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

12 Chapter 7: Simple and Multiple Linear Regression 7.1 Why Use Regression?7.2 The Least-Squares Principle7.3 Regression as a “New” Least-Squares Line7.4 The Population Least-Squares Regression Line7.5 How to Estimate Parameters in Regression7.6 How to Assess Goodness of Fit?7.7 R2 – Coefficient of Determination7.8 Adjusted R27.9 Regression in Python7.10 Multiple Linear Regression7.11 Defining the Multiple Regression Model7.12 Model Specification Error7.13 Multiple Regression in Python7.14 Model-Building Strategies: Forward, Backward, Stepwise7.15 Computer-Intensive “Algorithmic” Approaches7.16 Which Approach Should You Adopt?7.17 Concluding Remarks and Further Directions: Polynomial RegressionReview Exercises

13 Chapter 8: Logistic Regression and the Generalized Linear Model 8.1 How Are Variables Best Measured? Are There Ideal Scales on Which a Construct Should Be Targeted?8.2 The Generalized Linear Model8.3 Logistic Regression for Binary Responses: A Special Subclass of the Generalized Linear Model8.4 Logistic Regression in Python8.5 Multiple Logistic Regression8.5.1 A Model with Only Lag18.6 Further DirectionsReview Exercises

14 Chapter 9: Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis 9.1 Why Technically Most Univariate Models are Actually Multivariate9.2 Should I Be Running a Multivariate Model?9.3 The Discriminant Function9.4 Multivariate Tests of Significance: Why They Are Different from the F-Ratio9.4.1 Wilks’ Lambda9.4.2 Pillai’s Trace9.4.3 Roy’s Largest Root9.4.4 Lawley-Hotelling’s Trace9.5 Which Multivariate Test to Use?9.6 Performing MANOVA in Python9.7 Effect Size for MANOVA9.8 Linear Discriminant Function Analysis9.9 How Many Discriminant Functions Does One Require?9.10 Discriminant Analysis in Python: Binary Response9.11 Another Example of Discriminant Analysis: Polytomous Classification9.12 Bird’s Eye View of MANOVA, ANOVA, Discriminant Analysis, and Regression: A Partial Conceptual Unification9.13 Models “Subsumed” Under the Canonical Correlation FrameworkReview Exercises

15 Chapter 10: Principal Components Analysis 10.1 What Is Principal Components Analysis?10.2 Principal Components as Eigen Decomposition10.3 PCA on Correlation Matrix10.4 Why Icebergs Are Not Good Analogies for PCA10.5 PCA in Python10.6 Loadings in PCA: Making Substantive Sense Out of an Abstract Mathematical Entity10.7 Naming Components Using Loadings: A Few Issues10.8 Principal Components Analysis on USA Arrests Data10.9 Plotting the ComponentsReview Exercises

16 Chapter 11: Exploratory Factor Analysis 11.1 The Common Factor Analysis Model11.2 Factor Analysis as a Reproduction of the Covariance Matrix11.3 Observed vs. Latent Variables: Philosophical Considerations11.4 So, Why is Factor Analysis Controversial? The Philosophical Pitfalls of Factor Analysis11.5 Exploratory Factor Analysis in Python11.6 Exploratory Factor Analysis on USA Arrests DataReview Exercises

17 Chapter 12: Cluster Analysis 12.1 Cluster Analysis vs. ANOVA vs. Discriminant Analysis12.2 How Cluster Analysis Defines “Proximity”12.2.1 Euclidean Distance12.3 K-Means Clustering Algorithm12.4 To Standardize or Not?12.5 Cluster Analysis in Python12.6 Hierarchical Clustering12.7 Hierarchical Clustering in PythonReview Exercises

18 References

19 Index

20 End User License Agreement

1 Chapter 3Table 3.1 Percentage increases in COVID-19 in 14 days as of June 22, 2020.

2 Chapter 4Table 4.1 Matched design.Table 4.2 Randomized block design.Table 4.3 Learning as a function of trial (hypothetical data).Table 4.4 Contingency table for a 2 × 2 design.

3 Chapter 6Table 6.1 Achievement as a function of teacher.Table 6.2 Achievement as a function of teacher and textbook.Table 6.3 Achievement as a function of teacher and textbook.Table 6.4 Matched pairs design.Table 6.5 Learning as a function of trial (hypothetical data).

1 Chapter 1Figure 1.1 Sample death predictions in the United States during the COVID-19...

2 Chapter 3Figure 3.1 A Comparison of Nuclear Power in 1945 and 2020. Reproduced with...Figure 3.2 Total reported COVID-19 cases. Source: CDC (Centers for Disea...Figure 3.3 Map of COVID-19 Outbreak in the State of California as of O...Figure 3.4 Bubble plot of COVID-19 cases across the United States early...Figure 3.5 The price of oil from 2019 to 2020. The price turned negative...

3 Chapter 8Figure 8.1 Challenger shuttle disaster of 1986. Challenger in flight with...

4 Chapter 10Figure 10.1 Pumpkin with an eigenvector visible before the transformation...

5 Chapter 12Figure 12.1 COVID-19 Map of Montana Counties during the Pandemic of 2020.

1 Cover

2 Title page Applied Univariate, Bivariate, and Multivariate Statistics Using Python A Beginner’s Guide to Advanced Data Analysis Daniel J. Denis

3 Copyright

4 Dedication

5 Table of Contents

6 Preface

7 Begin Reading

8 References

9 Index

10 End User License Agreement

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