{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation and Experimental Design\n", "> In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions. This is the Summary of lecture \"Introduction to Statistics in Python\", via datacamp.\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Datacamp, Statistics]\n", "- image: images/lmplot_wh.png" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation\n", "- Correlation coefficient\n", " - Quantifies the linear relationship between two variables\n", " - Number between -1 and 1\n", " - Magnitude corresponds to strength of relationship\n", " - Sign (+ or -) corresponds to direction of relationship\n", "- Pearson product-moment correlation($r$)\n", " - Most Common\n", " - $\\bar{x}$ = mean of $x$\n", " - $\\sigma_x$ = standard deviation of $x$\n", " \n", " $$ r = \\sum_{i=1}^{n} \\frac{(x_i - \\bar{x})(y_i - \\bar{y})}{\\sigma_x \\times \\sigma_y} $$\n", " \n", " - Variation\n", " - Kendall's Tau\n", " - Spearman's rho" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relationships between variables\n", "In this chapter, you'll be working with a dataset `world_happiness` containing results from the [2019 World Happiness Report](https://worldhappiness.report/ed/2019/). The report scores various countries based on how happy people in that country are. It also ranks each country on various societal aspects such as social support, freedom, corruption, and others. The dataset also includes the GDP per capita and life expectancy for each country.\n", "\n", "In this exercise, you'll examine the relationship between a country's life expectancy (`life_exp`) and happiness score (`happiness_score`) both visually and quantitatively." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrysocial_supportfreedomcorruptiongenerositygdp_per_caplife_exphappiness_score
1Finland2.05.04.047.04240081.8155
2Denmark4.06.03.022.04830081.0154
3Norway3.03.08.011.06630082.6153
4Iceland1.07.045.03.04790083.0152
5Netherlands15.019.012.07.05050081.8151
\n", "
" ], "text/plain": [ " country social_support freedom corruption generosity gdp_per_cap \\\n", "1 Finland 2.0 5.0 4.0 47.0 42400 \n", "2 Denmark 4.0 6.0 3.0 22.0 48300 \n", "3 Norway 3.0 3.0 8.0 11.0 66300 \n", "4 Iceland 1.0 7.0 45.0 3.0 47900 \n", "5 Netherlands 15.0 19.0 12.0 7.0 50500 \n", "\n", " life_exp happiness_score \n", "1 81.8 155 \n", "2 81.0 154 \n", "3 82.6 153 \n", "4 83.0 152 \n", "5 81.8 151 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "world_happiness = pd.read_csv('./dataset/world_happiness.csv', index_col=0)\n", "world_happiness.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a scatterplot of happiness_score vs. life_exp and show\n", "sns.scatterplot(x='life_exp', y='happiness_score', data=world_happiness);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create scatterplot of happiness_score vs. life_exp with trendline\n", "sns.lmplot(x='life_exp', y='happiness_score', data=world_happiness, ci=None);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7802249053272061\n" ] } ], "source": [ "# Correlation between life_exp and happiness_score\n", "cor = world_happiness['life_exp'].corr(world_happiness['happiness_score'])\n", "print(cor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation caveats\n", "- Correlation only accounts for linear relationships\n", "- Transformation\n", " - Certain statistical methods rely on variables having a linear relationship\n", " - Correlation coefficient\n", " - Linear regression\n", "- Correlation does not imply causation\n", " - $x$ is correlated with $y$ **does not mean** $x$ causes $y$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What can't correlation measure?\n", "While the correlation coefficient is a convenient way to quantify the strength of a relationship between two variables, it's far from perfect. In this exercise, you'll explore one of the caveats of the correlation coefficient by examining the relationship between a country's GDP per capita (`gdp_per_cap`) and happiness score.\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7019547642148014\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Scatterplot of gdp_per_cap and life_exp\n", "sns.scatterplot(x='gdp_per_cap', y='life_exp', data=world_happiness);\n", "\n", "# Correlation between gdp_per_cap and life_exp\n", "cor = world_happiness['gdp_per_cap'].corr(world_happiness['life_exp'])\n", "print(cor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transforming variables\n", "When variables have skewed distributions, they often require a transformation in order to form a linear relationship with another variable so that correlation can be computed. In this exercise, you'll perform a transformation yourself." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7279733012222975\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Scatterplot of happiness_score vs. gdp_per_cap\n", "sns.scatterplot(x='gdp_per_cap', y='happiness_score', data=world_happiness);\n", "\n", "# Calculate correlation\n", "cor = world_happiness['gdp_per_cap'].corr(world_happiness['happiness_score'])\n", "print(cor)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8043146004918288\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create log_gdp_per_cap column\n", "world_happiness['log_gdp_per_cap'] = np.log(world_happiness['gdp_per_cap'])\n", "\n", "# Scatterplot of log_gdp_per_cap and happiness_score\n", "sns.scatterplot(x='log_gdp_per_cap', y='happiness_score', data=world_happiness);\n", "\n", "# Calculate correlation\n", "cor = world_happiness['log_gdp_per_cap'].corr(world_happiness['happiness_score'])\n", "print(cor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Does sugar improve happiness?\n", "A new column has been added to `world_happiness` called `grams_sugar_per_day`, which contains the average amount of sugar eaten per person per day in each country. In this exercise, you'll examine the effect of a country's average sugar consumption on its happiness score." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrysocial_supportfreedomcorruptiongenerositygdp_per_caplife_exphappiness_scoregrams_sugar_per_day
Unnamed: 0
1Finland254.0474240081.815586.8
2Denmark463.0224830081.0154152.0
3Norway338.0116630082.6153120.0
4Iceland1745.034790083.0152132.0
5Netherlands151912.075050081.8151122.0
..............................
