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Created February 18, 2021 15:32
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RiskEngineering notebooks
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://risk-engineering.org/static/img/logo-RE.png\" width=\"100\" alt=\"\" style=\"float:right;margin:15px;\">\n",
"\n",
"This notebook is an element of the [risk-engineering.org courseware](https://risk-engineering.org/). It can be distributed under the terms of the [Creative Commons Attribution-ShareAlike licence](https://creativecommons.org/licenses/by-sa/4.0/).\n",
"\n",
"Author: Eric Marsden <eric.marsden@risk-engineering.org>.\n",
"\n",
"---\n",
"\n",
"In this notebook, we illustrate NumPy features for working with correlated data. Check the [associated lecture slides](https://risk-engineering.org/correlation/) for background material and to download this content as a Jupyter/Python notebook. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear correlation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats\n",
"plt.style.use(\"bmh\")\n",
"%config InlineBackend.figure_formats=[\"svg\"]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"array([10.61814828, 10.61858713, 11.02503195, 10.18986596, 8.10406197,\n",
" 9.27218839, 9.47226537, 8.64640933, 10.93993313, 10.41304313,\n",
" 8.71687128, 10.1883356 , 9.22934453, 9.7716287 , 12.05342612,\n",
" 10.08733778, 9.16652605, 10.75431406, 10.38715795, 10.42303995,\n",
" 11.70397999, 9.75270787, 10.99170077, 10.43607076, 8.66180318,\n",
" 9.32142711, 11.0306571 , 10.93337681, 8.12964922, 10.68861485,\n",
" 10.24360568, 9.30507986, 10.57635911, 10.14665815, 9.15653791,\n",
" 10.43979938, 11.30089661, 10.65416433, 10.06271296, 10.87321638,\n",
" 10.51109708, 11.73676068, 9.66098537, 10.69520895, 9.41620176,\n",
" 10.10832764, 10.70660736, 9.65190639, 11.50146507, 10.09314444,\n",
" 9.8138468 , 11.8076821 , 11.36838194, 9.86215247, 10.58355737,\n",
" 8.70286738, 9.37772093, 10.92874555, 10.50141347, 9.06867466,\n",
" 10.42441915, 11.72641845, 10.96159125, 9.93405464, 9.67882264,\n",
" 10.59938862, 9.95544504, 10.40102748, 10.92535406, 9.67835959,\n",
" 11.23838123, 10.03868956, 9.61583934, 10.28279346, 8.55438262,\n",
" 10.20655428, 10.07934489, 12.07327146, 11.01966265, 10.97065019,\n",
" 8.75390581, 9.33782475, 9.87747483, 7.64285205, 11.42649407,\n",
" 10.44455667, 9.27989088, 9.24808891, 10.21857193, 9.56918006,\n",
" 10.63528119, 10.16540621, 11.38722721, 11.03095646, 9.07277751,\n",
" 9.91687303, 10.9557358 , 9.80342103, 12.14026711, 10.51454634])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = numpy.random.normal(10, 1, 100)\n",
"X"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"Y = -X + numpy.random.normal(0, 1, 100)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
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"source": [
"plt.scatter(X, Y);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the scatterplot above, we can see that the random variables $X$ and $Y$ are *correlated*. There are various statistical measures that allow us to quantify the degree of linear correlation. The most commonly used is [Pearson’s product-moment correlation coefficient](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient). It is available in `scipy.stats`. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-0.7136727388061208, 7.877150437410328e-17)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
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"source": [
"scipy.stats.pearsonr(X, Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first return value is the linear correlation coefficient, a value between -1 and 1 which measures the strength of the linear correlation. A value greater than 0.9 indicates a strong positive linear correlation, and a value lower than -0.9 indicates strong negative linear correlation (when $X$ increases, $Y$ decreases).\n",
"\n",
"(The second return value is a *p-value*, which is a measure of the confidence which can be placed in the estimation of the correlation coefficient (smaller = more confidence). It tells you the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Here we have a very very low p-value, so high confidence in the estimated value of the correlation coefficient.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise**: show that when the error in $Y$ decreases, the correlation coefficient increases."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise**: produce data and a plot with a negative correlation coefficient."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Anscombe’s quartet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let’s examine four datasets produced by the statistician [Francis Anscombe](https://en.wikipedia.org/wiki/Frank_Anscombe) to illustrate the importance of exploring your data qualitatively (for example by plotting the data), rather than relying only on summary statistics such as the linear correlation coefficient."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"x = numpy.array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\n",
"y1 = numpy.array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\n",
"y2 = numpy.array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\n",
"y3 = numpy.array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\n",
"x4 = numpy.array([8,8,8,8,8,8,8,19,8,8,8])\n",
"y4 = numpy.array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])"
]
},
{
"cell_type": "code",
"execution_count": 7,
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 14,
"metadata": {},
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"source": [
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"source": [
"Notice that the linear correlation coefficient (Pearson's $r$) of the four datasets is identical, though clearly the relationship between $X$ and $Y$ is very different in each case! This illustrates the risks of depending only on quantitative descriptors to understand your datasets: you should also use different types of plots to give you a better overview of the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Datasaurus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The [Datasaurus](http://www.thefunctionalart.com/2016/08/download-datasaurus-never-trust-summary.html) provides another illustration of the importance of plotting your data to make sure it doesn't contain any surprises, rather than relying only on summary statistics. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
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" <th>count</th>\n",
" <td>142.000000</td>\n",
" <td>142.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>54.263273</td>\n",
" <td>47.832253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>16.765142</td>\n",
" <td>26.935403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>22.307700</td>\n",
" <td>2.948700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>44.102600</td>\n",
" <td>25.288450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
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" <th>75%</th>\n",
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" <tr>\n",
" <th>max</th>\n",
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"text/plain": [
" 0 1\n",
"count 142.000000 142.000000\n",
"mean 54.263273 47.832253\n",
"std 16.765142 26.935403\n",
"min 22.307700 2.948700\n",
"25% 44.102600 25.288450\n",
"50% 53.333300 46.025600\n",
"75% 64.743600 68.525675\n",
"max 98.205100 99.487200"
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],
"source": [
"import pandas\n",
"\n",
"ds = pandas.read_csv(\"https://risk-engineering.org/static/data/datasaurus.csv\", header=None)\n",
"ds.describe()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-0.06447185270095164, 0.44589659802470616)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.pearsonr(ds[0], ds[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These summary statistics don't look too nasty, but check out the data once it's plotted!"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
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"source": [
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[This article](https://www.autodeskresearch.com/publications/samestats) titled *Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing* runs a lot further with this concept."
]
},
{
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