![](\"resources/figure0.jpg\")
\n",
"![](\"resources/figure1.jpg\")
\n",
"\n",
"\n",
"\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Calculate yearly stats across movies here" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Write your code to make plot with errorbars here" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Write your code to make filled plot here" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Write your interpretation here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Creating panels\n", "\n", "- Next, we want to understand how long the blockbusters are in the different genres. When working with a large number of categories (we have 16 genres in the movies dataset), the visualizations easily become cluttered, and it becomes useful to make panels with small subplots.\n", "\n", "- Make a 4x4 panel for the 16 genres. In each subplot, show a histogram with the distribution of movie lengths for different categories. Below, find a skets of how this visualization could look like. Note the same x- and y-scale that allows comparison between categories!\n", "\n", "- Hints: use Matplotlib subplots function to initialize the subplotting axes.\n", "\n", "- What genres do the longest movies belong too? What can you say about the length of Comedy and animation movies?\n", "\n", "- How could we assign different color to each subplot (each genre)? What type of palette would be the best choice? Make a variation of the visualzation where each genre is visualized in a different color.\n", "\n", "
![](\"resources/figure3.jpg\")
"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Calculate stats by genre here"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your code to make 4x4 panel here"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your interpretation here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.3 Heatmaps\n",
"\n",
"- What studios make blockbusters in which genre? \n",
"\n",
"- There are many studios and many genres. When working with two variables that both have many different categories, there is often no space for representing each combination of two categories with a detailed plot (such as barplot of boxplot). It becomes useful to represent each stratum with one number, indicated with color in a heatmap. Such visualizations are useful to quickly understand the data.\n",
"\n",
"- Visualize the number of blockbusters made by each studio, of each genre. You will find below a rough sketch. Hint: use Seaborn heatmap function. \n",
"\n",
"- Finally, with a heatmap we can represent three variables, by coding a third variable with color. Try to understand what is the relationship between genres by understanding the co-occurence patterns between the main and secondary genre. What is the average worldwide gross revenue for movies with different combinations of main and second genre?\n",
"\n",
"- You should be able to easily answer the following questions from the two visualizations: What genres do the majority of movies made by Warner Bros studio fall into? What is the most successful genre of Walt Disney pictures movies? What studio made the largest number of Romance blockbusters? Which combination of genres brings the highest revenue?\n",
"\n",
"\n",
"\n",
"![](\"resources/figure4.jpg\")
"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your code to make the first heatmap here"
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your code to make the second heatmap here"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your interpretations here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.4 Be a show-off\n",
"\n",
"Finally, we did not explore the impact of the rank of the movie among the top ten. How is the rank among the top ten related to the genres? What kind of visualization can be suitable to understand this better? To practice your data viz skills, sketch a potential visualization on a piece of paper, and then implement it using the appropriate type of visualization."
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your dataviz show-off code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 3: Visualizing skewed distributions\n",
"\n",
"__3.A__ Familiarize yourself with the \"pop_per_commune\" dataframe, that contains information about each commune, and its population. Plot a histogram of the population counts (population_Dec) and explain your observations. Do not use a log-scale plot for now. What does this histogram tell you about urban and rural communes in Switzerland? Are there any clear outliers on either side, and if so, which communes?"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"__3.B__ The figure below represents 4 types of histogram. At this stage, your distribution should look like Fig.(a). A common way to represent [power-laws](https://en.wikipedia.org/wiki/Power_law) is to use a histogram using a log-log scale -- remember: the x-axis of an histogram is segmented in bins of equal sizes and y-values are the average of each bin. As shown in Fig.(b), small bins sizes might introduce artifacts. Fig.(b) and Fig.(c) are examples of histograms with two different bin sizes. Another great way to visualize such distribution is to use a cumulative representation, as shown in Fig.(d), in which the y-axis represents the number of data points with values greater than y. Create the figures (b) and (d) using the data extracted for task 1A. For Fig.(b), represent two histograms using two different bin sizes and provide a brief description of the results. What does this tell you about the relationship between the two variables, namely the frequency of each bin and the value (i.e. population in case of the communal data) for each bin?\n",
"\n",
"![](\"resources/plaw.png\")
\n",
" \n",
"The figure is extracted from [this paper](https://arxiv.org/pdf/cond-mat/0412004.pdf) that contains more information about this family of distributions."