129Yemen10014783.0155234068.1577.9
130Rwanda144212.090211069.1414.1
131Tanzania1317834.049298067.7328.0
132Afghanistan151155136.0137176064.1224.5
133Central African Republic155133122.011379452.9122.4
\n", "

133 rows × 9 columns

\n", "
" ], "text/plain": [ " country social_support freedom corruption \\\n", "Unnamed: 0 \n", "1 Finland 2 5 4.0 \n", "2 Denmark 4 6 3.0 \n", "3 Norway 3 3 8.0 \n", "4 Iceland 1 7 45.0 \n", "5 Netherlands 15 19 12.0 \n", "... ... ... ... ... \n", "129 Yemen 100 147 83.0 \n", "130 Rwanda 144 21 2.0 \n", "131 Tanzania 131 78 34.0 \n", "132 Afghanistan 151 155 136.0 \n", "133 Central African Republic 155 133 122.0 \n", "\n", " generosity gdp_per_cap life_exp happiness_score \\\n", "Unnamed: 0 \n", "1 47 42400 81.8 155 \n", "2 22 48300 81.0 154 \n", "3 11 66300 82.6 153 \n", "4 3 47900 83.0 152 \n", "5 7 50500 81.8 151 \n", "... ... ... ... ... \n", "129 155 2340 68.1 5 \n", "130 90 2110 69.1 4 \n", "131 49 2980 67.7 3 \n", "132 137 1760 64.1 2 \n", "133 113 794 52.9 1 \n", "\n", " grams_sugar_per_day \n", "Unnamed: 0 \n", "1 86.8 \n", "2 152.0 \n", "3 120.0 \n", "4 132.0 \n", "5 122.0 \n", "... ... \n", "129 77.9 \n", "130 14.1 \n", "131 28.0 \n", "132 24.5 \n", "133 22.4 \n", "\n", "[133 rows x 9 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "world_happiness = pd.read_csv('./dataset/world_happiness_add_sugar.csv', index_col=0)\n", "world_happiness" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6939100021829635\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Scatterplot of grams_sugar_per_day and happiness_score\n", "sns.scatterplot(x='grams_sugar_per_day', y='happiness_score', data=world_happiness);\n", "\n", "# Correlation between grams_sugar_per_day and happiness_score\n", "cor = world_happiness['grams_sugar_per_day'].corr(world_happiness['happiness_score'])\n", "print(cor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Design of experiments\n", "- Vocabulary\n", " - Experiment aims to answer: What is the effect of the **treatment** on the **response**?\n", " - Treatment: explanatory / independent variable\n", " - Response: response / dependent variable\n", " - E.g.: What is the effect of an advertisement on the number of products purchased?\n", " - Treatment: advertisement\n", " - Response: number of products purchased\n", " - Controlled experiments\n", " - Participants are assigned by researchers to either treatment group or control group\n", " - Treatment group sees advertisement\n", " - Control group does not\n", " - Group should be comparable so that causation can be inferred\n", " - If groups are not comparable, this could lead to confounding (bias)\n", "- Gold standard of experiment\n", " - Randomized controlled trial\n", " - Participants are assigned to treatment/control randomly, not based on any other characteristics\n", " - Choosing randomly helps ensure that groups are comparable\n", " - Placebo\n", " - Resembles treatement, but has no effect\n", " - Participants will not know which group they're in\n", " - Double-blind trial\n", " - Person administering the treatment/running the study doesn't know whether the treatment is real or a placebo\n", " - Prevents bias in the response and/or analysis of results\n", " - Fewopportunities for bias = more reliable conclusion about causation\n", "- Observational studies\n", " - Participants are not assigned randomly to groups\n", " - Participants assign themselves, usually based on pre-existing characteristics\n", " - Many research questions are not conductive to a controlled experiment\n", " - Cannot force someone to smoke or have a disease\n", " - Establish association, not causation\n", " - Effects can be confounded by factors that got certain people into the control or treatment group\n", " - There are ways to control for confounders to get more reliable conclusions about association\n", " - Longitudinal vs. cross-sectional studies\n", " - Longitudinal study\n", " - Participants are followed over a period of time to examine effect of treatment on response\n", " - Effect of age on height is not confounded by generation\n", " - More expensive, results take longer\n", " - Cross-sectional study\n", " - Data on participants is collected from a single snapshot in time\n", " - Effect of age on height is confounded by generation\n", " - Cheaper, fater, more convenient" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }