Applied univariate, bivariate, and multivariate statistics using Python

• Main Author: Denis, Daniel J., 1974- (Author)
• Format: Electronic eBook
• Language: English
• Published: Hoboken, NJ : John Wiley & Sons, Inc., 2021.
• Subjects: Statistics > Software.; Multivariate analysis.; Python (Computer program language); elektronické knihy; electronic books
• ISBN: 9781119578208; 1119578205; 9781119578185; 1119578183; 9781119578178; 1119578175; 9781119578147
• Physical Description: 1 online resource

"This book is an elementary beginner's introduction to applied statistics using Python. It for the most part assumes no prior knowledge of statistics or data analysis, though a prior introductory course is desirable. It can be appropriately used in a 16-week course in statistics or data an...

Table of Contents:

• Cover
• Title Page
• Copyright Page
• Contents
• Preface
• 1. A Brief Introduction and Overview of Applied Statistics
• 1.1 How Statistical Inference Works
• 1.2 Statistics and Decision-Making
• 1.3 Quantifying Error Rates in Decision-Making: Type I and Type II Errors
• 1.4 Estimation of Parameters
• 1.5 Essential Philosophical Principles for Applied Statistics
• 1.6 Continuous vs. Discrete Variables
• 1.6.1 Continuity Is Not Always Clear-Cut
• 1.7 Using Abstract Systems to Describe Physical Phenomena: Understanding Numerical vs. Physical Differences
• 1.8 Data Analysis, Data Science, Machine Learning, Big Data
• 1.9 "Training" and "Testing" Models: What "Statistical Learning" Means in the Age of Machine Learning and Data Science
• 1.10 Where We Are Going From Here: How to Use This Book
• Review Exercises
• 2. Introduction to Python and the Field of Computational Statistics
• 2.1 The Importance of Specializing in Statistics and Research, Not Python: Advice for Prioritizing Your Hierarchy
• 2.2 How to Obtain Python
• 2.3 Python Packages
• 2.4 Installing a New Package in Python
• 2.5 Computing z-Scores in Python
• 2.6 Building a Dataframe in Python: And Computing Some Statistical Functions
• 2.7 Importing a .txt or .csv File
• 2.8 Loading Data into Python
• 2.9 Creating Random Data in Python
• 2.10 Exploring Mathematics in Python
• 2.11 Linear and Matrix Algebra in Python: Mechanics of Statistical Analyses
• 2.11.1 Operations on Matrices
• 2.11.2 Eigenvalues and Eigenvectors
• Review Exercises
• 3. Visualization in Python: Introduction to Graphs and Plots
• 3.1 Aim for Simplicity and Clarity in Tables and Graphs: Complexity is for Fools!
• 3.2 State Population Change Data
• 3.3 What Do the Numbers Tell Us? Clues to Substantive Theory
• 3.4 The Scatterplot
• 3.5 Correlograms
• 3.6 Histograms and Bar Graphs.
• 3.7 Plotting Side-by-Side Histograms
• 3.8 Bubble Plots
• 3.9 Pie Plots
• 3.10 Heatmaps
• 3.11 Line Charts
• 3.12 Closing Thoughts
• Review Exercises
• 4. Simple Statistical Techniques for Univariate and Bivariate Analyses
• 4.1 Pearson Product-Moment Correlation
• 4.2 A Pearson Correlation Does Not (Necessarily) Imply Zero Relationship
• 4.3 Spearman's Rho
• 4.4 More General Comments on Correlation: Don't Let a Correlation Impress You Too Much!
• 4.5 Computing Correlation in Python
• 4.6 T-Tests for Comparing Means
• 4.7 Paired-Samples t-Test in Python
• 4.8 Binomial Test
• 4.9 The Chi-Squared Distribution and Goodness-of-Fit Test
• 4.10 Contingency Tables
• Review Exercises
• 5. Power, Effect Size, P-Values, and Estimating Required Sample Size Using Python
• 5.1 What Determines the Size of a P-Value?
• 5.2 How P-Values Are a Function of Sample Size
• 5.3 What is Effect Size?
• 5.4 Understanding Population Variability in the Context of Experimental Design
• 5.5 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: Estimating Power or Sample Size
• 5.8 Demonstrating the Influence of Effect Size
• 5.9 The Influence of Significance Levels on Statistical Power
• 5.10 What About Power and Hypothesis Testing in the Age of "Big Data"?
• 5.11 Concluding Comments on Power, Effect Size, and Significance Testing
• Review Exercises
• 6. Analysis of Variance
• 6.1 T-Tests for Means as a "Special Case" of ANOVA
• 6.2 Why Not Do Several t-Tests?
• 6.3 Understanding ANOVA Through an Example