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your code here"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Write your interpretations here"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
diff --git a/examples/ExercisesC-Becoming a DataVizard solution.ipynb b/examples/ExercisesC-Becoming a DataVizard solution.ipynb
index f55d3c6..5bb5a09 100644
--- a/examples/ExercisesC-Becoming a DataVizard solution.ipynb
+++ b/examples/ExercisesC-Becoming a DataVizard solution.ipynb
@@ -1,1034 +1,692 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" Example extracted from the ADA 2022 repository by Robert West, more on: https://github.com/epfl-ada/2022/.\n",
"
\n",
"\n",
"## Becoming a DataVizard exercise \n",
"\n",
"This is an exercise about data visualization, welcome! You will use common python libraries, Matplotlib and Seaborn to create effective and clear visualizations. You will work with two datasets: a dataset containing information about the top ten grossing movies in each year, and a dataset with information about the population in Swiss communes. \n",
"\n",
"These are the visualizations we will make, and that you will use to solve the exercise:\n",
"- [Histogram](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.hist.html)\n",
"- [Boxplot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.boxplot.html)\n",
"- [Scatterplot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html)\n",
"- [Plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html)\n",
"- [Jointplot](https://seaborn.pydata.org/generated/seaborn.jointplot.html)\n",
"- [Barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html)\n",
"- [Violinplot](https://seaborn.pydata.org/generated/seaborn.violinplot.html)\n",
"- [Errorbar](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.errorbar.html)\n",
"- [Filled plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.fill_between.html)\n",
"- [Panels](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html)\n",
"- [Heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html)\n",
"- [Log-log](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.loglog.html)\n",
"\n",
"In the linked documentation, you will find complete information, and a lot more details compared to what is outlined in this notebook. We will make simple visualizations, but you will notice that all of the visualizations consider many optional parameters. You are encouraged to explore them, and modify them to make the most efficient visualizations, and match your desired style.\n",
"\n",
"Beyond Matplotlib and Seaborn, there are many more resources that might be useful for you, for example:\n",
"- [ggplot](http://ggplot.yhathq.com/)\n",
"- [plotly](https://plotly.com/python/)\n",
"- [Geopy](https://geopy.readthedocs.io/en/stable/) and [Folium](https://python-visualization.github.io/folium/)\n",
"\n",
"Create your custom signature colorpalettes:\n",
"- [coolors](https://coolors.co/)"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Let's load necessary libraries and the datasets\n",
"# \"pip install seaborn\" if necessary\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"import math\n",
"\n",
"data_folder = './resources/'\n",
"\n",
"\n",
"movies = pd.read_csv(data_folder+'blockbusters.csv')\n",
"movies['worldwide_gross'] = movies['worldwide_gross'].str.replace('$', '', regex=False).str.replace(',', '', regex=False).astype(float)\n",
"\n",
"commune_pop = pd.read_excel(data_folder+'communes_pop.xls',skiprows=5,skipfooter=7,\n",
" sheet_name='2017',names=['commune','population_Jan',\n",
" 'birth_alive','death','natrual_increase',\n",
" 'arrivals','departure','migratory_balance',\n",
" 'divergence_statistic','population_Dec','variation_num',\n",
" 'variation_ratio'])\n",
"\n",
"pop_per_commune = commune_pop[[name.startswith('......') for name in commune_pop.commune]].copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Tutorial: Using data visualization libraries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use the blockbusters dataset. Summary of the features:\n",
"- *Main_Genre*: the primary genre of the movie\n",
"- *Genre_2*\n",
"- *Genre_3*\n",
"- *imdb_rating*\n",
"- *length*: movie length in minutes\n",
"- *rank_in_year*: rank among the top 10 movies released that year\n",
"- *rating*: audience rating\n",
"- *studio*: the studio that produced the movie\n",
"- *title*: movie title\n",
"- *worldwide_gross*: total worldwide revenue in dollars\n",
"- *year*: year of release\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing a single variable\n",
"\n",
"- First we will see how to visualize a single variable using matplotlib library. Let's focus on \"Worldwide gross revenue\" column of the dataframe and inspect the distribution, using histogram and boxplot.\n",
"- Notice the use of plt.xlabel(), plt.ylabel(), plt.title() - every visualization should have axis labeled, and a title!\n",
"- plt.hist() takes a couple of important arguments: bins - the number of bins, and range - the range of values considered in the histogram."
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "\n",
"\n",
"\n",
"\n",
"\n" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Calculate yearly stats across movies here\n", "\n", "stats_by_year = movies.groupby(movies['year']).apply(lambda x: pd.Series({\n", " 'average_worldwide_gross': x['worldwide_gross'].mean(),\n", " 'std_dev_worldwide_gross': x['worldwide_gross'].std()\n", " }))" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Worldwide gross revenue')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
"
]
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Calculate stats by genre here\n",
"\n",
"stats_by_genre = movies.groupby('Main_Genre').apply(lambda x: pd.Series({\n",
" 'length': x['length'].values\n",
" }))"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.6, 'Number of movies')"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
]
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\n",
" \n",
"The figure is extracted from [this paper](https://arxiv.org/pdf/cond-mat/0412004.pdf) that contains more information about this family of distributions."
]
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "\n",
" Example extracted from the Signal Processing repository (COM303) by Paolo Prandoni, more on: https://github.com/prandoni/COM303.\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hearing the phase of a sound\n",
"\n",
"In this notebook we will investigate the effect of phase on the perceptual quality of a sound. It is often said that the human ear is largely insensitive to phase and that's why most of the equalization in commercial-grade audio equipment takes place in the magnitude domain only.\n",
"\n",
"But is it really so? Let's find out."
