Table Of ContentApplied Univariate, Bivariate, and Multivariate Statistics Using Python
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Applied Univariate, Bivariate, and Multivariate
Statistics Using Python
A Beginner’s Guide to Advanced Data Analysis
Daniel J. Denis
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This edition first published 2021
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Library of Congress Cataloging-in-Publication Data
Names: Denis, Daniel J., 1974- author. | John Wiley & Sons, Inc., publisher.
Title: Applied univariate, bivariate, and multivariate statistics using Python Subtitle: A beginner’s guide to
advanced data analysis / Daniel J. Denis, University of Montana, Missoula, MT.
Description: Hoboken, NJ : John Wiley & Sons, Inc., 2021. | Includes bibliographical
references and index.
Identifiers: LCCN 2020050202 (print) | LCCN 2020050203 (ebook) | ISBN 9781119578147 (hardback) |
ISBN 9781119578178 (pdf) | ISBN 9781119578185 (epub) | ISBN 9781119578208 (ebook)
Subjects: LCSH: Statistics--Software. | Multivariate analysis. | Python (Computer program language).
Classification: LCC QA276.45.P98 D46 2021 (print) | LCC QA276.45.P98 (ebook) |
DDC 519.5/302855133--dc23
LC record available at https://lccn.loc.gov/2020050202
LC ebook record available at https://lccn.loc.gov/2020050203
Cover image: © MR.Cole_Photographer/Getty Images
Cover design by Wiley
Set in 9.5/12.5 STIXTwoText by Integra Software Services, Pondicherry, India
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To Kaiser
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vii
Contents
Preface xii
1 A Brief Introduction and Overview of Applied Statistics 1
1.1 How Statistical Inference Works 4
1.2 Statistics and Decision-Making 7
1.3 Quantifying Error Rates in Decision-Making: Type I and Type II Errors 8
1.4 Estimation of Parameters 9
1.5 Essential Philosophical Principles for Applied Statistics 11
1.6 Continuous vs. Discrete Variables 13
1.6.1 Continuity Is Not Always Clear-Cut 15
1.7 Using Abstract Systems to Describe Physical Phenomena:
Understanding Numerical vs. Physical Differences 16
1.8 Data Analysis, Data Science, Machine Learning, Big Data 18
1.9 “Training” and “Testing” Models: What “Statistical Learning”
Means in the Age of Machine Learning and Data Science 20
1.10 Where We Are Going From Here: How to Use This Book 22
Review Exercises 23
2 Introduction to Python and the Field of Computational Statistics 25
2.1 The Importance of Specializing in Statistics and Research,
Not Python: Advice for Prioritizing Your Hierarchy 26
2.2 How to Obtain Python 28
2.3 Python Packages 29
2.4 Installing a New Package in Python 31
2.5 Computing z-Scores in Python 32
2.6 Building a Dataframe in Python: And Computing Some Statistical
Functions 35
2.7 Importing a .txt or .csv File 38
2.8 Loading Data into Python 39
2.9 Creating Random Data in Python 40
2.10 Exploring Mathematics in Python 40
2.11 Linear and Matrix Algebra in Python: Mechanics of
Statistical Analyses 41
2.11.1 Operations on Matrices 44
2.11.2 Eigenvalues and Eigenvectors 47
Review Exercises 48
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viii Contents
3 Visualization in Python: Introduction to Graphs and Plots 50
3.1 Aim for Simplicity and Clarity in Tables and Graphs:
Complexity is for Fools! 52
3.2 State Population Change Data 54
3.3 What Do the Numbers Tell Us? Clues to Substantive Theory 56
3.4 The Scatterplot 58
3.5 Correlograms 59
3.6 Histograms and Bar Graphs 61
3.7 Plotting Side-by-Side Histograms 62
3.8 Bubble Plots 63
3.9 Pie Plots 65
3.10 Heatmaps 66
3.11 Line Charts 68
3.12 Closing Thoughts 69
Review Exercises 70
4 Simple Statistical Techniques for Univariate and Bivariate Analyses 72
4.1 Pearson Product-Moment Correlation 73
4.2 A Pearson Correlation Does Not (Necessarily) Imply
Zero Relationship 75
4.3 Spearman’s Rho 76
4.4 More General Comments on Correlation:
Don’t Let a Correlation Impress You Too Much! 79
4.5 Computing Correlation in Python 80
4.6 T-Tests for Comparing Means 84
4.7 Paired-Samples t-Test in Python 88
4.8 Binomial Test 90
4.9 The Chi-Squared Distribution and Goodness-of-Fit Test 91
4.10 Contingency Tables 93
Review Exercises 94
5 Power, Effect Size, P-Values, and Estimating Required
Sample Size Using Python 96
5.1 What Determines the Size of a P-Value? 96
5.2 How P-Values Are a Function of Sample Size 99
5.3 What is Effect Size? 100
5.4 Understanding Population Variability in the Context of
Experimental Design 102
5.5 Where Does Power Fit into All of This? 103
5.6 Can You Have Too Much Power? Can a Sample Be Too Large? 104
5.7 Demonstrating Power Principles in Python: Estimating
Power or Sample Size 106
5.8 Demonstrating the Influence of Effect Size 108
5.9 The Influence of Significance Levels on Statistical Power 108
5.10 What About Power and Hypothesis Testing in the Age of “Big Data”? 110
5.11 Concluding Comments on Power, Effect Size, and Significance Testing 111
Review Exercises 112
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Contents ix
6 Analysis of Variance 113
6.1 T-Tests for Means as a “Special Case” of ANOVA 114
6.2 Why Not Do Several t-Tests? 116
6.3 Understanding ANOVA Through an Example 117
6.4 Evaluating Assumptions in ANOVA 121
6.5 ANOVA in Python 124
6.6 Effect Size for Teacher 125
6.7 Post-Hoc Tests Following the ANOVA F-Test 125
6.8 A Myriad of Post-Hoc Tests 127
6.9 Factorial ANOVA 129
6.10 Statistical Interactions 131
6.11 Interactions in the Sample Are a Virtual Guarantee:
Interactions in the Population Are Not 133
6.12 Modeling the Interaction Term 133
6.13 Plotting Residuals 134
6.14 Randomized Block Designs and Repeated Measures 135
6.15 Nonparametric Alternatives 138
6.15.1 Revisiting What “Satisfying Assumptions” Means:
A Brief Discussion and Suggestion of How to Approach
the Decision Regarding Nonparametrics 140
6.15.2 Your Experience in the Area Counts 140
6.15.3 What If Assumptions Are Truly Violated? 141
6.15.4 Mann-Whitney U Test 144
6.15.5 Kruskal-Wallis Test as a Nonparametric Alternative to ANOVA 145
Review Exercises 147
7 Simple and Multiple Linear Regression 148
7.1 Why Use Regression? 150
7.2 The Least-Squares Principle 152
7.3 Regression as a “New” Least-Squares Line 153
7.4 The Population Least-Squares Regression Line 154
7.5 How to Estimate Parameters in Regression 155
7.6 How to Assess Goodness of Fit? 157
7.7 R2 – Coefficient of Determination 158
7.8 Adjusted R2 159
7.9 Regression in Python 161
7.10 Multiple Linear Regression 164
7.11 Defining the Multiple Regression Model 164
7.12 Model Specification Error 166
7.13 Multiple Regression in Python 167
7.14 Model-Building Strategies: Forward, Backward, Stepwise 168
7.15 Computer-Intensive “Algorithmic” Approaches 171
7.16 Which Approach Should You Adopt? 171
7.17 Concluding Remarks and Further Directions:
Polynomial Regression 172
Review Exercises 174
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