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "\n", + "# Quick and simple boxplot\n", + "boxplot(moocs$EPFL_CourseGrade ~ moocs$MOOC, main=\"Grade by Activity Level\", xlab=\"Activity Level\", ylab=\"Grade\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Setting the size of plots generated with ggplot below. \n", + "options(repr.plot.width = 8, repr.plot.height = 5)\n", + "\n", + "# More sophisticated plots with GGPLOT.\n", + "# Tutorial: http://www.sthda.com/english/wiki/ggplot2-box-plot-quick-start-guide-r-software-and-data-visualization\n", + "ggplot(moocs, aes(x=MOOC, y=EPFL_CourseGrade, fill=MOOC)) +\n", + " geom_boxplot(notch=TRUE) + coord_flip() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Histograms\n", + "\n", + "Histograms give a more precise representation of the actual grade distribution. Below, we define bins of 0.5 to represent the frequency of the different grades. The vertical dashed lines represent the means of the distributions. Notice that we reuse the dataframe `mu` that we defined above. We see indeed that the `DO` group is less symmetric than the others." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Tutorial about generating such a figure\n", + "# http://www.sthda.com/english/wiki/ggplot2-histogram-plot-quick-start-guide-r-software-and-data-visualization\n", + "options(repr.plot.width = 8, repr.plot.height = 5)\n", + "\n", + "ggplot(moocs, aes(x=EPFL_CourseGrade, fill=MOOC)) +\n", + " geom_histogram(binwidth=.5, alpha=.5, position=\"dodge\") + \n", + " geom_vline(data=mu, aes(xintercept=grp.mean, color=MOOC), linetype=\"dashed\") \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean plots\n", + "Mean plots represent the mean along with the confidence interval for the mean. There is a 95% chance that the confidence interval contains the true mean of `EPFL_CourseGrade` for the population. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "\n", + "# plotmeans is defined in the gplots library \n", + "# make sure to load the library by executing > library(gplots) before. \n", + "plotmeans(moocs$EPFL_CourseGrade ~ moocs$MOOC, main=\"Grade given MOOC activity\", ylab=\"Grade\", xlab=\"Activity Level\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "\n", + "# Alternate form using the package ggpubr\n", + "# See examples here: http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/79-plot-meansmedians-and-error-bars/\n", + "\n", + "ggline(moocs, x = \"MOOC\", y = \"EPFL_CourseGrade\", \n", + " add = c(\"mean_ci\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ANOVA - Analysis of Variance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: ANOVA a.k.a. ANalysis Of VAriance \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ANOVA allows comparing whether one variable has the same mean in different groups ? The formula to specify the test is done in tow steps. First we define a linear model with `m <- lm(quantitative ~  categorical)` and then we do the Anova of the model `Anova(m)`.\n", + "\n", + "* H0: mean_1 = mean_2 = ...= Mean_n\n", + "* H1: mean_1 != mean_2 != ... != mean_n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the Analysis of Variance (ANOVA) to compare the mean of a variable across several modalities of a categorical variable. In the example below, we compare the course grade (`EPFL_CourseGrade`) across different modalities of MOOC usage (the `MOOC` variable takes three possible values: `NONE`, `WATCH` and `DO`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# needs library(car)\n", + "\n", + "m <- lm(moocs$EPFL_CourseGrade ~ moocs$MOOC) \n", + "Anova(m)\n", + "\n", + "r <- residuals(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This is the way you report the ANOVA in an article.\n", + "\n", + "> We conducted a one-way ANOVA to test whether the course grades differ for different categories of MOOC usage. There is a significant difference of course grades across different types of MOOC use (F[2,9311]=291.6, p<.001). \n", + "\n", + "Beware, that since we did not randomly assign subjects to experimental groups, but rather students chose whether they use the MOOC or not, we can't conclude about any causal effect. It is possible that better students are inclided to use MOOCs more than weaker students. Hence, the \"effect of\" the MOOC would be due to this hidden variable which actually explains the difference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Advanced topic : There are different ways to run anovas in R. They differ with regards to the type of Sum of Square that they report. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following http://goanna.cs.rmit.edu.au/~fscholer/anova.php the Type III Sum of Square is more adapted to interpret the contributions of the differnt variables. R and anova report Type I sums of square.\n", + "\n", + "Here is an explanation about how to obtain Type III sums of squares: https://www.r-bloggers.com/anova-%E2%80%93-type-iiiiii-ss-explained/\n", + "And some explanations here: prometheus.scp.rochester.edu/zlab/sites/default/files/InteractionsAndTypesOfSS.pdf\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Which groups differ among each other ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Pair-wise t-test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A pairwise test allows to know which groups differ from each other. Because of repeated testing, we have to adjust the p-value by dividing the .05 value by the number of tests. In our case, the tests show that all groups differ from each other when taken 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Checks which group is different from what other group in doing 2 by 2 tests.\n", + "pairwise.t.test(moocs$EPFL_CourseGrade, moocs$MOOC, p.adj = \"bonf\") \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "cat(\"Means\")\n", + "tapply(moocs$EPFL_CourseGrade, moocs$MOOC, mean)\n", + "\n", + "cat(\"Standard Deviations\")\n", + "tapply(moocs$EPFL_CourseGrade, moocs$MOOC, sd)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here is the wording for the article. \n", + "\n", + "> A series of pair-wise t-tests with bonferonni adjusment for repeated testing, shows that the average grade for students who did not use the MOOC (m=3.5, sd=2.5) is smaller than the average grade of students who watched videos (m=3.8, sd=1.3) and smaller than the average grade of students who did the assignments (m=4.4, sd=1.2)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assumptions\n", + "Is it ok to run an ANOVA blindly as we did just before ? \n", + "\n", + "Not really, we should have checked the assumptions for ANOVA first.\n", + "\n", + "* Normality of the residuals\n", + "* Homoscedasticity of the residuals = the error distribution should be the same regardless of the category.\n", + "\n", + "### Visually inspecting normality Assumption\n", + "Testing normality visually with quantile plots. If the variable is normally distributed, the observations would follow the line drawn by qqline. We see from the plots below that the distribution of the residuals of our model used for the Anova departs very strongly from a normal distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# split the plotting area into 2 lines and 2 columns\n", + "par(mfrow=c(2,2))\n", + "\n", + "# Setting the size of plots below. \n", + "options(repr.plot.width = 7.5, repr.plot.height = 7.5)\n", + "\n", + "v <- rnorm(1000)\n", + "hist(v, main=\"Normal variable\")\n", + "qqnorm(v, main=\"Normal variable\");\n", + "qqline(v);\n", + "\n", + "# The residuals of the model we used to run the Anova\n", + "# r <- residuals(m)\n", + "hist(r, main=\"Residuals\")\n", + "qqnorm(r, main=\"Residuals\");\n", + "qqline(r)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normality tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Shapiro-Wilks Test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Normality can also be tested statistically, however the normality tests are rather sensitive to small deviations, especially when sample sizes are large. For example, the Shapiro-Wilks test, is testing the null hypothesis that the variable is normally distributed. In this case the adequate p.value is .1. Hence, with p <<.1 we have to reject the null hypothesis and conclude that the variable is not normally distributed. This test work with samples smaller than 5000 observations. \n", + "\n", + "* H0: variable is normally distributed\n", + "* H1: variable is not normally distributed\n", + "\n", + "We see below that for our variable `v` that we generated to be normally distributed, p = 0.47, far greater than 0.1, hence we cannot reject H0.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "shapiro.test(v)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "However, for the residuals of our Anova, we have to reject H0, and conclude that they are not normally distributed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# taking a sample of 1500 residuals for illustrating the output of the shapiro test\n", + "shapiro.test(sample(r,1500)) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing Homoscedasticity assumption\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Bartlett test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* H0: variances are the same\n", + "* H1: variances are different\n", + "\n", + "We see below, that the p-value is much smaller than .05 and therefore we reject H0. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "bartlett.test(r ~ moocs$MOOC)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "The difference in variuance is also appararent in a boxplot, where we see that the variance for the DO group is smaller. We also see that the residuals for WATCH tend to be slightly more positive than negative (their median is above 0) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "boxplot(r ~ moocs$MOOC)\n", + "abline(h=0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If variances are unequal or residuals are not normally distributed\n", + "\n", + "If variances are unequal we can either \n", + "\n", + "* adapt the oneway test by stating that `var.equal = F` \n", + "* or use a non parametric alternative to ANOVA like the Kruskal Wallis test. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# adapt the caluclations to take unequal variances into account \n", + "oneway.test(moocs$EPFL_CourseGrade ~ moocs$MOOC, var.equal = F)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Kruskal test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tests the null hypothesis that the location parameters are the same in each of the groups. This test does not make assumptions about the distribution of the residuals, nor about the variances.\n", + "\n", + "* H0: the mean ranks of observations are the same in all groups\n", + "* H1: the mean ranks of observations are different." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "kruskal.test(moocs$EPFL_CourseGrade ~ moocs$MOOC)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Because the assumption of equal variances was not respected (K2[2]=130, p< .001) in our sample, we conducted a non-parametric Kruskal-Wallis test that showed that the mean rank of course grades differs significantly across groups (X2[2]=608.7, p < .001). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise : analyse the effect of prior knowlege on the grades.\n", + "Students' baccalaureat grade is given by the variable `EPFL_BacGrade`. In the plot below we see the distribution of the grades at the baccalaureat and display the median and quartiles as vertical bars.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "options(repr.plot.width = 6, repr.plot.height = 4)\n", + "\n", + "# Remove observations for which we do not have a EPFL_BacGrade\n", + "moocs.bac <- subset(moocs, ! is.na(moocs$EPFL_BacGrade))\n", + "\n", + "# Draw a histogram with 50 bins\n", + "hist(moocs.bac$EPFL_BacGrade, breaks=50)\n", + "\n", + "# Draw a vertical line at the median\n", + "abline(v=median(moocs.bac$EPFL_BacGrade, na.rm=T), col=\"red\", lwd=3) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task 1 : Define two categorical variables named prior and prior4 that distinguish students given their baccalaureat grade. \n", + "
    \n", + "
  • prior : The values of the variable are HI for strong students and LO for weakers students. use median() to define high and low levels of ability
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Define two categorical variables named moocs$prior and moocs$prior4 that distinguishstudents given at their baccalaureat grade.\n", + "\n", + "# 1) moocs$prior. \n", + "# The values of the variable are \"HI\"\" for strong students and \"LO\"\" for weakers students.\n", + "# => use median() to define high and low levels of ability\n", + "\n", + "################\n", + "# Begin solution\n", + "moocs.bac$prior <- ...\n", + "\n", + "\n", + "# end solution\n", + "##############\n", + "\n", + "\n", + "# count the number of observations in LO and HI \n", + "table(moocs.bac$prior)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task 2 : Plot the means for the EPFL_CourseGrade given the prior grade level. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "################\n", + "# Begin Solution\n", + "\n", + "\n", + "\n", + "# end Solution\n", + "################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task 3 : Compare the means of EPFL_CourseGrade. \n", + "
    \n", + "
  • Check assumptions to run an ANOVA
    • \n", + "
  • Run an ANOVA or a replacement if the assumptions are not set
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "################\n", + "# Begin Solution\n", + "\n", + "# Build model\n", + "\n", + "# Check normality of residuals\n", + "\n", + "# Check homoscedasticity \n", + "\n", + "# Plots\n", + "\n", + "# Make the test\n", + "\n", + "# end Solution\n", + "################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two factor ANOVA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can add more than one categorical predictor and also model the interaction of two factors. When there is an interaction, the effect of one variable is conditional on the effect of another variable \n", + "\n", + "Interaction between two terms is represented by a `:`. A syntaxic equivalent for the model that contains single and interaction effects is `A * B == A + B + A:B`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "m3 <- lm(moocs.bac$EPFL_CourseGrade ~ moocs.bac$MOOC + moocs.bac$prior + moocs.bac$MOOC:moocs.bac$prior)\n", + "print(Anova(m3, type=3))\n", + "\n", + "r3 <- residuals(m3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> We conducted a 3 x 2 factorial ANOVA with prior performance and MOOC useage as factors. The results indicate a main effect of MOOC usage (F[2,374]=102.40, p<.001), as well as a main effect of prior knowledge (F[1,1179]=645.78, p<.001). There is also an interaction effect between the two factors (F[20,2]=5.3, p<.01). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "The ggline package comes in handy to plot interactions between variables. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> In our case here, we see that the relation between the different types of MOOC activity is not the same from strong or weaker students. It seems that for strong students, watching videos does not increase the score compared to doing nothing. However, doing exercices is beneficial for both strong and weak students. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ggline(moocs.bac, x = \"MOOC\", y = \"EPFL_CourseGrade\", \n", + " add = c(\"mean_ci\"),\n", + " color = \"prior\", palette = \"jco\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing Assumptions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "options(repr.plot.width = 8, repr.plot.height = 4)\n", + "par(mfrow=c(1,3))\n", + "qqnorm(r3)\n", + "qqline(r3)\n", + "\n", + "boxplot(r3 ~ moocs.bac$MOOC)\n", + "boxplot(r3 ~ moocs.bac$prior)\n", + "\n", + "bartlett.test(r3 ~ moocs.bac$MOOC)\n", + "bartlett.test(r3 ~ moocs.bac$prior)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "options(repr.plot.width = 8, repr.plot.height = 8)\n", + "par(mfrow=c(2,2))\n", + "plot(m3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choosing a non parametric version of the 2-way ANOVA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "friedman.test(moocs.bac$EPFL_CourseGrade, moocs.bac$MOOC, moocs.bac$prior)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table( moocs.bac$MOOC, moocs.bac$prior)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CS411-stats-solutions.ipynb b/CS411-stats-solutions.ipynb new file mode 100644 index 0000000..6f52a31 --- /dev/null +++ b/CS411-stats-solutions.ipynb @@ -0,0 +1,2266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " CS411 - Autumn 2019-2020
\n", + " Patrick Jermann

\n", + " How to use this notebook?\n", + "
    \n", + "
  • This notebook is made of text cells and code cells. The code cells have to be executed to see the result of the program. To execute a cell, simply select it and click on the \"play\" button (⯈) in the tool bar just above the notebook, or type shift + enter. It is important to execute the code cells in their order of appearance in the notebook.
    • \n", + "
  • To improve readability, it can be useful to hide cells from the notebook (e.g. long code cells). To hide a cell, select it and click on the blue bar which appears on its left. To make the cell visible again, just click again on the blue bar, or on the three \"dots\" which represent the collapsed cell.