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import IPython\n",
"from scipy.io import wavfile"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams[\"figure.figsize\"] = (14,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be synthesizing audio clips so let's set the sampling rate for the rest of the notebook:"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Fs = 16000 # sampling freqency\n",
"TWOPI = 2 * np.pi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be synthesizing and playing audio clips so let's define a convenience function to \"beautify\" the resulting sound: basically, we want a gentle fade-in and fade-out to avoid abrupt \"clicks\" when the waveform begins and ends. \n",
"\n",
"Also, there is a \"bug\" in the current version of IPython whereby audio data is forcibly normalized prior to playing (see [here](https://github.com/ipython/ipython/issues/8608) for details; this may have been solved in the meantime). On the other hand, we want to avoid normalization in order to keep control over the volume of the sound. A way to do so is to make sure that all audio clips have at least one sample at a pre-defined maximum value, and this value is the same for all clips. To do so we add a slow \"tail\" to the data which will not result in an audible sound but will set a common maximum value in all clips."
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def prepare(x, max_value = 3):\n",
" N = len(x)\n",
" # fade-in and fade-out times max 0.2 seconds\n",
" tf = min(int(0.2 * Fs), int(0.1 * N))\n",
" for n in range(0, int(tf)):\n",
" s = float(n) / float(tf)\n",
" x[n] *= s\n",
" x[N-n-1] *= s\n",
" # let's append an anti-normalization tail; drawback is one second of silence in the end\n",
" x = np.concatenate((x, np.linspace(0, max_value, int(Fs/2)), np.linspace(max_value, 0, int(Fs/2))))\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1) Sustained sounds\n",
"\n",
"The first experiment will use sustained sounds, i.e. waveform whose global envelope does not change in time. A periodic sustained waveform is simply the sum of harmonically-related sinusoidal components, i.e. a set of sines or cosines with different amplitudes and phases at frequencies multiple of a fundamental. The fundamental frequency is also known as the pitch of the sound.\n",
"\n",
"A class of instruments that produce good sustained sounds is the woodwinds (clarinet, saxophone, etc). The mechanics of sound generation in woodwinds are beyond the scope of this notebook and plenty of references can be found on the internet. For our purposes we will choose to simulate a clarinet according to the analysis described [here](http://www.phy.mtu.edu/~suits/clarinet.html).\n",
"\n",
"\n",
"\n",
"A clarinet-like sustained sound will contain frequencies at odd multiples of the fundamental. We will just keep the fundamental and five harmonics and we be able to specify the initial phase offset for each component:"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def clarinet(f, phase = []):\n",
" # length in seconds of audio clips\n",
" T = 3\n",
" \n",
" # we will keep 5 harmonics and the fundamental\n",
" # amplitude of components: \n",
" ha = [0.75, 0.5, 0.14, 0.5, 0.12, 0.17]\n",
" \n",
" # phase\n",
" phase = np.concatenate((phase, np.zeros(len(ha)-len(phase))))\n",
"\n",
" x = np.zeros((T * Fs))\n",
" # clarinet has only odd harmonics\n",
" n = np.arange(len(x))\n",
" for k, h in enumerate(ha):\n",
" x += h * np.sin(phase[k] + TWOPI * (2*k + 1) * (float(f)/Fs) * n)\n",
" return x"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "\n",
" Example extracted from the How People Learn repository by Cécile Hardebolle and Roland Tormey, more on: https://c4science.ch/source/HPLnotebooks/.\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to hypothesis testing\n",
"\n",
"In this notebook we look at data on a type of flower called Iris and we analyze it using a programming language called Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning goals\n",
"\n",
"This notebook is designed for you to learn:\n",
"* How to distinguish between \"population\" datasets and \"sample\" datasets when dealing with experimental data\n",
"* How to compare a sample to a population using a statistical test called the \"t-test\" and interpret its results\n",
"* How to evaluate the magnitude of the difference between a sample and a population using a measure of the effect size called \"Cohen's d\" and interpret the results\n",
"* How to use Python scripts to make statistical analyses on a dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"\n",
" ![\"iris](\"figs/iris-picture.jpg\")
\n",
"\n",
"###### Iris virginica (Credit: Frank Mayfield CC BY-SA 2.0)\n",
"\n",
"
\n",
"\n",
"In 1935, an american botanist called Edgar Anderson worked on quantifying the morphologic variation of Iris flowers of three related species, Iris setosa, Iris virginica and Iris versicolor [[1]](#Bibliography). He realized a series of measures of the petal length, petal width, sepal length, sepal width and species.\n",
"Based on the combination of these four features, a British statistician and biologist named Ronald Fisher developed a model to distinguish the species from each other [[2]](#Bibliography).\n",
"\n",
"\n",
"### Question\n",
"A recent series of measurements has been carried out at the [Iris Garden of the Vullierens Castle](https://chateauvullierens.ch/en/) near Lausanne, on a sample of $n=50$ flowers of the Iris virginica species. \n",
"**How similar (or different) is the Iris sample from the Vullierens Castle compared to the Iris virginica population documented by Edgar Anderson?**\n",
