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Applied univariate, bivariate, and multivariate statistics using Python /
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.