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The MOOC dataset\n", + "The data we use in this document to demonstrate the use of basic statistics is about students' performance in academic exams (e.g. the algebra course taken at the university) and their activitiy online in a related Massive Open Online Course (e.g. the algebra MOOC). The general question we try to answer is: what is the relation between the use of the MOOC by students and their academic achievement. \n", + "\n", + "**Question**: Does the use of MOOCs help succeed in courses ? Do only good students use the MOOCs ? Are MOOCs more helpful for better students ?\n", + "\n", + "As a general remark, we should note that because we did not conduct a controlled experiment with random assignment of subjects to experimental groups, we cannot establish any causal relationships between MOOC use and academic performance. Rather, we use statistical tools to \"explore\" data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading libraries\n", + "Some extra libraries are needed to perform some operations. We collect the commands to load the libraries in this cell. Executing this cell first ensures that all the necessary libraries are loaded. If one library is missing it can be installed via the install.packages() command. Simply add the line to the cell and re-execute." + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Uncomment and run once if these packages are not installed yet.\n", + "# install.packages(\"gdata\")\n", + "# install.packages(\"gplots\")\n", + "# install.packages(\"car\")\n", + "# install.packages(\"ggpubr\")\n", + "\n", + "library(gdata) # reorder\n", + "library(gplots) # plotmeans\n", + "library(plyr) # ddply\n", + "library(dplyr) # summarySE\n", + "library(ggplot2) # plotting library\n", + "library(ggpubr) # plotting\n", + "library(car) # Anova\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Loading Data\n", + "Let's load data. The first step is to read a CSV file into a dataframe, which is the data structure used by R to store data." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# TODO: Adapt the path to your own installation.\n", + "# Set the working directory\n", + "setwd(\"./\")\n", + "\n", + "# Read a csv file (header = T signals that the first line of the file contains the names of the variables).\n", + "# The <- operator assigns the result of read.csv to the variable moocs.\n", + "# moocs is a dataframe, similar to an excel worksheet or a DataFrame in pandas.\n", + "moocs <- read.csv(file=\"moocs.basic.csv\", header = T)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our dataset contains variables which name starts with *EPFL* and other variables starting with *MOOC*. The variables with EPFL come from the academic database and describe student's performance in their EPFL exams. The variables starting with MOOC conern their online activity in the MOOC." + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\t
  1. 'EPFL_AcademicYear'
    2. \n", + "\t
  2. 'EPFL_IsRepeating'
    3. \n", + "\t
  3. 'EPFL_PropaedeuticGPA'
    4. \n", + "\t
  4. 'EPFL_CourseGrade'
    5. \n", + "\t
  5. 'EPFL_StudentSection'
    6. \n", + "\t
  6. 'MOOC_NVideosWatched'
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  7. 'MOOC_AverageMaxVideosWatched'
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  8. 'MOOC_PercentageVideosWatched'
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  9. 'MOOC_NProblemsSolved'
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  10. 'MOOC_AverageMaxProblemsSolved'
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  11. 'MOOC_PercentageProblemsSolved'
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  12. 'MOOC_Grade'
    13. \n", + "\t
  13. 'EPFL_Topic'
    14. \n", + "\t
  14. 'EPFL_BacGrade'
    15. \n", + "\t
  15. 'EPFL_BacLevel'
    16. \n", + "\t
  16. 'EPFL_CourseType'
    17. \n", + "\t
  17. 'EPFL_UniqueTopicID_Anonymous'
    18. \n", + "\t
  18. 'EPFL_AnonymousUserID'
    19. \n", + "
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'EPFL\\_AcademicYear'\n", + "\\item 'EPFL\\_IsRepeating'\n", + "\\item 'EPFL\\_PropaedeuticGPA'\n", + "\\item 'EPFL\\_CourseGrade'\n", + "\\item 'EPFL\\_StudentSection'\n", + "\\item 'MOOC\\_NVideosWatched'\n", + "\\item 'MOOC\\_AverageMaxVideosWatched'\n", + "\\item 'MOOC\\_PercentageVideosWatched'\n", + "\\item 'MOOC\\_NProblemsSolved'\n", + "\\item 'MOOC\\_AverageMaxProblemsSolved'\n", + "\\item 'MOOC\\_PercentageProblemsSolved'\n", + "\\item 'MOOC\\_Grade'\n", + "\\item 'EPFL\\_Topic'\n", + "\\item 'EPFL\\_BacGrade'\n", + "\\item 'EPFL\\_BacLevel'\n", + "\\item 'EPFL\\_CourseType'\n", + "\\item 'EPFL\\_UniqueTopicID\\_Anonymous'\n", + "\\item 'EPFL\\_AnonymousUserID'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'EPFL_AcademicYear'\n", + "2. 'EPFL_IsRepeating'\n", + "3. 'EPFL_PropaedeuticGPA'\n", + "4. 'EPFL_CourseGrade'\n", + "5. 'EPFL_StudentSection'\n", + "6. 'MOOC_NVideosWatched'\n", + "7. 'MOOC_AverageMaxVideosWatched'\n", + "8. 'MOOC_PercentageVideosWatched'\n", + "9. 'MOOC_NProblemsSolved'\n", + "10. 'MOOC_AverageMaxProblemsSolved'\n", + "11. 'MOOC_PercentageProblemsSolved'\n", + "12. 'MOOC_Grade'\n", + "13. 'EPFL_Topic'\n", + "14. 'EPFL_BacGrade'\n", + "15. 'EPFL_BacLevel'\n", + "16. 'EPFL_CourseType'\n", + "17. 'EPFL_UniqueTopicID_Anonymous'\n", + "18. 'EPFL_AnonymousUserID'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"EPFL_AcademicYear\" \"EPFL_IsRepeating\" \n", + " [3] \"EPFL_PropaedeuticGPA\" \"EPFL_CourseGrade\" \n", + " [5] \"EPFL_StudentSection\" \"MOOC_NVideosWatched\" \n", + " [7] \"MOOC_AverageMaxVideosWatched\" \"MOOC_PercentageVideosWatched\" \n", + " [9] \"MOOC_NProblemsSolved\" \"MOOC_AverageMaxProblemsSolved\"\n", + "[11] \"MOOC_PercentageProblemsSolved\" \"MOOC_Grade\" \n", + "[13] \"EPFL_Topic\" \"EPFL_BacGrade\" \n", + "[15] \"EPFL_BacLevel\" \"EPFL_CourseType\" \n", + "[17] \"EPFL_UniqueTopicID_Anonymous\" \"EPFL_AnonymousUserID\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# List the names of the variables\n", + "names(moocs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Descriptive Statistics\n", + "\n", + "## Summary for variables" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " EPFL_AcademicYear EPFL_IsRepeating EPFL_PropaedeuticGPA EPFL_CourseGrade\n", + " 2013-2014:2749 Min. :0.0000 Min. :1.690 Min. :0.000 \n", + " 2014-2015:2694 1st Qu.:0.0000 1st Qu.:3.680 1st Qu.:3.000 \n", + " 2015-2016:3871 Median :0.0000 Median :4.250 Median :4.000 \n", + " Mean :0.1846 Mean :4.133 Mean :3.719 \n", + " 3rd Qu.:0.0000 3rd Qu.:4.630 3rd Qu.:5.000 \n", + " Max. :1.0000 Max. :5.890 Max. :6.000 \n", + " NA's :1833 \n", + " EPFL_StudentSection MOOC_NVideosWatched MOOC_AverageMaxVideosWatched\n", + " SV :2199 Min. : 1.00 Min. :14.00 \n", + " GM :1157 1st Qu.: 7.50 1st Qu.:44.00 \n", + " MT :1128 Median :28.00 Median :45.00 \n", + " MA :1040 Mean :24.46 Mean :48.95 \n", + " GC : 970 3rd Qu.:39.00 3rd Qu.:58.00 \n", + " PH : 649 Max. :85.00 Max. :85.00 \n", + " (Other):2171 NA's :6339 NA's :6339 \n", + " MOOC_PercentageVideosWatched MOOC_NProblemsSolved\n", + " Min. : 0.00 Min. : 1.00 \n", + " 1st Qu.: 20.00 1st Qu.: 3.00 \n", + " Median : 60.00 Median : 7.00 \n", + " Mean : 54.28 Mean : 10.67 \n", + " 3rd Qu.: 80.00 3rd Qu.: 14.00 \n", + " Max. :100.00 Max. :144.00 \n", + " NA's :6339 NA's :7440 \n", + " MOOC_AverageMaxProblemsSolved MOOC_PercentageProblemsSolved MOOC_Grade \n", + " Min. : 17.00 Min. : 0.00 Min. :-Inf \n", + " 1st Qu.: 17.00 1st Qu.: 10.00 1st Qu.:-Inf \n", + " Median : 20.00 Median : 30.00 Median :-Inf \n", + " Mean : 36.44 Mean : 37.27 Mean :-Inf \n", + " 3rd Qu.: 25.00 3rd Qu.: 60.00 3rd Qu.: 0 \n", + " Max. :142.00 Max. :100.00 Max. : 100 \n", + " NA's :7440 NA's :7440 \n", + " EPFL_Topic EPFL_BacGrade EPFL_BacLevel EPFL_CourseType\n", + " CS-111 :1631 Min. :0.1260 HI :4346 CS :2947 \n", + " CS-112 :1316 1st Qu.:0.7500 LO :4249 EE : 797 \n", + " EE-102 : 797 Median :0.8083 NA's: 719 MATH:1404 \n", + " MATH-111:1404 Mean :0.8062 PHYS:4166 \n", + " PHYS-101:4166 3rd Qu.:0.8536 \n", + " Max. :1.0000 \n", + " NA's :719 \n", + " EPFL_UniqueTopicID_Anonymous\n", + " PHYS-101 2014-2015 T63: 336 \n", + " EE-102 2015-2016 T29 : 300 \n", + " MATH-111 2015-2016 T18: 295 \n", + " PHYS-101 2015-2016 T46: 295 \n", + " PHYS-101 2013-2014 T15: 288 \n", + " EE-102 2014-2015 T29 : 281 \n", + " (Other) :7519 \n", + " EPFL_AnonymousUserID solved_problems didMOOC \n", + " e2e988bc1a66ae2f50330fd60a0c2367: 8 NO.PROBLEM :7621 MOOC :3004 \n", + " 16af0f5438b6f7983392ab058a7e4eeb: 7 SOME.PROBLEM:1693 NO.MOOC:6310 \n", + " 29e1b05939960835d785c0aaa20ae388: 7 \n", + " 35ad976f794b6523fe4dd3388b02ff5b: 7 \n", + " 4abf1bcbc3198f4f3c2feba5bca4f6e6: 7 \n", + " 4c46db8f7de7dd9b3521efad9efe0aa9: 7 \n", + " (Other) :9271 \n", + " watched_videos MOOC \n", + " NO.VIDEO :6591 NONE :6555 \n", + " SOME.VIDEO:2723 WATCH:1066 \n", + " DO :1693 \n", + " \n", + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# For each variable, print some information about the distribution (if the variable is quantitative) \n", + "# or about the distinct values taken by the variable (if the variable is categorical). \n", + "\n", + "summary(moocs)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[AUDIT] 7\n", + "\\item[CGC] 513\n", + "\\item[EDBB] 1\n", + "\\item[EL] 129\n", + "\\item[GC] 970\n", + "\\item[GM] 1157\n", + "\\item[IN] 535\n", + "\\item[MA] 1040\n", + "\\item[MT] 1128\n", + "\\item[MX] 168\n", + "\\item[PH] 649\n", + "\\item[SC] 346\n", + "\\item[SIE] 472\n", + "\\item[SV] 2199\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "AUDIT\n", + ": 7CGC\n", + ": 513EDBB\n", + ": 1EL\n", + ": 129GC\n", + ": 970GM\n", + ": 1157IN\n", + ": 535MA\n", + ": 1040MT\n", + ": 1128MX\n", + ": 168PH\n", + ": 649SC\n", + ": 346SIE\n", + ": 472SV\n", + ": 2199\n", + "\n" + ], + "text/plain": [ + "AUDIT CGC EDBB EL GC GM IN MA MT MX PH SC SIE \n", + " 7 513 1 129 970 1157 535 1040 1128 168 649 346 472 \n", + " SV \n", + " 2199 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# To get information about only one variable, use the $ sign to separate the name of \n", + "# the dataframe and the name of the variable like so: $\n", + "\n", + "# For nominal variables we get the count for the different categories \n", + "summary(moocs$EPFL_StudentSection)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n", + " 0.00 20.00 60.00 54.28 80.00 100.00 6339 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# For a quantitative variable we get the ususal central tendency and dispersion indicators\n", + "summary(moocs$MOOC_PercentageVideosWatched)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "54.2823529411765" + ], + "text/latex": [ + "54.2823529411765" + ], + "text/markdown": [ + "54.2823529411765" + ], + "text/plain": [ + "[1] 54.28235" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "34.6713012975899" + ], + "text/latex": [ + "34.6713012975899" + ], + "text/markdown": [ + "34.6713012975899" + ], + "text/plain": [ + "[1] 34.6713" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "60" + ], + "text/latex": [ + "60" + ], + "text/markdown": [ + "60" + ], + "text/plain": [ + "[1] 60" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "100" + ], + "text/latex": [ + "100" + ], + "text/markdown": [ + "100" + ], + "text/plain": [ + "[1] 100" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "0" + ], + "text/latex": [ + "0" + ], + "text/markdown": [ + "0" + ], + "text/plain": [ + "[1] 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We can also compute these separately.\n", + "mean(moocs$MOOC_PercentageVideosWatched, na.rm=T) # na.rm removes missing values\n", + "sd(moocs$MOOC_PercentageVideosWatched, na.rm=T) # standard deviation\n", + "median(moocs$MOOC_PercentageVideosWatched, na.rm=T) # median (50% observations below and 50% observations above)\n", + "max(moocs$MOOC_PercentageVideosWatched, na.rm=T) # maximum\n", + "min(moocs$MOOC_PercentageVideosWatched, na.rm=T) # minimum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " TODO : What is the mean and the standard deviation of the EPFL_CourseGrade ?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", + " 0.000 3.000 4.000 3.719 5.000 6.000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "################\n", + "# Begin solution\n", + "\n", + "summary(moocs$EPFL_CourseGrade)\n", + "\n", + "# End solution\n", + "###############" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining new categorical variables\n", + "It is sometimes useful to transform a quantitative variable into a categorical variable. \n", + "\n", + "For example, below we create a variable that reflects whether students used the MOOC or not based on the number of videos they watched and the number of problems they attempted in the MOOC. In R, categorical variables are created with the function `factor`. The variable `didMOOC` is given the value `NO.MOOC` whenever the values for the number of videos watched and the number of problems attempted is missin. In other cases, the value is `MOOC`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\t
MOOC
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3004
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NO.MOOC
\n", + "\t\t
6310