"\n",
"\n",
"### Instructions \n",
"\n",
"**1. Read the notebook and execute the code cells**. \n",
"To check your understanding, ask yourself the following questions:\n",
"* Can I explain how the concept of sample differs from the concept of population?\n",
"* What does a t-test tell me on my sample?\n",
"* What does Cohen's d tell me on my sample?\n",
"\n",
"**2. Do the exercise** at the end of the notebook. \n",
"\n",
" \n",
"\n",
"--- \n",
"\n",
"## Getting started\n",
"\n",
"### Python tools for stats\n",
"Python comes with a number of libraries for processing data and computing statistics.\n",
"To use these tool you first have to load them using the `import` keyword. \n",
"The role of the code cell just below is to load the tools that we use in the rest of the notebook. It is important to execute this cell *prior to executing any other cell in the notebook*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plotting and display tools\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt \n",
"plt.style.use('seaborn-whitegrid') # global style for plotting\n",
"\n",
"from IPython.display import display, set_matplotlib_formats\n",
"set_matplotlib_formats('svg') # vector format for graphs\n",
"\n",
"# data computation tools\n",
"import numpy as np \n",
"import pandas as pan\n",
"import math\n",
"\n",
"# statistics tools\n",
"import scipy.stats as stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data available on the Anderson dataset\n",
"\n",
"Anderson has published summary statistics of his dataset. \n",
"You have the mean petal length of the Iris virginica species: $\\mu = 5.552$ cm."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define mu as mean petal length of iris virginica species from Anderson\n",
"mu = 5.552\n",
"\n",
"# Display mu\n",
"mu"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data available on the Vullierens sample\n",
"\n",
"You have the raw data collected on the petal length of the Vullierens sample, which is stored in the CSV file `iris-sample1-vullierens.csv` in the resource folder accessible through the file explorer in the left pane. \n",
"If you double click on the file it will open in a new tab and you can look at what is inside.\n",
"\n",
"Now to analyze the data using Python you have to read the file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Read the Vullierens sample data from the CSV file\n",
"sample_data = pan.read_csv('resources/iris-sample1-vullierens.csv')\n",
"\n",
"# Display the first few lines of the dataset\n",
"sample_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After reading the CSV file, its content is stored in the variable `sample_data`, which is a kind of table. Each line of the table is given an index number to identify it. We see above that, appart from the index, the table contains only one column, called `\"petal_length\"`, which contains all the measurements made on the Vullierens Irises. To get the list of all the values stored in that column, you can use the following syntax: `sample_data[\"petal_length\"]`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# All values stored in the \"petal_length\" column of the \"sample_data\" table\n",
"sample_data[\"petal_length\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First look at the data\n",
"\n",
"### Descriptive statistics\n",
"\n",
"Let's compute some simple descriptive statistics on this sample data. The `describe()` function gives us right away a number of useful descriptive stats:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Compute the descriptive stats\n",
"sample_stats = sample_data.describe()\n",
"\n",
"# Display the result\n",
"sample_stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can access individual elements of the `sample_stats` table using the corresponding names for the line and column of the value. \n",
"The following cell illustrates how to get the mean value of the petal length in the sample:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extract the sample mean from the descriptive stats\n",
"sample_mean = sample_stats.loc[\"mean\",\"petal_length\"]\n",
"\n",
"# Display the result\n",
"sample_mean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizations\n",
"\n",
"Now let's make some simple visualisations of this data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create visualisation\n",
"fig = plt.figure(figsize=(16, 4))\n",
"\n",
"# Plot the sample values\n",
"ax1 = plt.subplot(131)\n",
"ax1.set_xlabel('index of sample')\n",
"ax1.set_ylabel('petal length')\n",
"ax1.set_title(\"Samples\")\n",
"ax1.plot(sample_data[\"petal_length\"], 'go')\n",
"# Add the means\n",
"ax1.axhline(y=sample_mean, color='black', linestyle='-.', linewidth=1, label=\"sample mean $m$\")\n",
"ax1.axhline(y=mu, color='black', linestyle=':', linewidth=1, label=\"$\\mu$\")\n",
"ax1.legend()\n",
"\n",
"# Plot the distribution of the samples\n",
"ax2 = plt.subplot(132)\n",
"ax2.set_xlabel('petal length')\n",
"ax2.set_ylabel('number of samples')\n",
"ax2.set_title(\"Distribution\")\n",
"ax2.hist(sample_data[\"petal_length\"], color='green')\n",
"# Add the means\n",
"ax2.axvline(x=sample_mean, color='black', linestyle='-.', linewidth=1, label=\"sample mean $m$\")\n",
"ax2.axvline(x=mu, color='black', linestyle=':', linewidth=1, label=\"$\\mu$\")\n",
"ax2.legend()\n",
"\n",
"# Box plot with quartiles\n",
"ax3 = plt.subplot(133, sharey = ax1)\n",
"box = ax3.boxplot(sample_data[\"petal_length\"], sym='k+', patch_artist=True)\n",
"ax3.set_ylabel('petal length')\n",
