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      A Beginner’s Guide to Advanced Data Analysis

       Daniel J. Denis

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      © 2021 by John Wiley and Sons, Inc.

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       Library of Congress Cataloging-in-Publication Data

      ISBN 978-1-119-57814-7 (hardback)

      ISBN 978-1-119-57817-8 (ePDF)

      ISBN 978-1-119-57818-5 (ePub)

      ISBN 978-1-119-57820-8 (oBook)

      Cover image: © Photographer/Getty Images

      Cover design by Wiley

      Set in 9.5/12.5 STIXTwoText by Integra Software Services, Pondicherry, India

      1  Cover

      2  Title page

      3  Copyright

      4  Dedication

      5  Preface

      6 Chapter 1: A Brief Introduction and Overview of Applied Statistics1.1 How Statistical Inference Works1.2 Statistics and Decision-Making1.3 Quantifying Error Rates in Decision-Making: Type I and Type II Errors1.4 Estimation of Parameters1.5 Essential Philosophical Principles for Applied Statistics1.6 Continuous vs. Discrete Variables1.6.1 Continuity Is Not Always Clear-Cut1.7 Using Abstract Systems to Describe Physical Phenomena: Understanding Numerical vs. Physical Differences1.8 Data Analysis, Data Science, Machine Learning, Big Data1.9 “Training” and “Testing” Models: What “Statistical Learning” Means in the Age of Machine Learning and Data Science1.10 Where We Are Going From Here: How to Use This BookReview Exercises

      7 Chapter 2: Introduction to Python and the Field of Computational Statistics2.1 The Importance of Specializing in Statistics and Research, Not Python: Advice for Prioritizing Your Hierarchy2.2 How to Obtain Python2.3 Python Packages2.4 Installing a New Package in Python2.5 Computing z-Scores in Python2.6 Building a Dataframe in Python: And Computing Some Statistical Functions2.7 Importing a .txt or .csv File2.8 Loading Data into Python2.9 Creating Random Data in Python2.10 Exploring Mathematics in Python2.11 Linear and Matrix Algebra in Python: Mechanics of Statistical Analyses2.11.1 Operations on Matrices2.11.2 Eigenvalues and EigenvectorsReview Exercises

      8 Chapter 3: Visualization in Python: Introduction to Graphs and Plots3.1 Aim for Simplicity and Clarity in Tables and Graphs: Complexity is for Fools!3.2 State Population Change Data3.3 What Do the Numbers Tell Us? Clues to Substantive Theory3.4 The Scatterplot3.5 Correlograms3.6 Histograms and Bar Graphs3.7 Plotting Side-by-Side Histograms3.8 Bubble Plots3.9 Pie Plots3.10 Heatmaps3.11 Line Charts3.12 Closing ThoughtsReview Exercises

      9 Chapter 4: Simple Statistical Techniques for Univariate and Bivariate Analyses4.1 Pearson Product-Moment Correlation4.2 A Pearson Correlation Does Not (Necessarily) Imply Zero Relationship4.3 Spearman’s Rho4.4 More General Comments on Correlation: Don’t Let a Correlation Impress You Too Much!4.5 Computing Correlation in Python4.6 T-Tests for Comparing Means4.7 Paired-Samples t-Test in Python4.8 Binomial Test4.9 The Chi-Squared Distribution and Goodness-of-Fit Test4.10 Contingency TablesReview Exercises

      10 Chapter 5: Power, Effect Size, P-Values, and Estimating Required Sample Size Using Python5.1 What Determines the Size of a P-Value?5.2 How P-Values Are a Function of Sample Size5.3 What is Effect Size?5.4 Understanding Population Variability in the Context of Experimental Design5.5 Where Does Power Fit into All of This?5.6 Can You Have Too Much Power? Can a Sample Be Too Large?5.7 Demonstrating Power Principles in Python:

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