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\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[MOOC] 3004\n", + "\\item[NO.MOOC] 6310\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "MOOC\n", + ": 3004NO.MOOC\n", + ": 6310\n", + "\n" + ], + "text/plain": [ + " MOOC NO.MOOC \n", + " 3004 6310 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "moocs$didMOOC = factor(\n", + " ifelse(\n", + " is.na(moocs$MOOC_NVideosWatched) & \n", + " is.na(moocs$MOOC_NProblemsSolved), \n", + " \"NO.MOOC\", \n", + " \"MOOC\")\n", + ")\n", + "\n", + "summary(moocs$didMOOC)" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\t
NO.VIDEO
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6591
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SOME.VIDEO
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\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[NO.VIDEO] 6591\n", + "\\item[SOME.VIDEO] 2723\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "NO.VIDEO\n", + ": 6591SOME.VIDEO\n", + ": 2723\n", + "\n" + ], + "text/plain": [ + " NO.VIDEO SOME.VIDEO \n", + " 6591 2723 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We consider that not taking the MOOC is like not watching videos\n", + "moocs$watched_videos = factor(\n", + " ifelse(\n", + " moocs$MOOC_PercentageVideosWatched == 0 |\n", + " is.na(moocs$MOOC_PercentageVideosWatched),\n", + " \"NO.VIDEO\",\n", + " \"SOME.VIDEO\")\n", + " )\n", + "\n", + "summary(moocs$watched_videos)" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\t
NO.PROBLEM
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7621
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SOME.PROBLEM
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1693
\n", + "
\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[NO.PROBLEM] 7621\n", + "\\item[SOME.PROBLEM] 1693\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "NO.PROBLEM\n", + ": 7621SOME.PROBLEM\n", + ": 1693\n", + "\n" + ], + "text/plain": [ + " NO.PROBLEM SOME.PROBLEM \n", + " 7621 1693 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We consider that not taking the MOOC is like not solving any problems\n", + "moocs$solved_problems = factor(\n", + " ifelse(\n", + " moocs$MOOC_PercentageProblemsSolved == 0 |\n", + " is.na(moocs$MOOC_PercentageProblemsSolved),\n", + " \"NO.PROBLEM\",\n", + " \"SOME.PROBLEM\")\n", + " )\n", + "\n", + "summary(moocs$solved_problems)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking activity types\n", + "Do people who solve problems also watch videos ? When using a MOOC, some students only watch videos and don't do exercices. But most of the students who do exercices also watch videos. It appears that almost nobody is *only* solving problems. \n", + "> **Problem**: If we use these variables as two factors to analyse our data set we'll have very uneven groups. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " \n", + " NO.PROBLEM SOME.PROBLEM\n", + " NO.VIDEO 6555 36\n", + " SOME.VIDEO 1066 1657" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# With two arguments, the table command cross-tabulates the number of observations per category.\n", + "table(moocs$watched_videos, moocs$solved_problems)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " TODO : To fix this problem we qualify MOOC usage with three categories: \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* `NONE` corresponds to no use of MOOCs, \n", + "* `WATCH` corresponds to students mainly watching videos and \n", + "* `DO` corresponds to students doing some exercices." + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\t
DO
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NONE
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\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[DO] 1693\n", + "\\item[NONE] 6555\n", + "\\item[WATCH] 1066\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "DO\n", + ": 1693NONE\n", + ": 6555WATCH\n", + ": 1066\n", + "\n" + ], + "text/plain": [ + " DO NONE WATCH \n", + " 1693 6555 1066 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "#################\n", + "# Begin Solution:\n", + "moocs$MOOC <- factor(\n", + " ifelse(moocs$watched_videos == \"NO.VIDEO\" & moocs$solved_problems == \"NO.PROBLEM\",\n", + " \"NONE\",\n", + " ifelse(moocs$solved_problems == \"SOME.PROBLEM\",\n", + " \"DO\",\n", + " \"WATCH\")\n", + " )\n", + ")\n", + "# End Solution \n", + "###############\n", + "\n", + "summary(moocs$MOOC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The order of the categories is a bit 'unlogical'. We'd like to have them appear in outputs and graphs sorted by increasing level of involvement: NONE, WATCH and DO. Let's fix this problem by reordering the levels of the factor." + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " NONE WATCH DO \n", + " 6555 1066 1693 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "library(gdata)\n", + "# put the levels into a convenient order (for graphing, etc.)\n", + "moocs$MOOC <- reorder(moocs$MOOC, new.order=c(\"NONE\", \"WATCH\", \"DO\"))\n", + "table(moocs$MOOC)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Do people who use the MOOC get better grades ?\n", + "\n", + "## Looking at the means.\n", + "Let's start by looking at the mean grade for the three groups of our variable `MOOC`. The `ddply` function allows to apply a function to a subset of the data defined by a categorical variable (`MOOC` in our case). The ddply returns a new data frame with the category and the result." + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 3 × 2
MOOCgrp.mean
<fct><dbl>
NONE 3.513959
WATCH3.828799
DO 4.445363
\n" + ], + "text/latex": [ + "A data.frame: 3 × 2\n", + "\\begin{tabular}{r|ll}\n", + " MOOC & grp.mean\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t NONE & 3.513959\\\\\n", + "\t WATCH & 3.828799\\\\\n", + "\t DO & 4.445363\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 3 × 2\n", + "\n", + "| MOOC <fct> | grp.mean <dbl> |\n", + "|---|---|\n", + "| NONE | 3.513959 |\n", + "| WATCH | 3.828799 |\n", + "| DO | 4.445363 |\n", + "\n" + ], + "text/plain": [ + " MOOC grp.mean\n", + "1 NONE 3.513959\n", + "2 WATCH 3.828799\n", + "3 DO 4.445363" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "mu <- ddply(moocs, \"MOOC\", summarise, grp.mean=mean(EPFL_CourseGrade))\n", + "mu\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Looking at the variance ?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 3 × 3
MOOCgrp.sdgrp.mean
<fct><dbl><dbl>
DO 1.1970954.445363
NONE 1.4909693.513959
WATCH1.3258023.828799
\n" + ], + "text/latex": [ + "A data.frame: 3 × 3\n", + "\\begin{tabular}{r|lll}\n", + " MOOC & grp.sd & grp.mean\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t DO & 1.197095 & 4.445363\\\\\n", + "\t NONE & 1.490969 & 3.513959\\\\\n", + "\t WATCH & 1.325802 & 3.828799\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 3 × 3\n", + "\n", + "| MOOC <fct> | grp.sd <dbl> | grp.mean <dbl> |\n", + "|---|---|---|\n", + "| DO | 1.197095 | 4.445363 |\n", + "| NONE | 1.490969 | 3.513959 |\n", + "| WATCH | 1.325802 | 3.828799 |\n", + "\n" + ], + "text/plain": [ + " MOOC grp.sd grp.mean\n", + "1 DO 1.197095 4.445363\n", + "2 NONE 1.490969 3.513959\n", + "3 WATCH 1.325802 3.828799" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "sigma <- ddply(moocs, \"MOOC\", summarise, grp.sd=sd(EPFL_CourseGrade))\n", + "#head(sigma)\n", + "\n", + "info <- merge(sigma, mu)\n", + "info\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "To further investigate the difference between the ghree groups, let's plot the grade of the students `EPFL_CourseGrade` against the categorical variable `MOOC` that we just defined. We'll use three different plots: \n", + "\n", + "* boxplot, \n", + "* histogram and \n", + "* mean plots.\n", + "\n", + "### Boxplots\n", + "\n", + "Boxplots allow to visualise the distribution of the variable. The central line in the box shows the median and the edges of the boxes show the interquartiles. A simple tutorial for variations of this type of plot is available here: http://www.sthda.com/english/wiki/ggplot2-box-plot-quick-start-guide-r-software-and-data-visualization.\n", + "\n", + "From the plot below, it seems that the distribution of grades for people who did not use the MOOC (`NONE`) tends to be lower in the grade scale than the grades of people who `WATCH` videos or people who `DO` assignments. From the shape of the boxesm we also see that the distributions for `NONE` and `WATCH` are more symmetric than for `DO`." + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "Plot with title “Grade by Activity Level”" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Setting the size of plots generated with ggplot below. \n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "\n", + "# Quick and simple boxplot\n", + "boxplot(moocs$EPFL_CourseGrade ~ moocs$MOOC, main=\"Grade by Activity Level\", xlab=\"Activity Level\", ylab=\"Grade\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HkHDhx455136ow7HI533303MzPTnZ4VTz31lCRJK1asaMxJmpKnFWiF0WicNm3a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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Setting the size of plots generated with ggplot below. \n", + "options(repr.plot.width = 8, repr.plot.height = 5)\n", + "\n", + "# More sophisticated plots with GGPLOT.\n", + "# Tutorial: http://www.sthda.com/english/wiki/ggplot2-box-plot-quick-start-guide-r-software-and-data-visualization\n", + "ggplot(moocs, aes(x=MOOC, y=EPFL_CourseGrade, fill=MOOC)) +\n", + " geom_boxplot(notch=TRUE) + coord_flip() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Histograms\n", + "\n", + "Histograms give a more precise representation of the actual grade distribution. Below, we define bins of 0.5 to represent the frequency of the different grades. The vertical dashed lines represent the means of the distributions. Notice that we reuse the dataframe `mu` that we defined above. We see indeed that the `DO` group is less symmetric than the others." + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vV6ze4l8xQWFtbV1ZndReax2+25ubnNzc0+n8/sXrqCVrkuibUCEyfFnZ7yUZdE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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Tutorial about generating such a figure\n", + "# http://www.sthda.com/english/wiki/ggplot2-histogram-plot-quick-start-guide-r-software-and-data-visualization\n", + "options(repr.plot.width = 8, repr.plot.height = 5)\n", + "\n", + "ggplot(moocs, aes(x=EPFL_CourseGrade, fill=MOOC)) +\n", + " geom_histogram(binwidth=.5, alpha=.5, position=\"dodge\") + \n", + " geom_vline(data=mu, aes(xintercept=grp.mean, color=MOOC), linetype=\"dashed\") \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean plots\n", + "Mean plots represent the mean along with the confidence interval for the mean. There is a 95% chance that the confidence interval contains the true mean of `EPFL_CourseGrade` for the population. " + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HPCrFmzeFMl3717FxkZuWDBAmVl5Q0b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\n3/3ud9a78Tz/7rvv/vnPf+Y4bvjw4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+ "text/plain": [ + "Plot with title “Grade given MOOC activity”" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "\n", + "# plotmeans is defined in the gplots library \n", + "# make sure to load the library by executing > library(gplots) before. \n", + "plotmeans(moocs$EPFL_CourseGrade ~ moocs$MOOC, main=\"Grade given MOOC activity\", ylab=\"Grade\", xlab=\"Activity Level\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "\n", + "# Alternate form using the package ggpubr\n", + "# See examples here: http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/79-plot-meansmedians-and-error-bars/\n", + "\n", + "ggline(moocs, x = \"MOOC\", y = \"EPFL_CourseGrade\", \n", + " add = c(\"mean_ci\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ANOVA - Analysis of Variance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: ANOVA a.k.a. ANalysis Of VAriance \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ANOVA allows comparing whether one variable has the same mean in different groups ? The formula to specify the test is done in tow steps. First we define a linear model with `m <- lm(quantitative ~  categorical)` and then we do the Anova of the model `Anova(m)`.\n", + "\n", + "* H0: mean_1 = mean_2 = ...= Mean_n\n", + "* H1: mean_1 != mean_2 != ... != mean_n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the Analysis of Variance (ANOVA) to compare the mean of a variable across several modalities of a categorical variable. In the example below, we compare the course grade (`EPFL_CourseGrade`) across different modalities of MOOC usage (the `MOOC` variable takes three possible values: `NONE`, `WATCH` and `DO`). " + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A anova: 2 × 4
Sum SqDfF valuePr(>F)
<dbl><dbl><dbl><dbl>
moocs$MOOC 1181.668 2291.59361.479869e-123
Residuals18866.1759311 NA NA