"ax3.set_title(\"Quartiles\")\n",
"ax3.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
"plt.setp(box['medians'], color='black')\n",
"for patch in box['boxes']:\n",
" patch.set(facecolor='green')\n",
"# Add the means\n",
"ax3.axhline(y=sample_mean, color='black', linestyle='-.', linewidth=1, label=\"sample mean $m$\")\n",
"ax3.axhline(y=mu, color='black', linestyle=':', linewidth=1, label=\"$\\mu$\")\n",
"ax3.legend()\n",
"\n",
"# Display the graph\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, the Python code necessary to generate the graphs above is quite long. Hiding this particular code cell can improve the readability of the notebook. To hide it, select it and then click on the blue bar at its left. If you want to make the code visible again, simply click on the three \"dots\" or on the blue bar.\n",
"\n",
"\n",
"### Interpretation and hypothesis\n",
"\n",
"The simple analyses we have made so far allow us to have a preliminary idea about how the Irises from Vullierens compare to those observed by Anderson. One feature to look at for the comparison is their respective mean petal length. We see above that the mean petal length $m$ of the Vullierens sample is quite close to the mean $\\mu$ reported by Anderson.\n",
"\n",
"Let's formulate this as an **hypothesis** which we state as: the sample mean $m$ is similar to the mean of the reference population $\\mu$, i.e. $m = \\mu$. This hypothesis is noted $H_0$ and called the \"null\" hypothesis because it states that there is no difference between the sample and the population. \n",
"The \"alternate\" hypothesis $H_a$ is that the sample mean is not similar to the mean of the reference population, $m \\neq \\mu$.\n",
"\n",
"How can we test our hypothesis? In the following, we use a **statistical test** to answer this question."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
"---\n",
"\n",
"## Testing our hypothesis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In our hypothesis we compare the mean of one sample to a reference value. To test this hypothesis we can use a statistical test called a **one-sample t-test**. \n",
"\n",
"But what does it mean when we test the hypothesis that a sample mean is potentially equal to a given value? \n",
"\n",
"### Sample versus population\n",
"\n",
"\n",
"\n",
"###### Iris virginica (Credit: Frank Mayfield CC BY-SA 2.0)\n",
"\n",
"\n",
"![](\"figs/diagram-samples.png\")
\n",
"![](\"figs/diagram-normalcurveAB.png\")
\n",
"![](\"figs/diagram-normalcurveAlpha.png\")
\n",
"
\n",
"\n",
"To understand this, it is useful to start by thinking about a population, in this case our population of Irises which has a mean petal length of $\\mu = 5.552$ cm.\n",
"\n",
"Now imagine you take a sample of, say, 50 flowers from this population. The mean petal length of this sample is $m_1 = 6.234$ cm. You then take a second sample of 50, which ends up having a mean petal length of $m_2 = 5.874$ cm. You then take a third sample of 50 which gives you a mean petal length of $m_3 = 5.349$ cm.\n",
"\n",
"If you keep taking samples from this population, you will start to notice a pattern: while some of the samples will give a mean average length which is not at all close to the population mean, most of the mean petal lengths are reasonably close to the population mean of 5.552 cm. Furthermore, the mean average of the mean average of the samples will be the same as that of the population as a whole i.e. 5.552 cm. \n",
"\n",
"In fact, if we keep taking samples from this population, it turns out that the distribution of the average of these samples will take a very particular pattern that looks like a normal curve. In fact if you take bigger sample sizes (say 130 instead of 50) the distribution will get closer and closer to being a normal curve for which the mean average is equal to the mean average of the population. For these smaller samples, the distribution is called the **[Student's t-distribution](https://en.wikipedia.org/wiki/Student%27s_t-distribution)** (actually it is a family of distributions, which depend on the sample size).\n",
"\n",
"\n",
"This is useful because it allows us to rephrase our question as to how similar or different our sample from Vullierens Castle is to the population of Irises as described by Edgar Anderson. \n",
"**What we have from the Vullierens Castle is a sample**. We want to know if it is a sample that might have come from a population like that described by Edgar Anderson. We now know the shape (more or less a normal distribution) and the mean (5.552 cm) of all of the samples that could be taken from the population described by Edgar Anderson. **So our question becomes \"where does our sample fall on the distribution of all such sample means?\"**. \n",
"If our mean is in position A on the figure on the right, then it is plausible that our sample came from a population like that of Edgar Anderson. If our mean is in position B, then it is less plausible to believe that our sample came from a population like Anderson’s.\n",
"\n",
"### Cut-off point\n",
"\n",
"You might be wondering, how far away is far enough away for us to think it is implausible that our sample comes from a population like Anderson’s. The answer is, it depends on how sure you want to be. \n",
"\n",
"One common answer to this question is to be 95% sure - meaning that a sample mean would need to be in the most extreme 5% of cases before we would think it is implausible that our sample comes from a population like Anderson’s ($\\alpha=0.05$). These most extreme 5% cases are represented by the zones in light blue on the figure on the right. If the sample mean falls into these most extreme zones, we say that *the difference is \"statistically significant\"*.\n",
