A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python
Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied.
The datasets used in the book are small enough to easily be entered into Python manually, although they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, and the book is perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python.
Readers will also benefit from the inclusion of:
• A thorough review of essential statistical principles, including types of data, scales of measurement, significance tests, significance levels, and type I and type II errors
• An introduction to Python, including how to communicate with Python
• A treatment of exploratory data analysis, basic statistics, and visual displays, including frequencies and descriptives, stem-and-leaf plots, q-q plots, box-and-whisker plots, and data transformations
• An exploration of data management in Python
Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Author(s): Daniel J. Denis
Edition: 1
Publisher: Wiley
Year: 2021
Language: English
Commentary: Vector PDF
Pages: 304
City: Hoboken, NJ
Tags: Data Analysis; Python; Clustering; Principal Component Analysis; Statistics; Linear Regression; Logistic Regression; Probability Theory; Analysis of Variance; Elementary; Data Exploration
Applied Univariate, Bivariate, and Multivariate Statistics Using Python
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