\n" + ], + "text/latex": [ + "A anova: 2 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & Sum Sq & Df & F value & Pr(>F)\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\tmoocs\\$MOOC & 1181.668 & 2 & 291.5936 & 1.479869e-123\\\\\n", + "\tResiduals & 18866.175 & 9311 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A anova: 2 × 4\n", + "\n", + "| | Sum Sq <dbl> | Df <dbl> | F value <dbl> | Pr(>F) <dbl> |\n", + "|---|---|---|---|---|\n", + "| moocs$MOOC | 1181.668 | 2 | 291.5936 | 1.479869e-123 |\n", + "| Residuals | 18866.175 | 9311 | NA | NA |\n", + "\n" + ], + "text/plain": [ + " Sum Sq Df F value Pr(>F) \n", + "moocs$MOOC 1181.668 2 291.5936 1.479869e-123\n", + "Residuals 18866.175 9311 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# needs library(car)\n", + "\n", + "m <- lm(moocs$EPFL_CourseGrade ~ moocs$MOOC) \n", + "Anova(m)\n", + "\n", + "r <- residuals(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This is the way you report the ANOVA in an article.\n", + "\n", + "> We conducted a one-way ANOVA to test whether the course grades differ for different categories of MOOC usage. There is a significant difference of course grades across different types of MOOC use (F[2,9311]=291.6, p<.001). \n", + "\n", + "Beware, that since we did not randomly assign subjects to experimental groups, but rather students chose whether they use the MOOC or not, we can't conclude about any causal effect. It is possible that better students are inclided to use MOOCs more than weaker students. Hence, the \"effect of\" the MOOC would be due to this hidden variable which actually explains the difference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Advanced topic : There are different ways to run anovas in R. They differ with regards to the type of Sum of Square that they report. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following http://goanna.cs.rmit.edu.au/~fscholer/anova.php the Type III Sum of Square is more adapted to interpret the contributions of the differnt variables. R and anova report Type I sums of square.\n", + "\n", + "Here is an explanation about how to obtain Type III sums of squares: https://www.r-bloggers.com/anova-%E2%80%93-type-iiiiii-ss-explained/\n", + "And some explanations here: prometheus.scp.rochester.edu/zlab/sites/default/files/InteractionsAndTypesOfSS.pdf\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Which groups differ among each other ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Pair-wise t-test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A pairwise test allows to know which groups differ from each other. Because of repeated testing, we have to adjust the p-value by dividing the .05 value by the number of tests. In our case, the tests show that all groups differ from each other when taken 2" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tPairwise comparisons using t tests with pooled SD \n", + "\n", + "data: moocs$EPFL_CourseGrade and moocs$MOOC \n", + "\n", + " NONE WATCH \n", + "WATCH 6.7e-11 - \n", + "DO < 2e-16 < 2e-16\n", + "\n", + "P value adjustment method: bonferroni " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Checks which group is different from what other group in doing 2 by 2 tests.\n", + "pairwise.t.test(moocs$EPFL_CourseGrade, moocs$MOOC, p.adj = \"bonf\") \n" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Means" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here is the wording for the article. \n", + "\n", + "> A series of pair-wise t-tests with bonferonni adjusment for repeated testing, shows that the average grade for students who did not use the MOOC (m=3.5, sd=2.5) is smaller than the average grade of students who watched videos (m=3.8, sd=1.3) and smaller than the average grade of students who did the assignments (m=4.4, sd=1.2)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assumptions\n", + "Is it ok to run an ANOVA blindly as we did just before ? \n", + "\n", + "Not really, we should have checked the assumptions for ANOVA first.\n", + "\n", + "* Normality of the residuals\n", + "* Homoscedasticity of the residuals = the error distribution should be the same regardless of the category.\n", + "\n", + "### Visually inspecting normality Assumption\n", + "Testing normality visually with quantile plots. If the variable is normally distributed, the observations would follow the line drawn by qqline. We see from the plots below that the distribution of the residuals of our model used for the Anova departs very strongly from a normal distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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k/IaX8YTMdZEgYSHGRdHrXokrDfi+ZkDglZMbLJK5V072PBqw2m4yQJDyUo6IJ7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\nCRcaLab7gYQikDCR+3PG7KMmH47ua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+ "text/plain": [ + "Plot with title “Residuals”" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# split the plotting area into 2 lines and 2 columns\n", + "par(mfrow=c(2,2))\n", + "\n", + "# Setting the size of plots below. \n", + "options(repr.plot.width = 7.5, repr.plot.height = 7.5)\n", + "\n", + "v <- rnorm(1000)\n", + "hist(v, main=\"Normal variable\")\n", + "qqnorm(v, main=\"Normal variable\");\n", + "qqline(v);\n", + "\n", + "# The residuals of the model we used to run the Anova\n", + "# r <- residuals(m)\n", + "hist(r, main=\"Residuals\")\n", + "qqnorm(r, main=\"Residuals\");\n", + "qqline(r)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normality tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Shapiro-Wilks Test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Normality can also be tested statistically, however the normality tests are rather sensitive to small deviations, especially when sample sizes are large. For example, the Shapiro-Wilks test, is testing the null hypothesis that the variable is normally distributed. In this case the adequate p.value is .1. Hence, with p <<.1 we have to reject the null hypothesis and conclude that the variable is not normally distributed. This test work with samples smaller than 5000 observations. \n", + "\n", + "* H0: variable is normally distributed\n", + "* H1: variable is not normally distributed\n", + "\n", + "We see below that for our variable `v` that we generated to be normally distributed, p = 0.47, far greater than 0.1, hence we cannot reject H0.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tShapiro-Wilk normality test\n", + "\n", + "data: v\n", + "W = 0.99866, p-value = 0.6612\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "shapiro.test(v)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "However, for the residuals of our Anova, we have to reject H0, and conclude that they are not normally distributed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tShapiro-Wilk normality test\n", + "\n", + "data: sample(r, 1500)\n", + "W = 0.95659, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# taking a sample of 1500 residuals for illustrating the output of the shapiro test\n", + "shapiro.test(sample(r,1500)) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing Homoscedasticity assumption\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Bartlett test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* H0: variances are the same\n", + "* H1: variances are different\n", + "\n", + "We see below, that the p-value is much smaller than .05 and therefore we reject H0. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBartlett test of homogeneity of variances\n", + "\n", + "data: r by moocs$MOOC\n", + "Bartlett's K-squared = 130, df = 2, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "bartlett.test(r ~ moocs$MOOC)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "The difference in variuance is also appararent in a boxplot, where we see that the variance for the DO group is smaller. We also see that the residuals for WATCH tend to be slightly more positive than negative (their median is above 0) " + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "boxplot(r ~ moocs$MOOC)\n", + "abline(h=0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If variances are unequal or residuals are not normally distributed\n", + "\n", + "If variances are unequal we can either \n", + "\n", + "* adapt the oneway test by stating that `var.equal = F` \n", + "* or use a non parametric alternative to ANOVA like the Kruskal Wallis test. " + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tOne-way analysis of means (not assuming equal variances)\n", + "\n", + "data: moocs$EPFL_CourseGrade and moocs$MOOC\n", + "F = 366.38, num df = 2.0, denom df = 2495.4, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# adapt the caluclations to take unequal variances into account \n", + "oneway.test(moocs$EPFL_CourseGrade ~ moocs$MOOC, var.equal = F)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Test: Kruskal test\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tests the null hypothesis that the location parameters are the same in each of the groups. This test does not make assumptions about the distribution of the residuals, nor about the variances.\n", + "\n", + "* H0: the mean ranks of observations are the same in all groups\n", + "* H1: the mean ranks of observations are different." + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tKruskal-Wallis rank sum test\n", + "\n", + "data: moocs$EPFL_CourseGrade by moocs$MOOC\n", + "Kruskal-Wallis chi-squared = 608.7, df = 2, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "kruskal.test(moocs$EPFL_CourseGrade ~ moocs$MOOC)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Because the assumption of equal variances was not respected (K2[2]=130, p< .001) in our sample, we conducted a non-parametric Kruskal-Wallis test that showed that the mean rank of course grades differs significantly across groups (X2[2]=608.7, p < .001). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise : analyse the effect of prior knowlege on the grades.\n", + "Students' baccalaureat grade is given by the variable `EPFL_BacGrade`. In the plot below we see the distribution of the grades at the baccalaureat and display the median and quartiles as vertical bars.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 347, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ADWIXAAAAAA6xA4AAAAgHUIHAAAAMA6BA4AAABgHQIHAAAAsA6BAwAAAFiHwAEA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+ "text/plain": [ + "Plot with title “Histogram of moocs.bac$EPFL_BacGrade”" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 6, repr.plot.height = 4)\n", + "\n", + "# Remove observations for which we do not have a EPFL_BacGrade\n", + "moocs.bac <- subset(moocs, ! is.na(moocs$EPFL_BacGrade))\n", + "\n", + "# Draw a histogram with 50 bins\n", + "hist(moocs.bac$EPFL_BacGrade, breaks=50)\n", + "\n", + "# Draw a vertical line at the median\n", + "abline(v=median(moocs.bac$EPFL_BacGrade, na.rm=T), col=\"red\", lwd=3) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task 1 : Define two categorical variables named prior and prior4 that distinguish students given their baccalaureat grade. \n", + "
    \n", + "
  • prior : The values of the variable are HI for strong students and LO for weakers students. use median() to define high and low levels of ability
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " LO HI \n", + "4318 4277 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + " XLO LO HI XHI \n", + "2285 2032 2128 2149 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: Define two categorical variables named moocs$prior and moocs$prior4 that distinguishstudents given at their baccalaureat grade.\n", + "\n", + "# 1) moocs$prior. \n", + "# The values of the variable are \"HI\"\" for strong students and \"LO\"\" for weakers students.\n", + "# => use median() to define high and low levels of ability\n", + "\n", + "################\n", + "# Begin solution\n", + "moocs.bac$prior <- factor(\n", + " ifelse(moocs.bac$EPFL_BacGrade > median(moocs.bac$EPFL_BacGrade, na.rm=T),\n", + " \"HI\",\n", + " \"LO\"\n", + " )\n", + ")\n", + "\n", + "# reorder the levels of prior so that low comes before hi in the graphs\n", + "moocs.bac$prior <- reorder(moocs.bac$prior, new.order=c(\"LO\", \"HI\"))\n", + "\n", + "\n", + "# end solution\n", + "##############\n", + "\n", + "\n", + "# count the number of observations in LO and HI \n", + "table(moocs.bac$prior)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task 2 : Plot the means for the EPFL_CourseGrade given the prior grade level. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mvixImZmZm8CwcA0I6jo6OampqrqyvV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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "################\n", + "# Begin Solution\n", + "\n", + "options(repr.plot.width = 5, repr.plot.height = 5)\n", + "plotmeans(moocs.bac$EPFL_CourseGrade ~ moocs.bac$prior)\n", + "\n", + "# end Solution\n", + "################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Task 3 : Compare the means of EPFL_CourseGrade. \n", + "
    \n", + "
  • Check assumptions to run an ANOVA
    • \n", + "
  • Run an ANOVA or a replacement if the assumptions are not set