"\n",
"A second, common answer is 99% sure meaning that a sample mean would need to be in the most extreme 1% of cases before we would think it is implausible that our sample comes from a population like Anderson’s ($\\alpha=0.01$). \n",
"\n",
"In the following, **we will work on the basis of being 95% sure**. Let's define our $\\alpha=0.05$:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define alpha\n",
"alpha = 0.05\n",
"\n",
"# Display alpha\n",
"alpha"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If our distribution of sample means is a normal curve then we know that the most extreme 5% of sample means are found above or below ±1.96 standard deviations above and below the mean. In our case, because our sample size is less than 130 (it is 50), our distribution is close to normal but not quite normal. \n",
"Still we can find out the relevant cut off point from looking it up in statistical tables: for a sample size of 50, the most extreme 5% of cases are found above or below 2.01 standard deviations from the mean. Let's define our cutoff point:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define the cutoff point\n",
"cutoff = 2.01\n",
"\n",
"# Display cutoff\n",
"cutoff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Error in the distribution of means\n",
"\n",
"So far we know a lot that will help us to test the hypothesis that our sample mean is similar to Anderson’s population mean. We know:\n",
"* Our sample mean $m$\n",
"* The population mean $\\mu$\n",
"* The shape of the distribution of the mean of all samples that would come from this population (a normal curve, centred on the population mean)\n",
"* Our cut off point defined by $\\alpha$ (the most extreme 5% of cases, above or below 2.01 standard deviations from the mean)\n",
"\n",
"The last piece of information missing that would enable us to test this hypothesis is the size of the standard deviation of the distribution of sample means from Anderson’s population. \n",
"It turns out that a good guess for the size of this standard deviation can be obtained from knowing the standard deviation of our sample.\n",
"If $s$ is the sample standard deviation of our sample and $n$ is the sample size, then the standard deviation of the distribution of sample means is:\n",
"\n",
"$\n",
"\\begin{align}\n",
"\\sigma_{\\overline{X}} = \\frac{s}{\\sqrt{n}}\n",
"\\end{align}\n",
"$ \n",
"\n",
"This standard deviation of the distribution of sample means is called the \"standard error of the mean\" (also noted SEM). \n",
"We can compute it by retrieving the sample size and standard deviation from the descriptive stats we have computed earlier: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extract the sample size from the descriptive stats generated earlier\n",
"sample_size = sample_stats.loc[\"count\",\"petal_length\"]\n",
"\n",
"# Extract the sample standard deviation from the descriptive stats\n",
"sample_std = sample_stats.loc[\"std\",\"petal_length\"]\n",
"\n",
"# Compute the estimation of the standard deviation of sample means from Anderson's population (standard error)\n",
"sem = sample_std / math.sqrt(sample_size)\n",
"\n",
"# Display the standard error\n",
"sem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now restate our question in more precise terms: **\"is our sample mean in the most extreme 5% of samples that would be drawn from a population with the same mean as Anderson’s population?\"**. \n",
"Or to be even more precise, **\"is the gap between our sample mean and Anderson’s population mean greater than 2.01 times the standard error?\"**. \n",
"\n",
"This would be equivalent to compare\n",
"$\n",
"\\begin{align}\n",
"\\frac{m - \\mu}{\\sigma_{\\overline{X}}}\n",
"\\end{align}\n",
"$\n",
"to our cutoff of 2.01. That is the definition of the **t** statistics: the value $t = $\n",
"$\n",
"\\begin{align}\n",
"\\frac{m - \\mu}{\\sigma_{\\overline{X}}}\n",
"\\end{align}\n",
"$ \n",
" has to be compared to the cutoff point we have chosen to determine if the sample mean falls into the most extreme zones and to be able to say whether the difference is statistically significant or not.\n",
"Let's compute $t$:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Compute the t statistics:\n",
"t = (sample_mean - mu) / sem\n",
"\n",
"# Display t\n",
"print(t)\n",
"\n",
"# Compare t with our cutoff point\n",
"if t > cutoff: \n",
" print(\"The difference is statistically significant.\")\n",
"else: \n",
" print(\"The difference is not statistically significant.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see in the results above that for our Vullierens sample $t < 2.01$, therefore the difference between the two means is not greater than 2.01 times the standard error. In other words, our sample mean **is not in the most extremes 5%** of samples that would be drawn from a population with the same mean as Anderson's population. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conclusion\n",
"\n",
"What can we conclude from there? What the one sample t-test tells us is that we don't have evidence which would lead us to think that the sample doesn't come from an Anderson like population. Therefore we **cannot reject our hypothesis $H_0$**. However this is not the same to say that *it is* the same as the Anderson population. This is one of the limits of the t-test: like many other statistical tests, **it can be used only to reject an hypothesis**, not to confirm it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
