Applied Univariate, Bivariate, and Multivariate Statistics Using Python. Daniel J. Denis

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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

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