• 6.4 Evaluating Assumptions in ANOVA
• 6.5 ANOVA in Python
• 6.6 Effect Size for Teacher
• 6.7 Post-Hoc Tests Following the ANOVA F-Test
• 6.8 A Myriad of Post-Hoc Tests
• 6.9 Factorial ANOVA
• 6.10 Statistical Interactions.
• 6.11 Interactions in the Sample Are a Virtual Guarantee: Interactions in the Population Are Not
• 6.12 Modeling the Interaction Term
• 6.13 Plotting Residuals
• 6.14 Randomized Block Designs and Repeated Measures
• 6.15 Nonparametric Alternatives
• 6.15.1 Revisiting What "Satisfying Assumptions" Means: A Brief Discussion and Suggestion of How to Approach the Decision Regarding Nonparametrics
• 6.15.2 Your Experience in the Area Counts
• 6.15.3 What If Assumptions Are Truly Violated?
• 6.15.4 Mann-Whitney U Test
• 6.15.5 Kruskal-Wallis Test as a Nonparametric Alternative to ANOVA
• Review Exercises
• 7. Simple and Multiple Linear Regression
• 7.1 Why Use Regression?
• 7.2 The Least-Squares Principle
• 7.3 Regression as a "New" Least-Squares Line
• 7.4 The Population Least-Squares Regression Line
• 7.5 How to Estimate Parameters in Regression
• 7.6 How to Assess Goodness of Fit?
• 7.7 R2
• Coefficient of Determination
• 7.8 Adjusted R2
• 7.9 Regression in Python
• 7.10 Multiple Linear Regression
• 7.11 Defining the Multiple Regression Model
• 7.12 Model Specification Error
• 7.13 Multiple Regression in Python
• 7.14 Model-Building Strategies: Forward, Backward, Stepwise
• 7.15 Computer-Intensive "Algorithmic" Approaches
• 7.16 Which Approach Should You Adopt?
• 7.17 Concluding Remarks and Further Directions: Polynomial Regression
• Review Exercises
• 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 Model
• 8.3 Logistic Regression for Binary Responses: A Special Subclass of the Generalized Linear Model
• 8.4 Logistic Regression in Python
• 8.5 Multiple Logistic Regression
• 8.5.1 A Model with Only Lag1
• 8.6 Further Directions
• Review Exercises.
• 9. Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis
• 9.1 Why Technically Most Univariate Models are Actually Multivariate
• 9.2 Should I Be Running a Multivariate Model?
• 9.3 The Discriminant Function
• 9.4 Multivariate Tests of Significance: Why They Are Different from the F-Ratio
• 9.4.1 Wilks' Lambda
• 9.4.2 Pillai's Trace
• 9.4.3 Roy's Largest Root
• 9.4.4 Lawley-Hotelling's Trace
• 9.5 Which Multivariate Test to Use?
• 9.6 Performing MANOVA in Python
• 9.7 Effect Size for MANOVA
• 9.8 Linear Discriminant Function Analysis
• 9.9 How Many Discriminant Functions Does One Require?
• 9.10 Discriminant Analysis in Python: Binary Response
• 9.11 Another Example of Discriminant Analysis: Polytomous Classification
• 9.12 Bird's Eye View of MANOVA, ANOVA, Discriminant Analysis, and Regression: A Partial Conceptual Unification
• 9.13 Models "Subsumed" Under the Canonical Correlation Framework
• Review Exercises
• 10. Principal Components Analysis
• 10.1 What Is Principal Components Analysis?
• 10.2 Principal Components as Eigen Decomposition
• 10.3 PCA on Correlation Matrix
• 10.4 Why Icebergs Are Not Good Analogies for PCA
• 10.5 PCA in Python
• 10.6 Loadings in PCA: Making Substantive Sense Out of an Abstract Mathematical Entity
• 10.7 Naming Components Using Loadings: A Few Issues
• 10.8 Principal Components Analysis on USA Arrests Data
• 10.9 Plotting the Components
• Review Exercises
• 11. Exploratory Factor Analysis
• 11.1 The Common Factor Analysis Model
• 11.2 Factor Analysis as a Reproduction of the Covariance Matrix
• 11.3 Observed vs. Latent Variables: Philosophical Considerations
• 11.4 So, Why is Factor Analysis Controversial? The Philosophical Pitfalls of Factor Analysis
• 11.5 Exploratory Factor Analysis in Python
• 11.6 Exploratory Factor Analysis on USA Arrests Data.
• Review Exercises
• 12. Cluster Analysis
• 12.1 Cluster Analysis vs. ANOVA vs. Discriminant Analysis
• 12.2 How Cluster Analysis Defines "Proximity"
• 12.2.1 Euclidean Distance
• 12.3 K-Means Clustering Algorithm
• 12.4 To Standardize or Not?
• 12.5 Cluster Analysis in Python
• 12.6 Hierarchical Clustering
• 12.7 Hierarchical Clustering in Python
• Review Exercises
• References
• Index
• EULA.