"---\n",
"\n",
"## Testing our hypothesis using a predefined Python function\n",
"\n",
"So far we have made the computations by hand but Python comes with a number of libraries with interesting statistical tools. \n",
"In particular, the `stats` library includes a function for doing a **one-sample t-test** as we have done above. \n",
"\n",
"### Computation of the test\n",
"\n",
"Let's now use it and then look at what information it gives us."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Compute the t-test\n",
"t, p = stats.ttest_1samp(sample_data[\"petal_length\"], mu)\n",
"\n",
"# Display the result\n",
"print(\"t = {:.3f}\".format(t))\n",
"print(\"p = {:.3f}\".format(p))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function returns two values `t` and `p` which say the same thing but in two different ways:\n",
"* `t` tells us where our sample mean falls on the distribution of all the possible sample means for the Anderson population ; `t` has to be compared to the `cutoff` value (2.01) to know if our sample mean is in the most extremes 5%.\n",
"* `p` (called the \"p-value\") is the probability to get a more extreme sample mean than the one we observe ; `p` has to be compared to `alpha` (0.05) to know if our sample mean is in the most extremes 5%.\n",
"\n",
"### Interpretation of the results\n",
"\n",
"We see above that `t < cutoff`, which means that the difference between the two means is smaller than 2.01 times the standard error and `p > alpha`, which means that the probability of getting more extreme sample mean than the one we observe is higher than 5% so it cannot be considered as one of the extreme possible values. Because they convey the same information, you can use either `t` or `p` to interpret the result of the t-test. In practice, most people use the p-value. \n",
"\n",
"As expected from the calculations we have made above, the test confirms that the difference between the mean petal length of the Vullierens sample and the mean petal length of Anderson's population is **not statistically significant**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualization\n",
"\n",
"Using Python we can visualize what that means graphically by plotting the t-distribution of all the possible sample means that would be drawn from a population with the same mean as Anderson's population and showing where `t` is in the distribution compared to the zone defined by our $\\alpha$ of 5%:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create the t-test visualization\n",
"fig, ax = plt.subplots(figsize=(12, 4))\n",
"ax.set_title(\"Probability distribution of all possible sample means if $H_0$ is true\")\n",
"\n",
"# Let's plot the T distribution for this sample size\n",
"tdist = stats.t(df=sample_size, loc=0, scale=1)\n",
"x = np.linspace(tdist.ppf(0.0001), tdist.ppf(0.9999), 100)\n",
"y = tdist.pdf(x) \n",
"ax.plot(x, y, color='blue', linewidth=1)\n",
"\n",
"# Polish the look of the graph\n",
"ax.get_yaxis().set_visible(False) # hide the y axis\n",
"ax.set_ylim(bottom=0) \n",
"ax.grid(False) # hide the grid \n",
"ax.spines['top'].set_visible(False) # hide the frame except bottom line\n",
"ax.spines['right'].set_visible(False)\n",
"ax.spines['left'].set_visible(False)\n",
"\n",
"# Plot the rejection zone two tailed\n",
"x_zone_1 = np.linspace(tdist.ppf(0.0001), tdist.ppf(alpha/2), 100)\n",
"x_zone_2 = np.linspace(tdist.ppf(1-alpha/2), tdist.ppf(0.9999), 100)\n",
"y_zone_1 = tdist.pdf(x_zone_1) \n",
"y_zone_2 = tdist.pdf(x_zone_2) \n",
"ax.fill_between(x_zone_1, y_zone_1, 0, alpha=0.3, color='blue', label = r'rejection of $H_0$ with $\\alpha={}$'.format(alpha))\n",
"ax.fill_between(x_zone_2, y_zone_2, 0, alpha=0.3, color='blue')\n",
"\n",
"# Plot the t-test stats\n",
"ax.axvline(x=t, color='green', linestyle='dashed', linewidth=1)\n",
"ax.annotate('t-test $t$={:.3f}'.format(t), xy=(t, 0), xytext=(-10, 130), textcoords='offset points', bbox=dict(boxstyle=\"round\", facecolor = \"white\", edgecolor = \"green\", alpha = 0.8))\n",
"\n",
"# Plot the p-value\n",
"if t >= 0: x_t = np.linspace(t, tdist.ppf(0.9999), 100)\n",
"else: x_t = np.linspace(tdist.ppf(0.0001), t, 100)\n",
"y_t = tdist.pdf(x_t) \n",
"ax.fill_between(x_t, y_t, 0, facecolor=\"none\", edgecolor=\"green\", hatch=\"///\", linewidth=0.0, label = r'p-value $p$={:.3f}'.format(p))\n",
"\n",
"# Add a legend\n",
"ax.legend()\n",
"\n",
"# Display the graph\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
"---\n",
"\n",
"## Evaluating the magnitude of the difference between two means: effect size\n",
"\n",
"So far we have tested our hypothesis regarding the similarity of means between our sample and the population and the results show us that there is probably no difference.\n",
"However whether this statistical test finds a difference depends on the sample size: with small samples it is harder to find a statistically significant difference.\n",
"We need therefore to **distinguish between a difference being statistically significant and a difference being large**.\n",
"The t-test is used to assess whether the difference is statistically significant. To assess whether the difference is large we use a measure called the effect size.\n",
"\n",
"### Computation of the effect size\n",
"\n",
"The effect size represents the size of the difference between the sample mean and the population mean taking into account the variation inside the sample (and inside the population, if known). \n",
"Cohen's d is one of the existing measures of effect size. \n",