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 346, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tShapiro-Wilk normality test\n", + "\n", + "data: sample(r2, 2000)\n", + "W = 0.95704, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBartlett test of homogeneity of variances\n", + "\n", + "data: r2 by moocs.bac$prior\n", + "Bartlett's K-squared = 114.6, df = 1, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tKruskal-Wallis rank sum test\n", + "\n", + "data: moocs.bac$EPFL_CourseGrade by moocs.bac$prior\n", + "Kruskal-Wallis chi-squared = 867.09, df = 1, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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IWmn2eOZwqtkPye1ZujNyAdh3+EPlF1Bm4VRGEpNP088ztsZwnT96wm0LfrIA9w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\nvdC7e1XOuPpD5enVd+QFq/f+QS6/0Ton44qn/lQ22t6jqm+NZv1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+ "text/plain": [ + "Plot with title “Normal Q-Q Plot”" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "################\n", + "# Begin Solution\n", + "\n", + "# Build model\n", + "m2 <- lm(moocs.bac$EPFL_CourseGrade ~ moocs.bac$prior)\n", + "r2 <- residuals(m2)\n", + "\n", + "# Check normality of residuals\n", + "shapiro.test(sample(r2, 2000))\n", + "\n", + "# Check homoscedasticity \n", + "bartlett.test(r2 ~ moocs.bac$prior)\n", + "\n", + "# Plots\n", + "options(repr.plot.width = 6, repr.plot.height = 4)\n", + "par(mfrow=c(1,2))\n", + "qqnorm(r2)\n", + "qqline(r2)\n", + "\n", + "boxplot(r2 ~ moocs.bac$prior)\n", + "\n", + "# The variances appear to be different across the levels of prior experience\n", + "# => Kruskal test instead\n", + "\n", + "kruskal.test(moocs.bac$EPFL_CourseGrade ~ moocs.bac$prior)\n", + "\n", + "# end Solution\n", + "################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two factor ANOVA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can add more than one categorical predictor and also model the interaction of two factors. When there is an interaction, the effect of one variable is conditional on the effect of another variable \n", + "\n", + "Interaction between two terms is represented by a `:`. A syntaxic equivalent for the model that contains single and interaction effects is `A * B == A + B + A:B`" + ] + }, + { + "cell_type": "code", + "execution_count": 363, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anova Table (Type III tests)\n", + "\n", + "Response: moocs.bac$EPFL_CourseGrade\n", + " Sum Sq Df F value Pr(>F) \n", + "(Intercept) 31721 1 17373.5762 < 2.2e-16 ***\n", + "moocs.bac$MOOC 374 2 102.4039 < 2.2e-16 ***\n", + "moocs.bac$prior 1179 1 645.7809 < 2.2e-16 ***\n", + "moocs.bac$MOOC:moocs.bac$prior 20 2 5.3793 0.004626 ** \n", + "Residuals 15682 8589 \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" + ] + } + ], + "source": [ + "\n", + "m3 <- lm(moocs.bac$EPFL_CourseGrade ~ moocs.bac$MOOC + moocs.bac$prior + moocs.bac$MOOC:moocs.bac$prior)\n", + "print(Anova(m3, type=3))\n", + "\n", + "r3 <- residuals(m3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> We conducted a 3 x 2 factorial ANOVA with prior performance and MOOC useage as factors. The results indicate a main effect of MOOC usage (F[2,374]=102.40, p<.001), as well as a main effect of prior knowledge (F[1,1179]=645.78, p<.001). There is also an interaction effect between the two factors (F[20,2]=5.3, p<.01). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "The ggline package comes in handy to plot interactions between variables. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Reporting\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> In our case here, we see that the relation between the different types of MOOC activity is not the same from strong or weaker students. It seems that for strong students, watching videos does not increase the score compared to doing nothing. However, doing exercices is beneficial for both strong and weak students. " + ] + }, + { + "cell_type": "code", + "execution_count": 360, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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VM+XKVrGyp0fUkWul7g1MHaRUEDg8AB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\n", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ggline(moocs.bac, x = \"MOOC\", y = \"EPFL_CourseGrade\", \n", + " add = c(\"mean_ci\"),\n", + " color = \"prior\", palette = \"jco\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing Assumptions" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBartlett test of homogeneity of variances\n", + "\n", + "data: r3 by moocs.bac$MOOC\n", + "Bartlett's K-squared = 116.22, df = 2, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBartlett test of homogeneity of variances\n", + "\n", + "data: r3 by moocs.bac$prior\n", + "Bartlett's K-squared = 131.29, df = 1, p-value < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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gIqIzD/SiwG1BdYyphYeVuNV5uFXk3jhurDjjRrN7REV3AEUSkBlRGYAJXiZyOJ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\nAElFTkSuQmCC", + "text/plain": [ + "Plot with title “Normal Q-Q Plot”" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 8, repr.plot.height = 4)\n", + "par(mfrow=c(1,3))\n", + "qqnorm(r3)\n", + "qqline(r3)\n", + "\n", + "boxplot(r3 ~ moocs.bac$MOOC)\n", + "boxplot(r3 ~ moocs.bac$prior)\n", + "\n", + "bartlett.test(r3 ~ moocs.bac$MOOC)\n", + "bartlett.test(r3 ~ moocs.bac$prior)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 374, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tm8eXNkZKRVH0ZAQMBbb721YMGCZ599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RTPqbShiCqH3X+PHjdX44ADOHBPo/pFJp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\nzMxM8Z6+eA+C0OoBFH8nT56kHPeyrl+/XqpUq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XJkdlixjfPx8WE9K1gOQHarV6+WO4QCQo0K4zhFhLli2bOnp6dYg87aGY1Gg6+/hXDO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\n2okTJ7gOCnQBE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+ "text/plain": [ + "Plot with title “”" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 8, repr.plot.height = 8)\n", + "par(mfrow=c(2,2))\n", + "plot(m3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choosing a non parametric version of the 2-way ANOVA" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "metadata": {}, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in friedman.test.default(moocs.bac$EPFL_CourseGrade, moocs.bac$MOOC, : not an unreplicated complete block design\n", + "output_type": "error", + "traceback": [ + "Error in friedman.test.default(moocs.bac$EPFL_CourseGrade, moocs.bac$MOOC, : not an unreplicated complete block design\nTraceback:\n", + "1. friedman.test(moocs.bac$EPFL_CourseGrade, moocs.bac$MOOC, moocs.bac$prior)", + "2. friedman.test.default(moocs.bac$EPFL_CourseGrade, moocs.bac$MOOC, \n . moocs.bac$prior)", + "3. stop(\"not an unreplicated complete block design\")" + ] + } + ], + "source": [ + "friedman.test(moocs.bac$EPFL_CourseGrade, moocs.bac$MOOC, moocs.bac$prior)" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " \n", + " LO HI\n", + " NONE 3235 2798\n", + " WATCH 477 509\n", + " DO 606 970" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "table( moocs.bac$MOOC, moocs.bac$prior)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/moocs.basic.csv b/moocs.basic.csv new file mode 100644 index 0000000..970597b 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2015-2016 T21","257eb4e6f14251adeb3abb959cc58b70" +"2015-2016","0",3.69,3,"GC",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.701666666666667,"LO","MATH","MATH-111 2015-2016 T21","5e0cb78612ade0f753518753bd9a7f52" +"2015-2016","0",5.02,4.5,"GC",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.898333333333333,"HI","MATH","MATH-111 2015-2016 T21","5a37cde6668ddcb218243d969cfd25c5" +"2015-2016","0",4.56,4,"SIE",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.731666666666667,"LO","MATH","MATH-111 2015-2016 T21","019afdcd59528fb1a0a9f8cb7afffcb2" +"2015-2016","0",3.18,3,"GC",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8,"LO","MATH","MATH-111 2015-2016 T21","927d0a7b109eafea933812f1a434c419" +"2015-2016","0",NA,2.5,"SIE",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.815,"HI","MATH","MATH-111 2015-2016 T21","07ed72eff6e129737ae83bba3f5d96f5" +"2015-2016","0",NA,3,"SIE",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.783333333333333,"LO","MATH","MATH-111 2015-2016 T21","477509d7fa4d6e25cb92708ea73dd5ec" 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T36","c24c971cefdcede90058fd550322bb5e" +"2015-2016","0",4.79,5.5,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8,"LO","MATH","MATH-111 2015-2016 T36","2f89aba68ea2283ded6503f52f5fc478" +"2015-2016","0",4.91,5.5,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.816666666666667,"HI","MATH","MATH-111 2015-2016 T36","b75e93e2356e527589cc0ab4c0a6d4a2" +"2015-2016","1",4.69,5,"SV",10,44,30,91,142,80,57,"MATH-111",0.8165,"HI","MATH","MATH-111 2015-2016 T36","caca1ccdfac0f4110fd0b831cd577c97" +"2015-2016","1",4.27,4.5,"SV",8,40,20,NA,NA,NA,0,"MATH-111",0.83,"HI","MATH","MATH-111 2015-2016 T36","2cc1de1590c69039060acc8b523d37e4" +"2015-2016","1",5.07,5,"SV",23,44,60,142,142,100,49,"MATH-111",0.926,"HI","MATH","MATH-111 2015-2016 T36","194ada38834009c9971257ffde0dbb65" +"2015-2016","1",NA,4.5,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.916666666666667,"HI","MATH","MATH-111 2015-2016 T36","7a619cd6e87695654c107af988404b4a" +"2015-2016","1",4.35,4.5,"SV",3,40,10,NA,NA,NA,0,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T36","f19a7d7776e68a6aa312850a2c0493dc" +"2015-2016","1",4.75,5.5,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",NA,NA,"MATH","MATH-111 2015-2016 T36","4c46db8f7de7dd9b3521efad9efe0aa9" +"2015-2016","0",4.39,4,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T36","098e2a45af1c598989c63963d2a931f3" +"2015-2016","0",3.95,3,"SV",5,40,20,4,142,0,0,"MATH-111",0.8435,"HI","MATH","MATH-111 2015-2016 T36","1e0123ba6eefe223eb92edc4809b34e2" +"2015-2016","0",3.48,2,"SV",5,44,10,8,142,10,0,"MATH-111",0.875,"HI","MATH","MATH-111 2015-2016 T36","e0bf2948661cf14a2c42a90ac98f847c" +"2015-2016","1",4.6,4,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8365,"HI","MATH","MATH-111 2015-2016 T36","6b6c87ed20d65fee2bcdbd861e27334a" +"2015-2016","1",4.57,4.5,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.898333333333333,"HI","MATH","MATH-111 2015-2016 T36","ebddb18d3503ba4dbaf8ed14ee119bda" +"2015-2016","1",4.96,6,"SV",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.9035,"HI","MATH","MATH-111 2015-2016 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T18","ea22419f70fa84e6d2433dc66831786f" +"2015-2016","1",4.49,4.5,"GM",1,40,0,NA,NA,NA,0,"MATH-111",0.826666666666667,"HI","MATH","MATH-111 2015-2016 T18","1a0c30f9d2bd53ac8bd54cd177e3e677" +"2015-2016","1",4.07,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.733333333333333,"LO","MATH","MATH-111 2015-2016 T18","1d6b907594b91e171e673ca3531ca52a" +"2015-2016","0",4.21,4.5,"GM",2,40,10,NA,NA,NA,0,"MATH-111",0.888888888888889,"HI","MATH","MATH-111 2015-2016 T18","0b66e780ec229e282858f853c6bb49a2" +"2015-2016","0",4.66,5,"EL",46,44,100,75,142,70,0,"MATH-111",0.8415,"HI","MATH","MATH-111 2015-2016 T18","0592b2e3d91f95131d4ada1ced26ba68" +"2015-2016","0",4.42,4,"GM",1,40,0,13,142,10,9,"MATH-111",0.866666666666667,"HI","MATH","MATH-111 2015-2016 T18","7814685d063303801eafe8d996c40167" +"2015-2016","0",4.22,4.5,"GM",1,40,0,NA,NA,NA,0,"MATH-111",0.8235,"HI","MATH","MATH-111 2015-2016 T18","ac908b3bf08cb6b9b4e51700ada088f4" 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T18","a5c01c6bffba5edd42aa844db83d2d7a" +"2015-2016","1",4.71,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8735,"HI","MATH","MATH-111 2015-2016 T18","e21ccb3c47d100b70eaaa65205bef7be" +"2015-2016","0",NA,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8375,"HI","MATH","MATH-111 2015-2016 T18","36ea3e874de4c49edd892182fb2ee5be" +"2015-2016","1",4.87,5.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.916666666666667,"HI","MATH","MATH-111 2015-2016 T18","7669d554124c9beb55f07bd9ca2228c6" +"2015-2016","0",4.36,5.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.831,"HI","MATH","MATH-111 2015-2016 T18","a325b6da5e53f41a8f19791f93f95519" +"2015-2016","1",3.82,3.5,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.8025,"LO","MATH","MATH-111 2015-2016 T18","9b1327121a3d2c25770b12c4de38033e" +"2015-2016","1",4.6,5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","8410093a051434284efe2bf937dd668e" +"2015-2016","0",4.38,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.785,"LO","MATH","MATH-111 2015-2016 T18","3323736b0add2a839cf01802d559c3fa" +"2015-2016","1",4.3,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",NA,NA,"MATH","MATH-111 2015-2016 T18","1c25e1b01c1bb0b881437424f8b8e7e9" +"2015-2016","1",4.7,5.