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Applied Univariate, Bivariate, and Multivariate Statistics Using Python. Daniel J. Denis
Читать онлайн.Название Applied Univariate, Bivariate, and Multivariate Statistics Using Python
Год выпуска 0
isbn 9781119578185
Автор произведения Daniel J. Denis
Жанр Математика
Издательство John Wiley & Sons Limited
11 Chapter 6: Analysis of Variance6.1 T-Tests for Means as a “Special Case” of ANOVA6.2 Why Not Do Several t-Tests?6.3 Understanding ANOVA Through an Example6.4 Evaluating Assumptions in ANOVA6.5 ANOVA in Python6.6 Effect Size for Teacher6.7 Post-Hoc Tests Following the ANOVA F-Test6.8 A Myriad of Post-Hoc Tests6.9 Factorial ANOVA6.10 Statistical Interactions6.11 Interactions in the Sample Are a Virtual Guarantee: Interactions in the Population Are Not6.12 Modeling the Interaction Term6.13 Plotting Residuals6.14 Randomized Block Designs and Repeated Measures6.15 Nonparametric Alternatives6.15.1 Revisiting What “Satisfying Assumptions” Means: A Brief Discussion and Suggestion of How to Approach the Decision Regarding Nonparametrics6.15.2 Your Experience in the Area Counts6.15.3 What If Assumptions Are Truly Violated?6.15.4 Mann-Whitney U Test6.15.5 Kruskal-Wallis Test as a Nonparametric Alternative to ANOVAReview Exercises
12 Chapter 7: Simple and Multiple Linear Regression7.1 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 Model8.1 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 Analysis9.1 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 Analysis10.1 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 Analysis11.1 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 Analysis12.1 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
List of Tables
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).
List of Illustrations
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.
Guide
1 Cover
5 Table of Contents
6 Preface
9 Index
Pages
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