"With $m$ and $s$ respectively the mean and standard deviation of the sample and $\\mu$ the population mean, Cohen's d for one sample (when the standard deviation of the population $\\sigma$ is not known) can be calculated as follows: \n",
"\n",
"$\n",
"\\begin{align}\n",
"d = \\frac{m - \\mu}{s}\n",
"\\end{align}\n",
"$\n",
"\n",
"Let's compute the effect size of the difference between the Vullierens sample and Anderson's population:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Compute cohen's d\n",
"d = (sample_mean - mu)/sample_std\n",
"\n",
"# Display the result\n",
"print(\"d = {:.3f}\".format(d))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interpretation of the results\n",
"\n",
"To interpret this result we have to **compare it to thresholds from the litterature**. Cohen suggested that $d=0.2$ was a small effect size, $0.5$ represents a medium effect size and $0.8$ represents a large effect size.\n",
"For our Vullierens sample the effect size is therefore small. In this case, the difference is also not statistically significant. However, with a larger sample size, it would be possible to have a statistically significant difference which nonetheless would be so small as to be trivial.\n",
"\n",
"\n",
"### Visualization\n",
"\n",
"Let's visualize the result graphically (again, you can hide the lengthy code by clicking on the blue bar at its left). \n",
"The graph below represents the theoretical distributions of the population in blue and of the sample in green. We see below that the two groups largely overlap, which is representative of the size of the difference being trivial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create the vizualisation for the effect size\n",
"fig, ax = plt.subplots(figsize=(8, 4))\n",
"\n",
"# Plot the normal distribution\n",
"norm = stats.norm(loc=0, scale=1)\n",
"x = np.linspace(norm.ppf(0.0001), norm.ppf(0.9999), 100)\n",
"y = norm.pdf(x) \n",
"ax.plot(x, y, color='blue', linewidth=1)\n",
"ax.axvline(x=0, color='blue', linestyle='dashed', linewidth=1)\n",
"\n",
"# Plot the distribution of the sample\n",
"norm_d = stats.norm(loc=d, scale=1)\n",
"x_d = np.linspace(norm_d.ppf(0.0001), norm_d.ppf(0.9999), 100)\n",
"y_d = norm_d.pdf(x_d) \n",
"ax.plot(x_d, y_d, color='green', linewidth=1)\n",
"ax.axvline(x=d, color='green', linestyle='dashed', linewidth=1)\n",
"\n",
"# Display the value of Cohen's d\n",
"max_y = np.max(y)+.02\n",
"ax.plot([0,d], [max_y, max_y], color='red', linewidth=1, marker=\".\")\n",
"ax.annotate(\"effect size $d$={:.3f}\".format(d), xy=(d, max_y), xytext=(15, -5), textcoords='offset points', bbox=dict(boxstyle=\"round\", facecolor = \"white\", edgecolor = \"red\", alpha = 0.8))\n",
"\n",
"# Display the graph\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
"---\n",
"\n",
"## Summary\n",
"\n",
"To summarize, to compare the mean of a sample to a reference value from a population, you have to proceed in four main steps:\n",
"1. formulate the hypothese you want to test: the null hypothesis $H_0: m = \\mu$ and its alternate $H_a: m \\neq \\mu$ \n",
"1. choose a cut-off point for being sure, usually $\\alpha = .05$, $\\alpha = .01$ or $\\alpha = .001$ \n",
"1. compute the result of the t-test and interpret the result - in particular if the p-value is *below* the significance level you have chosen, $p \\lt \\alpha$, then it means $H_0$ should probably be rejected\n",
"1. compute the effect size and interpret the result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
"---\n",
"\n",
"## Exercise\n",
"\n",
"**A. Getting familiar with the code.**\n",
"1. In the code cells above, where is the t-test computed using the predefined Python function?\n",
"1. What are the two parameters that the t-test function takes as input?\n",
"1. If you wanted to change the population mean to a different value, like $\\mu = 5.4$ cm for instance, in which cell would you change it? \n",
"Then what would you need to do to update the result of the analysis in the notebook?\n",
"1. What is the result of the t-test if you compare the Vullierens sample to a population mean of $\\mu = 5.4$ cm? And what is the effect size?\n",
"\n",
"*Change the value of $\\mu$ back to 5.552 before working on the following questions.*\n",
"\n",
"**B. Analyzing another dataset.**\n",
"\n",
"A researcher from Tokyo sends you the results of a series of measurements she has done on the Irises of the [Meiji Jingu Imperial Gardens](http://www.meijijingu.or.jp/english/nature/2.html). The dataset can be found in the `iris-sample2-meiji.csv` file. \n",
"How similar (or different) is the Meiji sample compared to the Iris virginica population documented by Edgar Anderson? \n",
"The following questions are designed to guide you in analyzing this new dataset using this notebook.\n",
"\n",
"1. Which of the code cells above loads the data from the file containing the Vullierens dataset? Modify it to load the Meiji dataset.\n",
"1. Do you need to modify anything else in the code to analyze this new dataset?\n",
"1. What can you conclude about the Meiji sample from this analysis?\n",
"\n",
"**C. Going a bit further in the interpretation of the t-test.**\n",
"1. In the code cells above, where is the cut-off point $\\alpha$ defined? Change its value to 0.01 and re-execute the notebook. \n",
"1. How does this affect the result of the t-test for the Meiji sample?\n",
"\n",
" \n",
"\n",
"*A solution of the exercise is available [in this file](solution/TextbookC-Stats-solution.ipynb).*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" \n",
"\n",
"---\n",
"\n",
"\n",
"\n",
"\n",
"