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","a11e5ec3f581949b3c52ee2679fb4175" +"2015-2016","1",4.57,4,"GM",1,40,0,NA,NA,NA,0,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","85beb91a2c8b622d394e35af02358482" +"2015-2016","1",4.38,5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8,"LO","MATH","MATH-111 2015-2016 T18","c47319477d256f08599c787a774d072f" +"2015-2016","1",4.28,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.7,"LO","MATH","MATH-111 2015-2016 T18","caf8d5b6ff013d8266ec4f5ff3ea6dfc" +"2015-2016","1",4,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8655,"HI","MATH","MATH-111 2015-2016 T18","c90b1fefc7e5a0e510b352cd66203901" +"2015-2016","1",4.05,3.5,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.831,"HI","MATH","MATH-111 2015-2016 T18","06ed437fcbd5a408ab08e783e06a315a" +"2015-2016","1",4.44,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.78,"LO","MATH","MATH-111 2015-2016 T18","f9ff44a81a25f8fdc509cd0559aad323" +"2015-2016","1",3.88,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.846,"HI","MATH","MATH-111 2015-2016 T18","6d8179183a40067ecef878d9dc89c99d" +"2015-2016","1",4.54,5,"GM",1,40,0,47,142,30,0,"MATH-111",0.8155,"HI","MATH","MATH-111 2015-2016 T18","7ececd8742272aae330fda3968944074" +"2015-2016","1",3.87,3,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.809,"HI","MATH","MATH-111 2015-2016 T18","32b49a2da55e0e85362f395a09c1f9c7" +"2015-2016","0",4.25,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.833333333333333,"HI","MATH","MATH-111 2015-2016 T18","651bb718fd097e1e889f76590c1bd714" +"2015-2016","1",4.35,5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.708333333333333,"LO","MATH","MATH-111 2015-2016 T18","eaee17d80f4fb8a7c7c467b26356e2eb" 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2015-2016 T18","23cf04cdf015a24368c6ed7f2c2112ba" +"2015-2016","0",4.25,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.766666666666667,"LO","MATH","MATH-111 2015-2016 T18","b77940c716174075f41c0d6ba775ce1c" +"2015-2016","0",NA,2.5,"GM",1,40,0,NA,NA,NA,-Inf,"MATH-111",0.85,"HI","MATH","MATH-111 2015-2016 T18","94e615142b0ffb36c52042f1df9cafed" +"2015-2016","0",4.47,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.833333333333333,"HI","MATH","MATH-111 2015-2016 T18","36f9d9444b937a97b3ae34407fdfedbb" +"2015-2016","0",4.22,4,"GM",1,40,0,NA,NA,NA,0,"MATH-111",0.826666666666667,"HI","MATH","MATH-111 2015-2016 T18","d9eeb0335067c4589e2ac93ec7b53c0f" +"2015-2016","0",2.27,2,"GM",1,40,0,NA,NA,NA,-Inf,"MATH-111",0.702380952380952,"LO","MATH","MATH-111 2015-2016 T18","1b2fa3421ea3a1d61d4e514524be2373" +"2015-2016","0",4.56,5,"GM",40,40,100,NA,NA,NA,0,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","b7752d4a0ca2f1afc5f3350e6b22ed9a" 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T18","e31bd45d89965460c9239fe6c11b27ea" +"2015-2016","0",3.51,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.851,"HI","MATH","MATH-111 2015-2016 T18","d9d7c631ce139c4a83645f0efb6faeb9" +"2015-2016","0",4.28,4.5,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.8,"LO","MATH","MATH-111 2015-2016 T18","cfbb0cfd8deaa04adf3de5a320d6c6a6" +"2015-2016","0",3.6,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.826666666666667,"HI","MATH","MATH-111 2015-2016 T18","0eef21665f3655950c8d9836b09d02df" +"2015-2016","0",3.08,2.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","0be1efacd94edefd2a19af6375dd70fe" +"2015-2016","0",4.06,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.803,"LO","MATH","MATH-111 2015-2016 T18","9b984c36959b23613ec9ccdce5e02939" +"2015-2016","0",4.66,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.9525,"HI","MATH","MATH-111 2015-2016 T18","5ac06bc44a1fe4c8b20cb94ed07dd871" +"2015-2016","0",4.69,4.5,"GM",12,44,40,1,142,0,0,"MATH-111",0.801,"LO","MATH","MATH-111 2015-2016 T18","14637f9d034f18f6f7227fae2250e0d7" +"2015-2016","0",4.51,4.5,"GM",2,40,10,NA,NA,NA,0,"MATH-111",0.843,"HI","MATH","MATH-111 2015-2016 T18","d21d9cb4aa5010e04915b07938411de5" +"2015-2016","0",4.49,4.5,"GM",7,44,20,NA,NA,NA,0,"MATH-111",0.821,"HI","MATH","MATH-111 2015-2016 T18","29662a9a3f0c300d3d0abb6a4e2f9b98" +"2015-2016","0",3.12,3,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.826,"HI","MATH","MATH-111 2015-2016 T18","7d42ba2003cc452f4802b0b6a5857c8e" +"2015-2016","0",5.27,6,"GM",16,40,40,43,142,30,13,"MATH-111",0.851,"HI","MATH","MATH-111 2015-2016 T18","763072e974e870eb283038dc9fa26dcb" +"2015-2016","0",4.1,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.834,"HI","MATH","MATH-111 2015-2016 T18","587e5c0f8fefd025e96bd470c7b844b0" +"2015-2016","0",4.2,4,"GM",12,40,30,NA,NA,NA,0,"MATH-111",0.81,"HI","MATH","MATH-111 2015-2016 T18","5b83d87b788c80386ce3eb7ee513f2ea" +"2015-2016","0",5.25,5.5,"GM",4,44,10,6,142,0,1,"MATH-111",0.916666666666667,"HI","MATH","MATH-111 2015-2016 T18","65705c1d68efc68e68cf042fbcab4f29" +"2015-2016","0",4.87,6,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.828,"HI","MATH","MATH-111 2015-2016 T18","3b643586ea6a40d897bd66a572dc5b5a" +"2015-2016","0",3.55,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.926,"HI","MATH","MATH-111 2015-2016 T18","f3a71ac56a3a9e9e29c615307026fcef" +"2015-2016","0",NA,2,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",NA,NA,"MATH","MATH-111 2015-2016 T18","1047273525de7c244d28cd8f011a5ded" +"2015-2016","0",4.1,3.5,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.9,"HI","MATH","MATH-111 2015-2016 T18","08bdd0e5c5b4f941fad0b9d7c2823f66" +"2015-2016","0",3.85,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.875,"HI","MATH","MATH-111 2015-2016 T18","135b6199297dc1acaa2f6959d2b7cddf" +"2015-2016","0",2.73,2.5,"GM",43,40,100,108,142,80,-Inf,"MATH-111",0.8195,"HI","MATH","MATH-111 2015-2016 T18","f0928b7621aa11ef9927ea44f55df466" +"2015-2016","0",4.08,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.875,"HI","MATH","MATH-111 2015-2016 T18","3fd127ae1eaaec6ae213c8d898853ea7" +"2015-2016","0",3.98,2.5,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.825,"HI","MATH","MATH-111 2015-2016 T18","12b58cd97e03675281b3f985b9139347" +"2015-2016","0",NA,2,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.791666666666667,"LO","MATH","MATH-111 2015-2016 T18","58f1846af58d4eaec0b15883a3149430" +"2015-2016","0",NA,3,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.803333333333333,"LO","MATH","MATH-111 2015-2016 T18","56455b810e41fb5ddf0f39f975da8b66" +"2015-2016","0",3.64,2.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.725,"LO","MATH","MATH-111 2015-2016 T18","45c314ea4ce6b4c25605a483ac6cd5c8" +"2015-2016","0",5.11,5,"GM",5,40,10,NA,NA,NA,0,"MATH-111",0.8865,"HI","MATH","MATH-111 2015-2016 T18","51f374a686c291a192bb715af5e75010" +"2015-2016","0",3.62,2.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.796666666666667,"LO","MATH","MATH-111 2015-2016 T18","790beb16e7c0f0203b0ed0ad1a24395b" 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T18","f7dcc7a70c0ea91ca2161038e5030a5c" +"2015-2016","0",4.18,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.9275,"HI","MATH","MATH-111 2015-2016 T18","347a53a37273c8694eb34c5ac42748f3" +"2015-2016","0",3.76,3,"GM",2,40,10,NA,NA,NA,-Inf,"MATH-111",0.8165,"HI","MATH","MATH-111 2015-2016 T18","100c5c69b01ac2278003da529195ccee" +"2015-2016","0",3.03,2.5,"GM",6,40,20,NA,NA,NA,0,"MATH-111",NA,NA,"MATH","MATH-111 2015-2016 T18","bd70216c2484f4274b9d068518472c9f" +"2015-2016","0",NA,2,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.666666666666667,"LO","MATH","MATH-111 2015-2016 T18","848a4d080982eaded617d1200c4384c0" +"2015-2016","0",3.14,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","01d1f0bf2da74b9b96178d84b598fb0d" +"2015-2016","0",4.19,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.816666666666667,"HI","MATH","MATH-111 2015-2016 T18","9e8f5c501974d6dc769c05716782d9af" 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T18","962cacf31057e539775c78eb46c0d8f6" +"2015-2016","0",3.65,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.9155,"HI","MATH","MATH-111 2015-2016 T18","283d52bfd1e077238591a2ffe398f6a9" +"2015-2016","0",4.54,5.5,"GM",1,44,0,NA,NA,NA,0,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","d75043c8c529950d417faddaf1c4ab97" +"2015-2016","0",2.8,2,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","31381b126a62c6242bbdbddf2ec3cde5" +"2015-2016","0",4.78,5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.845,"HI","MATH","MATH-111 2015-2016 T18","869a2cca3dfdc74663cef329e59f3366" +"2015-2016","0",5.04,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.871666666666667,"HI","MATH","MATH-111 2015-2016 T18","4deb420c823691e662332051d50f688d" +"2015-2016","0",4.27,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8,"LO","MATH","MATH-111 2015-2016 T18","51ef2eaee0ac2d8d65ac3ff8c9c7c54d" +"2015-2016","0",3.81,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8105,"HI","MATH","MATH-111 2015-2016 T18","8d6c53173537aaf0e27ee4295b63740a" +"2015-2016","0",5.24,5,"GM",1,40,0,NA,NA,NA,-Inf,"MATH-111",0.864,"HI","MATH","MATH-111 2015-2016 T18","0b71be5ad608385a801314b156f57ca1" +"2015-2016","0",4.22,4,"GM",2,40,10,3,142,0,0,"MATH-111",0.9065,"HI","MATH","MATH-111 2015-2016 T18","76485e318b1a1195a650d9ea21a184c4" +"2015-2016","0",4.37,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.883333333333333,"HI","MATH","MATH-111 2015-2016 T18","892c2fe3b263adf7bedf5fc629d93f24" +"2015-2016","0",NA,0,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.833333333333333,"HI","MATH","MATH-111 2015-2016 T18","ad896a452493ffd71f5935dc8d263851" +"2015-2016","0",NA,3,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.7,"LO","MATH","MATH-111 2015-2016 T18","cf7207efa34a22d62b2ebb11b4ba940c" +"2015-2016","0",NA,0,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.701666666666667,"LO","MATH","MATH-111 2015-2016 T18","8cb5c0daf011329b7eebedf7f2c89874" 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+"2015-2016","0",4.73,5,"GM",27,40,70,14,142,10,5,"MATH-111",0.9285,"HI","MATH","MATH-111 2015-2016 T18","6ffe172b9a890fa744753199e615e55f" +"2015-2016","0",4.76,4.5,"GM",13,40,30,5,142,0,0,"MATH-111",0.8825,"HI","MATH","MATH-111 2015-2016 T18","2a1f3f137f87e894bf4788ff02197c72" +"2015-2016","0",4.79,5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8,"LO","MATH","MATH-111 2015-2016 T18","618507c036476d12b3d5fdada3cf9b45" +"2015-2016","0",4.07,4.5,"GM",3,40,10,NA,NA,NA,-Inf,"MATH-111",NA,NA,"MATH","MATH-111 2015-2016 T18","cf5e69ee6c8e4313a201d7a3fcafac35" +"2015-2016","0",3.62,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","09019e065ba185126a6e9dc10ebfbc45" +"2015-2016","0",NA,2,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.7,"LO","MATH","MATH-111 2015-2016 T18","36fc2d7af309060d355658da1444d34f" +"2015-2016","0",NA,2.5,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.72,"LO","MATH","MATH-111 2015-2016 T18","af3fd4bbaa16421f60b5e6f2237311d6" +"2015-2016","0",5.6,6,"GM",5,40,10,23,142,20,20,"MATH-111",0.917,"HI","MATH","MATH-111 2015-2016 T18","dbce25bf211f98b013334100158fdb3a" +"2015-2016","0",4.24,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.755,"LO","MATH","MATH-111 2015-2016 T18","49982cb593da89004d782c7bd628ce44" +"2015-2016","0",4.25,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8735,"HI","MATH","MATH-111 2015-2016 T18","ac8fd64065cffc1e109e1086fb0f72e5" +"2015-2016","0",4.11,4,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.8615,"HI","MATH","MATH-111 2015-2016 T18","aac8dadd336633360c8195a5f91b7cad" +"2015-2016","0",NA,3,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.826,"HI","MATH","MATH-111 2015-2016 T18","d439b66fdbf58658f4579e4114ee46d2" +"2015-2016","0",NA,3,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.925,"HI","MATH","MATH-111 2015-2016 T18","0236b16a60a5d5df9d3f021e0fa37074" +"2015-2016","0",4.14,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.809,"HI","MATH","MATH-111 2015-2016 T18","03f84482a97b0681317e2977d0908529" +"2015-2016","0",NA,3.5,"GM",5,40,10,NA,NA,NA,0,"MATH-111",0.923333333333333,"HI","MATH","MATH-111 2015-2016 T18","99de24157639e3fe2f346e2891ff34a5" +"2015-2016","0",2.09,1,"GM",1,40,0,NA,NA,NA,0,"MATH-111",0.716666666666667,"LO","MATH","MATH-111 2015-2016 T18","a434224678fa8be6a0ead856051625d9" +"2015-2016","0",2.77,2.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",NA,NA,"MATH","MATH-111 2015-2016 T18","3a01f3c02572d9e2b5acf4b2e3624f56" +"2015-2016","0",5.47,6,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.981666666666667,"HI","MATH","MATH-111 2015-2016 T18","654817d392a2604acf59b318dd1f951b" +"2015-2016","0",2.89,2.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.708333333333333,"LO","MATH","MATH-111 2015-2016 T18","f7248b506fb4a039ba85c8924906c6a3" +"2015-2016","0",4.13,3.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.85,"HI","MATH","MATH-111 2015-2016 T18","da83b5ca119f8fe21df390325ce58e82" +"2015-2016","0",2.54,3,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.816666666666667,"HI","MATH","MATH-111 2015-2016 T18","c360b01d03f36705754e9d65c2978e0d" +"2015-2016","0",3.52,2.5,"GM",7,40,20,NA,NA,NA,0,"MATH-111",0.833333333333333,"HI","MATH","MATH-111 2015-2016 T18","a68b2f153dee8679955a62b9da2a541e" +"2015-2016","0",3.9,2,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.791666666666667,"LO","MATH","MATH-111 2015-2016 T18","d3ea4796865fc3e68b412c458a300269" +"2015-2016","0",3.18,2.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.708333333333333,"LO","MATH","MATH-111 2015-2016 T18","669652d182c237eab45fc6a3f826cdd1" +"2015-2016","0",4.5,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.905,"HI","MATH","MATH-111 2015-2016 T18","92be38000ccdda26c1622d0ff6184245" +"2015-2016","0",3.14,3,"GM",NA,NA,NA,NA,NA,NA,0,"MATH-111",0.75,"LO","MATH","MATH-111 2015-2016 T18","4a7b5765c487ac822cb571b9f808a0e7" +"2015-2016","0",4.55,4.5,"GM",NA,NA,NA,NA,NA,NA,-Inf,"MATH-111",0.766666666666667,"LO","MATH","MATH-111 2015-2016 T18","2ac9553d9f5538da4a37de99ce3b73c9" 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+"2013-2014","0",3.91,3.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.701666666666667,"LO","EE","EE-102 2013-2014 T29","e7f556496141439d95ea489e6cf326ec" +"2013-2014","1",4.74,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.85,"HI","EE","EE-102 2013-2014 T29","60e58a9b171ed8b9add7b59a73d8936c" +"2013-2014","0",4.05,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.805,"LO","EE","EE-102 2013-2014 T29","a65fbc867a33b0526eca35837d3c7747" +"2013-2014","1",4.39,4,"MT",13,14,90,4,52,10,0,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","394db6192bd0c1abe25c65228ec8b42e" +"2013-2014","1",3.99,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7525,"LO","EE","EE-102 2013-2014 T29","ae063254ac7db7893f47b9e2d37d3950" +"2013-2014","1",4.23,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.716666666666667,"LO","EE","EE-102 2013-2014 T29","66466a1d22f39114a97967f02e27954d" +"2013-2014","1",NA,0,"MT",NA,NA,NA,NA,NA,NA,0,"EE-102",0.711666666666667,"LO","EE","EE-102 2013-2014 T29","37477478922f9d50bb4716437faacc3b" +"2013-2014","1",3.51,3,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.761666666666667,"LO","EE","EE-102 2013-2014 T29","b069c08f1c0ceb47a6accae7bb8fac3a" +"2013-2014","0",3.87,4.5,"MT",14,14,100,36,52,70,0,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","afd524d768f71c255d8138701f2f3d47" +"2013-2014","1",4.35,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.6945,"LO","EE","EE-102 2013-2014 T29","0054254e17b14c1b6a234c046264d033" +"2013-2014","0",4.25,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.8035,"LO","EE","EE-102 2013-2014 T29","992fe8fed24e25a6e378d587e8f2159e" +"2013-2014","1",3.16,3.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.708333333333333,"LO","EE","EE-102 2013-2014 T29","1d3b23456ae1cc85e6a9ce5d5b7d01a5" +"2013-2014","1",2.9,2.5,"MT",9,14,60,1,52,0,1,"EE-102",0.666666666666667,"LO","EE","EE-102 2013-2014 T29","11aeb0c80ca40307925097fe52adf68a" +"2013-2014","0",2.72,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7235,"LO","EE","EE-102 2013-2014 T29","330c4281e79e3b90b8a5e021074e137a" +"2013-2014","1",4.42,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.858333333333333,"HI","EE","EE-102 2013-2014 T29","3fe0f95f64c31e7d64858e5d3b42b766" +"2013-2014","0",5.17,4,"MT",8,14,60,14,52,30,17,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","5cfdd8c212ad2273ce55eb57edd8cdbe" +"2013-2014","0",4.82,5,"MT",10,14,70,NA,NA,NA,0,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","b4d4de4271c6ec06e35c9262cc14e98e" +"2013-2014","0",5.31,5.5,"MT",14,14,100,23,52,40,0,"EE-102",1,"HI","EE","EE-102 2013-2014 T29","58ab1cd282afd7703981487a33f64a38" +"2013-2014","1",4.19,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.666666666666667,"LO","EE","EE-102 2013-2014 T29","5bc2789334431cba4116e328887c2006" +"2013-2014","1",4.09,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.785,"LO","EE","EE-102 2013-2014 T29","4c35eeec288418057044f53fdd036eda" +"2013-2014","0",3.76,4.5,"MT",2,14,10,13,52,30,19,"EE-102",0.755,"LO","EE","EE-102 2013-2014 T29","26bbc04c490b73bf875d7890d41d2a1a" +"2013-2014","0",3.91,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","d46f2b03090b600034cf4abd7dd04116" +"2013-2014","0",4.41,5.5,"MT",1,14,10,NA,NA,NA,0,"EE-102",0.916666666666667,"HI","EE","EE-102 2013-2014 T29","6961f1b8ca51728976e022b15300fdb5" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.733333333333333,"LO","EE","EE-102 2013-2014 T29","79c2b84f2d86c7b4d02f105a5592c580" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.733333333333333,"LO","EE","EE-102 2013-2014 T29","c676d8e1713b31a8d19de075df00a32d" +"2013-2014","0",3.64,3.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.673333333333333,"LO","EE","EE-102 2013-2014 T29","fd94e6b6fd2b9be8a70b4c5c3f2e6943" +"2013-2014","1",4.17,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.678333333333333,"LO","EE","EE-102 2013-2014 T29","1ac76ea33b3fc3d253ccde1f0adfe5ed" +"2013-2014","1",3.7,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.733333333333333,"LO","EE","EE-102 2013-2014 T29","0aaf34d2e55a3f41753ebc4939187614" +"2013-2014","1",4.47,3.5,"MT",3,14,20,8,52,20,0,"EE-102",0.773333333333333,"LO","EE","EE-102 2013-2014 T29","e16b60f8c118cd8fd421e4de4c51e04e" +"2013-2014","1",4.34,4,"MT",8,14,60,4,52,10,0,"EE-102",0.816666666666667,"HI","EE","EE-102 2013-2014 T29","942c32ba1e60ab5922429fa0d0372573" +"2013-2014","1",4.22,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.84,"HI","EE","EE-102 2013-2014 T29","a95cd615f11e489a1eb21e01cb77e74b" +"2013-2014","0",4.42,3.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.85,"HI","EE","EE-102 2013-2014 T29","074e53e12ae0de935a67c92d9ca287df" +"2013-2014","0",2.36,1.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","ed134ad25d65867ab43af63c53ccda41" +"2013-2014","0",4.68,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","d71b2c498d2e20c866cfc62244f893df" +"2013-2014","1",3.94,3.5,"MT",NA,NA,NA,NA,NA,NA,0,"EE-102",0.739,"LO","EE","EE-102 2013-2014 T29","a03343b7cfb8a7174b037a7abe888cff" +"2013-2014","1",4.33,4,"MT",7,14,50,2,52,0,4,"EE-102",0.763,"LO","EE","EE-102 2013-2014 T29","18faecc7bf75407253ec781300a9a11e" +"2013-2014","0",5.02,5.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.857368421052632,"HI","EE","EE-102 2013-2014 T29","2b0359cb4e946e798fe613787382df6c" +"2013-2014","0",4.22,4,"MT",13,14,90,5,52,10,0,"EE-102",0.783333333333333,"LO","EE","EE-102 2013-2014 T29","e0841f61b7924462b8677da9f89f27c8" +"2013-2014","0",2.56,2,"MT",1,14,10,NA,NA,NA,0,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","e4484df3664e98a3aed079ce5b67e6ae" +"2013-2014","0",3.67,4,"MT",10,14,70,12,52,20,23,"EE-102",0.8325,"HI","EE","EE-102 2013-2014 T29","f6928bbe67c48fee90ea893e0ff334f4" +"2013-2014","0",4.56,5,"MT",5,14,40,16,52,30,0,"EE-102",0.867,"HI","EE","EE-102 2013-2014 T29","a44786e188d69d87ea6e3c184676c5f4" +"2013-2014","0",4.2,4,"MT",4,14,30,12,52,20,0,"EE-102",0.821,"HI","EE","EE-102 2013-2014 T29","aef90c4d27a59331e4acbc85558af563" +"2013-2014","0",4.18,4.5,"MT",11,14,80,20,52,40,15,"EE-102",0.803333333333333,"LO","EE","EE-102 2013-2014 T29","4fa13b9a58ed244324f50ccee7afd339" +"2013-2014","0",4.59,5,"MT",11,14,80,21,52,40,0,"EE-102",0.822,"HI","EE","EE-102 2013-2014 T29","5e0876068e5c484561d50cbdbfbeb6f1" +"2013-2014","0",5.07,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.966666666666667,"HI","EE","EE-102 2013-2014 T29","b7930f21a3bfbd3a2bfb7b315fb00e9b" +"2013-2014","0",4.62,4.5,"MT",4,14,30,NA,NA,NA,0,"EE-102",0.8,"LO","EE","EE-102 2013-2014 T29","b84c49c2f4f1fec4edc22cfa36e067cd" +"2013-2014","0",4.35,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.766666666666667,"LO","EE","EE-102 2013-2014 T29","89066d604ba1a165c53b6e84a4e0c14c" +"2013-2014","0",2.81,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.716666666666667,"LO","EE","EE-102 2013-2014 T29","7ed4808d70aea59c8a52bc869d1c0c54" +"2013-2014","0",3.93,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.826666666666667,"HI","EE","EE-102 2013-2014 T29","b363204b6da713c2304e2b1d86dc2092" +"2013-2014","0",3.87,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.83,"HI","EE","EE-102 2013-2014 T29","6f409d8d11fc0a47047576dc08bf2840" +"2013-2014","0",2.88,1.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.655,"LO","EE","EE-102 2013-2014 T29","bb01d299382408ffe897deefa08b7599" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","dc635b654bf102df7322430139d8f222" +"2013-2014","0",3.22,3,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.8105,"HI","EE","EE-102 2013-2014 T29","dc7b73d2febad55d0a9c2679e34e787e" +"2013-2014","0",3.47,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.8575,"HI","EE","EE-102 2013-2014 T29","7b4ba7dc55472ee59a4f51e62e075170" +"2013-2014","0",3.18,2,"MT",11,14,80,13,52,30,0,"EE-102",0.801,"LO","EE","EE-102 2013-2014 T29","9ffbdd9ca2e052b2161ade9b962bc1fc" +"2013-2014","0",4.84,5.5,"MT",3,14,20,NA,NA,NA,0,"EE-102",0.8065,"HI","EE","EE-102 2013-2014 T29","42fe1417bf6a017209be9e014e8e61d6" +"2013-2014","0",NA,0,"MT",5,14,40,NA,NA,NA,0,"EE-102",0.742,"LO","EE","EE-102 2013-2014 T29","622c02a20428203337583a6e931cf721" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.798888888888889,"LO","EE","EE-102 2013-2014 T29","e94a9f8c12de52600f1c83561d5e7365" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.791666666666667,"LO","EE","EE-102 2013-2014 T29","d8b2ad433dff4291a23e42bf2a265f44" +"2013-2014","0",2.35,1.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7025,"LO","EE","EE-102 2013-2014 T29","65b02ae8ba7baf2d1406d124bd7e69fa" +"2013-2014","0",4.57,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.76,"LO","EE","EE-102 2013-2014 T29","cd525278208e09ff724f24196826fef7" +"2013-2014","0",4.49,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.912698412698413,"HI","EE","EE-102 2013-2014 T29","95a58db67bcd553f12cdf2ac0552a5c1" +"2013-2014","0",3.14,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.8525,"HI","EE","EE-102 2013-2014 T29","b71148a5aceb568e31fb6f30b76de81e" +"2013-2014","0",3.29,3.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.905,"HI","EE","EE-102 2013-2014 T29","aff7c959506a30208c1da31cdd0852f9" +"2013-2014","0",4.8,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.846666666666667,"HI","EE","EE-102 2013-2014 T29","ea7508d589e93a94337b1d3e43e3ea0e" +"2013-2014","0",4.93,4.5,"MT",4,14,30,NA,NA,NA,0,"EE-102",0.851,"HI","EE","EE-102 2013-2014 T29","a1f1288d38e9fc198f753110f012509c" +"2013-2014","0",NA,1.5,"MT",3,14,20,NA,NA,NA,0,"EE-102",0.8445,"HI","EE","EE-102 2013-2014 T29","54a85ba1aa1dd40e707a33518a5d26ff" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.843,"HI","EE","EE-102 2013-2014 T29","05dbe3e0417c53c07339baccc9673e30" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.811666666666667,"HI","EE","EE-102 2013-2014 T29","c5e4d1878d38d0631eaa2ca2d459af75" +"2013-2014","0",3.23,3,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7815,"LO","EE","EE-102 2013-2014 T29","bafc64f3fbf7c515e088e8bd91446bd4" +"2013-2014","0",3.31,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","742574908a0863a9477623eaee556b8b" +"2013-2014","0",4.28,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.743333333333333,"LO","EE","EE-102 2013-2014 T29","3c67db1af01cb2d8be72e070c58c89b4" +"2013-2014","0",3.76,3.5,"MT",2,14,10,NA,NA,NA,0,"EE-102",0.8485,"HI","EE","EE-102 2013-2014 T29","77d8e69683fd10daabb500cecfd7ee9c" +"2013-2014","0",2.58,1.5,"MT",11,14,80,NA,NA,NA,0,"EE-102",0.8325,"HI","EE","EE-102 2013-2014 T29","14bf17414d974dcc12dd9ca05b37875f" +"2013-2014","0",3.1,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.738,"LO","EE","EE-102 2013-2014 T29","1148019ef528d013b31fd485e0cbaa23" +"2013-2014","0",4.35,4.5,"MT",9,14,60,19,52,40,0,"EE-102",0.803333333333333,"LO","EE","EE-102 2013-2014 T29","f36dffeda7cb7ccac6b536c7f3ffc358" +"2013-2014","0",4.94,5.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.85,"HI","EE","EE-102 2013-2014 T29","2b173062c0958a346afd79b802d8f1ac" +"2013-2014","0",3.77,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.747,"LO","EE","EE-102 2013-2014 T29","9d914696b56a252d61d6c527ddea9e2d" +"2013-2014","0",3.96,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.756666666666667,"LO","EE","EE-102 2013-2014 T29","de3dccd8031102b04832c15431b03ff3" +"2013-2014","0",4.17,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","fe9a10645661d912c8b2f93b42fb3a66" +"2013-2014","0",3.67,3,"MT",9,14,60,NA,NA,NA,0,"EE-102",0.8535,"HI","EE","EE-102 2013-2014 T29","f5bf83cd5ecd381c584a56b0d411229f" +"2013-2014","0",3.13,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.788,"LO","EE","EE-102 2013-2014 T29","0f1df9bac93d5eb04aa5808b9317bae7" +"2013-2014","0",3.46,3.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","a3fc0e3b3b42995a6e0bf696e48c24b5" +"2013-2014","0",3.23,3,"MT",3,14,20,5,52,10,0,"EE-102",0.7605,"LO","EE","EE-102 2013-2014 T29","2c9c21cbb7a0c64675ad373d03ba74c9" +"2013-2014","0",4.55,5,"MT",3,14,20,NA,NA,NA,0,"EE-102",0.833333333333333,"HI","EE","EE-102 2013-2014 T29","f30a5f99ef735e5d2d6960768ca36b83" +"2013-2014","0",4.47,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.803333333333333,"LO","EE","EE-102 2013-2014 T29","c22ac7f63d2693c3231958e6d94fb8d4" +"2013-2014","0",3.99,4,"MT",8,14,60,NA,NA,NA,-Inf,"EE-102",0.833333333333333,"HI","EE","EE-102 2013-2014 T29","f11078438e95181a915563726d72dacc" +"2013-2014","0",4.68,5,"MT",6,14,40,NA,NA,NA,0,"EE-102",0.8775,"HI","EE","EE-102 2013-2014 T29","cbf9723c84ef5aa75974c8f6da6ae0e8" +"2013-2014","0",3.06,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.773333333333333,"LO","EE","EE-102 2013-2014 T29","008da5bb7830fcbbd6acfa4ad4713f4b" +"2013-2014","0",3.59,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.6905,"LO","EE","EE-102 2013-2014 T29","3bc72263401ec7ac5ed71846853d1fba" +"2013-2014","0",4.74,4,"MT",5,14,40,NA,NA,NA,0,"EE-102",0.8105,"HI","EE","EE-102 2013-2014 T29","256fc632fafe22d18454783514d77ca3" +"2013-2014","0",4.84,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.808333333333333,"HI","EE","EE-102 2013-2014 T29","e550f272d8a4573082d9d65841e9b322" +"2013-2014","0",2.8,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.768333333333333,"LO","EE","EE-102 2013-2014 T29","60485f69d925cc1e7e66158e1ec50097" +"2013-2014","0",4.08,5,"MT",9,14,60,NA,NA,NA,0,"EE-102",0.833333333333333,"HI","EE","EE-102 2013-2014 T29","ccd4526ca850a78b8fa04f2fd29cdf02" +"2013-2014","0",4.99,5.5,"MT",1,14,10,NA,NA,NA,-Inf,"EE-102",0.9105,"HI","EE","EE-102 2013-2014 T29","64bc9e31cab1845eed9e363c13076c10" +"2013-2014","0",4.26,4.5,"MT",2,14,10,3,52,10,0,"EE-102",0.85,"HI","EE","EE-102 2013-2014 T29","bcfe782eec819542bbee8d0c9565929f" +"2013-2014","0",4.48,4,"MT",4,14,30,NA,NA,NA,0,"EE-102",0.83,"HI","EE","EE-102 2013-2014 T29","41d4e0dc1c4e34a52b49ebadd0c3525c" +"2013-2014","0",NA,2,"MT",1,14,10,NA,NA,NA,0,"EE-102",0.6605,"LO","EE","EE-102 2013-2014 T29","df2c22d1945da1d022676e752dd16f1f" +"2013-2014","0",3.84,4.5,"MT",4,14,30,NA,NA,NA,-Inf,"EE-102",0.8625,"HI","EE","EE-102 2013-2014 T29","7023989d39f0d6210b843949e51e6a90" +"2013-2014","0",3.26,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7655,"LO","EE","EE-102 2013-2014 T29","8945eada60f00666499789c9eb2a92ce" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.666666666666667,"LO","EE","EE-102 2013-2014 T29","86302d48ad72f444027462b658f96262" +"2013-2014","0",4.55,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.883333333333333,"HI","EE","EE-102 2013-2014 T29","35bc9dc661c9ea67240368e1dcad098b" +"2013-2014","0",4.24,4.5,"MT",2,14,10,NA,NA,NA,0,"EE-102",0.718,"LO","EE","EE-102 2013-2014 T29","d4d93fb924ba693df91221e34fcec045" +"2013-2014","0",4.16,5,"MT",1,14,10,NA,NA,NA,0,"EE-102",0.805,"LO","EE","EE-102 2013-2014 T29","d512a3df2923877ee4d7f2474be048b8" +"2013-2014","0",3.15,2.5,"MT",1,14,10,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","84d8ceaca3e942c7545e1a0c18650bdc" +"2013-2014","0",2.92,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.772,"LO","EE","EE-102 2013-2014 T29","8ade6a1a54efc71c736ccef469b74e4f" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7235,"LO","EE","EE-102 2013-2014 T29","22613b7b6d98b5923ac4806ffd86a27f" +"2013-2014","0",4.57,4.5,"MT",2,14,10,NA,NA,NA,0,"EE-102",0.893,"HI","EE","EE-102 2013-2014 T29","e91cf64cd7000949d263fa63fadabf9c" +"2013-2014","0",4.14,4.5,"MT",8,14,60,NA,NA,NA,0,"EE-102",0.734,"LO","EE","EE-102 2013-2014 T29","8de6b725abba55a9d3d252d0f23c3adc" +"2013-2014","0",3.99,4.5,"MT",8,14,60,NA,NA,NA,-Inf,"EE-102",0.7065,"LO","EE","EE-102 2013-2014 T29","df7b15205045849765fa033980a2fbe4" +"2013-2014","0",3.4,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7605,"LO","EE","EE-102 2013-2014 T29","ab97e1baf7474644fec1a111a26d1d13" +"2013-2014","0",4.11,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.863333333333333,"HI","EE","EE-102 2013-2014 T29","fce31f7477bfef9c77b1da343fd82b27" +"2013-2014","0",3.52,3.5,"MT",12,14,90,12,52,20,-Inf,"EE-102",0.733333333333333,"LO","EE","EE-102 2013-2014 T29","a225d179ba0ec20f3a7d7b8c6891bbf1" +"2013-2014","0",4.23,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.761666666666667,"LO","EE","EE-102 2013-2014 T29","6fc51bf2000120c630d68885b2070d22" +"2013-2014","0",NA,0,"MT",2,14,10,8,52,20,0,"EE-102",0.695,"LO","EE","EE-102 2013-2014 T29","42ab0a914d35c2965062b30f11dde38a" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.78,"LO","EE","EE-102 2013-2014 T29","f5f2cd4ac4b38051355a1e6a1cd91b1e" +"2013-2014","0",4.64,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.833333333333333,"HI","EE","EE-102 2013-2014 T29","ed0693d83fa42193c61c739ad3f8b3aa" +"2013-2014","0",4.5,5,"MT",5,14,40,8,52,20,0,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","45080a933273db78231cbd1fcf227f4a" +"2013-2014","0",4.23,4.5,"MT",4,14,30,NA,NA,NA,0,"EE-102",0.816666666666667,"HI","EE","EE-102 2013-2014 T29","d85914cdee6f936ad1e3bd10c3adc14d" +"2013-2014","0",4.74,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","39e98548f7418a5441ef47ee5873d7c7" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","ff99a61caf6d22efb9df30146a226f1d" +"2013-2014","0",4.11,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.916666666666667,"HI","EE","EE-102 2013-2014 T29","1dd2c83953f1a8001e347446099e13f7" +"2013-2014","0",3.87,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.817,"HI","EE","EE-102 2013-2014 T29","74b2fb6141212a3f13ea9396cc3c8921" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7105,"LO","EE","EE-102 2013-2014 T29","6639b53604338aa85d52020bc3a8b26d" +"2013-2014","0",2.81,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.767,"LO","EE","EE-102 2013-2014 T29","9c1028a0a0ca3ca05756ac2576027754" +"2013-2014","0",2.75,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.708333333333333,"LO","EE","EE-102 2013-2014 T29","f08d5db2e3dbab917ac0e1cad92ed825" +"2013-2014","0",4.99,5,"MT",5,14,40,18,52,40,0,"EE-102",0.95,"HI","EE","EE-102 2013-2014 T29","6937efee4e13cbb589c918df859500be" +"2013-2014","0",2.52,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","8a94d172906b57736811b5e73e7bce8f" +"2013-2014","0",3.81,4.5,"MT",1,14,10,NA,NA,NA,0,"EE-102",0.764,"LO","EE","EE-102 2013-2014 T29","be2476cbe81586c5d5dfbdf6ade16be8" +"2013-2014","0",3.94,3,"MT",5,14,40,NA,NA,NA,-Inf,"EE-102",0.8815,"HI","EE","EE-102 2013-2014 T29","c3219cb7640f6d2fe8b2d2158e70f1ca" +"2013-2014","0",5.55,5.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.8405,"HI","EE","EE-102 2013-2014 T29","5017d2e71ba06e891270ae9441ae89f5" +"2013-2014","0",3.67,3,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.761666666666667,"LO","EE","EE-102 2013-2014 T29","87e95dc6c0a699b6003c94b15cdd070c" +"2013-2014","0",3.99,3.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.768333333333333,"LO","EE","EE-102 2013-2014 T29","68594117e92de12103cee49f1191740e" +"2013-2014","0",3.94,4,"MT",5,14,40,NA,NA,NA,0,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","f99a2bbeaaedd60d6836899921d113b5" +"2013-2014","0",4.18,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.8525,"HI","EE","EE-102 2013-2014 T29","8cae559f5cbcd328e50523a9ab57e732" +"2013-2014","0",3.6,4,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.709,"LO","EE","EE-102 2013-2014 T29","fe81900f51fe80ea0a1769d1a8c90197" +"2013-2014","0",5.02,6,"MT",1,14,10,NA,NA,NA,0,"EE-102",0.8,"LO","EE","EE-102 2013-2014 T29","4d40cb54fc188ce6161d6d620348e8cf" +"2013-2014","0",NA,0,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.726666666666667,"LO","EE","EE-102 2013-2014 T29","c3b0598b7b481e16bb6b857a3c7ae4ee" +"2013-2014","0",NA,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.726666666666667,"LO","EE","EE-102 2013-2014 T29","5249951bf5d136560fc7e840cfbf73df" +"2013-2014","0",4.21,3,"MT",4,14,30,NA,NA,NA,0,"EE-102",0.7695,"LO","EE","EE-102 2013-2014 T29","e67f4791aac73c9b7d09279b9b84b069" +"2013-2014","0",2.97,3,"MT",2,14,10,NA,NA,NA,0,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","1464b3e0af6f31238378d1165ee72d69" +"2013-2014","0",2.92,1.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.785,"LO","EE","EE-102 2013-2014 T29","2a375c5f9e569b7d7c25c6b8eb008639" +"2013-2014","0",2.95,3,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","e6a759e08faee44427d18619e604b30e" +"2013-2014","0",3.74,3,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7065,"LO","EE","EE-102 2013-2014 T29","e2fa77f5a49e3c6d2350294435bdb142" +"2013-2014","0",3.65,2.5,"MT",8,14,60,1,52,0,0,"EE-102",0.87,"HI","EE","EE-102 2013-2014 T29","9b87fce9935e9a39286f67731523f3cd" +"2013-2014","0",3.88,4,"MT",5,14,40,NA,NA,NA,0,"EE-102",0.814,"HI","EE","EE-102 2013-2014 T29","ef987adeda5ae4de98cffd63eb7d8be0" +"2013-2014","0",2.36,1.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.743,"LO","EE","EE-102 2013-2014 T29","d4d4305f4bb2e5b05e1d731d82ca9ca4" +"2013-2014","0",3.99,4,"MT",6,14,40,NA,NA,NA,0,"EE-102",0.7035,"LO","EE","EE-102 2013-2014 T29","43182e048dca79b92c66dc913d4454c5" +"2013-2014","0",2.73,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.7035,"LO","EE","EE-102 2013-2014 T29","07050c6154c58dfa48493438ccc1001e" +"2013-2014","0",3.67,4,"MT",12,14,90,6,52,10,0,"EE-102",0.821666666666667,"HI","EE","EE-102 2013-2014 T29","abd72d81c28f671daff055a28a2c1893" +"2013-2014","0",2.95,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.738333333333333,"LO","EE","EE-102 2013-2014 T29","05037b053ea73f13d82f199b8d4048f3" +"2013-2014","0",4.3,3.5,"MT",14,14,100,35,52,70,0,"EE-102",0.833333333333333,"HI","EE","EE-102 2013-2014 T29","6b6972a68211fc3f072ce184e2255e76" +"2013-2014","0",3.61,4,"MT",3,14,20,NA,NA,NA,0,"EE-102",0.798333333333333,"LO","EE","EE-102 2013-2014 T29","15702ba7a5229fc9b5610c2408142f6f" +"2013-2014","0",4.57,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.743333333333333,"LO","EE","EE-102 2013-2014 T29","2459a5826507128980a0bf93e8390b9c" +"2013-2014","0",2.71,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.75,"LO","EE","EE-102 2013-2014 T29","a1ea68139c3af5f20255aa250345a236" +"2013-2014","0",2.89,2.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","b9333b7bbe500b6e09336dfe39b13006" +"2013-2014","0",4.79,4.5,"MT",9,14,60,NA,NA,NA,0,"EE-102",0.9,"HI","EE","EE-102 2013-2014 T29","fe01141eeb35cf97feba04628819855c" +"2013-2014","0",3.13,2,"MT",10,14,70,NA,NA,NA,-Inf,"EE-102",0.697,"LO","EE","EE-102 2013-2014 T29","e06f4e355d57f4a5b4f6bbfa5f6fe627" +"2013-2014","0",3.4,2.5,"MT",5,14,40,14,52,30,0,"EE-102",0.675,"LO","EE","EE-102 2013-2014 T29","66e9367199e8f5ecd20df75bffa827d4" +"2013-2014","0",4.7,4.5,"MT",5,14,40,NA,NA,NA,0,"EE-102",0.888,"HI","EE","EE-102 2013-2014 T29","dbd5bbf9f03ae2b706b1e9de7e8e3343" +"2013-2014","0",3.35,3.5,"MT",9,14,60,6,52,10,-Inf,"EE-102",0.792,"LO","EE","EE-102 2013-2014 T29","331cef582a60b31489a4e204f4351e51" +"2013-2014","0",5.62,5.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",1,"HI","EE","EE-102 2013-2014 T29","da0df5faaeb6ed24c89b9a9d09c181f7" +"2013-2014","0",3.61,3,"MT",12,14,90,34,52,70,-Inf,"EE-102",0.733333333333333,"LO","EE","EE-102 2013-2014 T29","5c5c406721183338be79c66cc6ce8266" +"2013-2014","0",4.65,4.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.833333333333333,"HI","EE","EE-102 2013-2014 T29","213dc74fd3ee3cd551ab38a442be591b" +"2013-2014","0",3.84,3,"MT",11,14,80,NA,NA,NA,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","f498702d31b078ad2fd01ca15427db74" +"2013-2014","0",4.31,5.5,"MT",3,14,20,NA,NA,NA,0,"EE-102",0.85,"HI","EE","EE-102 2013-2014 T29","1ec4256d2f0a78aabd01d6511f37fc69" +"2013-2014","0",3.72,4,"MT",3,14,20,NA,NA,NA,0,"EE-102",0.733333333333333,"LO","EE","EE-102 2013-2014 T29","f1de2907544e42f03534aea5127a9192" +"2013-2014","0",3.54,1.5,"MT",3,14,20,2,52,0,-Inf,"EE-102",0.766666666666667,"LO","EE","EE-102 2013-2014 T29","d80804f1255cd09af8ff0e0d91479e58" +"2013-2014","0",3.67,4,"MT",12,14,90,17,52,30,0,"EE-102",0.8,"LO","EE","EE-102 2013-2014 T29","34057a35328916b7f00d810784a6daec" +"2013-2014","0",NA,1.5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.723333333333333,"LO","EE","EE-102 2013-2014 T29","bdb1f813a3fac0b20f2135a7bfbb1b7e" +"2013-2014","0",3.92,3,"MT",3,14,20,8,52,20,0,"EE-102",0.883333333333333,"HI","EE","EE-102 2013-2014 T29","4e4ff0976f9813d05d7230eddc516a7f" +"2013-2014","0",4.79,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.8025,"LO","EE","EE-102 2013-2014 T29","bd75bebe374503abc86805c7d8e477ce" +"2013-2014","0",3.91,5,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.833333333333333,"HI","EE","EE-102 2013-2014 T29","fea984d018447a1a610eebcba7f07b48" +"2013-2014","0",4.06,4,"MT",3,14,20,5,52,10,-Inf,"EE-102",NA,NA,"EE","EE-102 2013-2014 T29","a3852c3433fbd040464bf4a7e6dd11be" +"2013-2014","0",2.6,2,"MT",NA,NA,NA,NA,NA,NA,-Inf,"EE-102",0.783333333333333,"LO","EE","EE-102 2013-2014 T29","ca3f8b2389bf28ff978b3cd82af7258e" 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+"2014-2015","0",4.36,5,"MA",25,58,40,12,63,20,0,"PHYS-101",1,"HI","PHYS","PHYS-101 2014-2015 T6","20644c7d9749ce091f077ae2be4e92d9" +"2014-2015","1",4.05,4,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.759,"LO","PHYS","PHYS-101 2014-2015 T6","6fa5c9549a5eceb552f26a1bd60bfb4f" +"2014-2015","1",4.56,4.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",NA,NA,"PHYS","PHYS-101 2014-2015 T6","834d386644d4d1a9e282549eb5540d22" +"2014-2015","1",NA,2,"MA",1,58,0,NA,NA,NA,0,"PHYS-101",0.717,"LO","PHYS","PHYS-101 2014-2015 T6","de79057bf622376055633191787b3cf3" +"2014-2015","1",3.4,2,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.75,"LO","PHYS","PHYS-101 2014-2015 T6","624e393a8ab3db3210ce737a3a4bf653" +"2014-2015","1",4.52,4.5,"MA",15,58,30,8,63,10,1.79387998580933,"PHYS-101",0.876,"HI","PHYS","PHYS-101 2014-2015 T6","fc2cb1818a44c11f5ebf79bbad380809" +"2014-2015","1",NA,3,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",NA,NA,"PHYS","PHYS-101 2014-2015 T6","0668dd44d404e4ad37e0910752b35861" 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T6","29a3138ab2b6b779514af2a98444ccec" +"2014-2015","1",3.5,3.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.866666666666667,"HI","PHYS","PHYS-101 2014-2015 T6","0d0e7550d755a1fe38f8e0319efaf590" +"2014-2015","1",NA,3,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.75,"LO","PHYS","PHYS-101 2014-2015 T6","16cac5a016d70182ed723541d811704f" +"2014-2015","1",4.38,4.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.803333333333333,"LO","PHYS","PHYS-101 2014-2015 T6","98071987c8f20deaf0280d8c77bf57cd" +"2014-2015","0",4.07,2.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.817,"HI","PHYS","PHYS-101 2014-2015 T6","e23523e9aa53aa37f4865abd58532d6d" +"2014-2015","1",4.35,5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.85,"HI","PHYS","PHYS-101 2014-2015 T6","e9be1698b64e2103dcbd240944fbde0b" +"2014-2015","1",5.1,5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.776,"LO","PHYS","PHYS-101 2014-2015 T6","de9be2f5e1407ea9073b86e8b1158598" +"2014-2015","1",4.38,4.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",NA,NA,"PHYS","PHYS-101 2014-2015 T6","41a2b17f62b9631fc503f813d9d3dcf3" +"2014-2015","1",2.51,1.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.75,"LO","PHYS","PHYS-101 2014-2015 T6","4a02819810d37c213d0190c471140456" +"2014-2015","0",3.57,4,"MA",37,58,60,1,63,0,0,"PHYS-101",0.5905,"LO","PHYS","PHYS-101 2014-2015 T6","8843c3c67d757c28ed3b1ec7d0da16f6" +"2014-2015","1",4.56,5,"MA",4,58,10,1,63,0,0,"PHYS-101",NA,NA,"PHYS","PHYS-101 2014-2015 T6","6e5d8251f68a02c0ccb824f1a3cb2838" +"2014-2015","1",2.93,1.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.7,"LO","PHYS","PHYS-101 2014-2015 T6","6e633ce2a03434ec9b127bd2afaecaa3" +"2014-2015","1",4.29,3.5,"MA",47,58,80,12,63,20,0,"PHYS-101",0.8445,"HI","PHYS","PHYS-101 2014-2015 T6","115338a0ac06ade64a48931a591d96e0" +"2014-2015","1",4.45,4.5,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.839,"HI","PHYS","PHYS-101 2014-2015 T6","b3be59b0f85a7d40e78bccd06f8f9867" +"2014-2015","1",3.91,3,"MA",NA,NA,NA,NA,NA,NA,-Inf,"PHYS-101",0.8485,"HI","PHYS","PHYS-101 2014-2015 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