Univariate, Bivariate, and Multivariate Statistics Using R

Name: Univariate, Bivariate, and Multivariate Statistics Using R | Quantitative Tools for Data Analysis and Data Science
Brand: Wiley-Blackwell
Price: 107.99 EUR
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Quantitative Tools for Data Analysis and Data Science

Daniel J. Denis(Autor*in)

Wiley-Blackwell (Verlag)

1. Auflage

Erschienen am 25. März 2020

384 Seiten

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Beschreibung

A practical source for performing essential statistical analyses and data management tasks in R

Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author-- a noted expert in quantitative teaching --has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output.

The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource:

* Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software

* Provides a resource for students and professionals in the social, behavioral, and natural sciences

* Puts the emphasis on the computational tools used in the discovery of empirical patterns

* Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects

* Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science

Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.

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Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 Introduction to Applied Statistics
1.1 The Nature of Statistics and Inference
1.2 A Motivating Example
1.3 What About ``Big Data´´?
1.4 Approach to Learning R
1.5 Statistical Modeling in a Nutshell
1.6 Statistical Significance Testing and Error Rates
1.7 Simple Example of Inference Using a Coin
1.8 Statistics Is for Messy Situations
1.9 Type I versus Type II Errors
1.10 Point Estimates and Confidence Intervals
1.11 So What Can We Conclude from One Confidence Interval?
1.12 Variable Types
1.13 Sample Size, Statistical Power, and Statistical Significance
1.14 How ``p&0.05´´ Happens
1.15 Effect Size
1.16 The Verdict on Significance Testing
1.17 Training versus Test Data
1.18 How to Get the Most Out of This Book
Exercises
Chapter 2 Introduction to R and Computational Statistics
2.1 How to Install R on Your Computer
2.2 How to Do Basic Mathematics with R
2.2.1 Combinations and Permutations
2.2.2 Plotting Curves Using curve()
2.3 Vectors and Matrices in R
2.4 Matrices in R
2.4.1 The Inverse of a Matrix
2.4.2 Eigenvalues and Eigenvectors
2.5 How to Get Data into R
2.6 Merging Data Frames
2.7 How to Install a Package in R, and How to Use It
2.8 How to View the Top, Bottom, and ``Some´´ of a Data File
2.9 How to Select Subsets from a Dataframe
2.10 How R Deals with Missing Data
2.11 Using ls( ) to See Objects in the Workspace
2.12 Writing Your Own Functions
2.13 Writing Scripts
2.14 How to Create Factors in R
2.15 Using the table() Function
2.16 Requesting a Demonstration Using the example() Function
2.17 Citing R in Publications
Exercises
Chapter 3 Exploring Data with R: Essential Graphics and Visualization
3.1 Statistics, R, and Visualization
3.2 R's plot() Function
3.3 Scatterplots and Depicting Data in Two or More Dimensions
3.4 Communicating Density in a Plot
3.5 Stem-and-Leaf Plots
3.6 Assessing Normality
3.7 Box-and-Whisker Plots
3.8 Violin Plots
3.9 Pie Graphs and Charts
3.10 Plotting Tables
Exercises
Chapter 4 Means, Correlations, Counts: Means, Correlations, Counts: Drawing Inferences Using Easy-to-Implement Statistical Tests
4.1 Computing z and Related Scores in R
4.2 Plotting Normal Distributions
4.3 Correlation Coefficients in R
4.4 Evaluating Pearson's r for Statistical Significance
4.5 Spearman's Rho: A Nonparametric Alternative to Pearson
4.6 Alternative Correlation Coefficients in R
4.7 Tests of Mean Differences
4.7.1 t-Tests for One Sample
4.7.2 Two-Sample t-Test
4.7.3 Was the Welch Test Necessary?
4.7.4 t-Test via Linear Model Set-up
4.7.5 Paired-Samples t-Test
4.8 Categorical Data
4.8.1 Binomial Test
4.8.2 Categorical Data Having More Than Two Possibilities
4.9 Radar Charts
4.10 Cohen's Kappa
Exercises
Chapter 5 Power Analysis and Sample Size Estimation Using R
5.1 What Is Statistical Power?
5.2 Does That Mean Power and Huge Sample Sizes Are ``Bad?´´
5.3 Should I Be Estimating Power or Sample Size?
5.4 How Do I Know What the Effect Size Should Be?
5.4.1 Ways of Setting Effect Size in Power Analyses
5.5 Power for t-Tests
5.5.1 Example: Treatment versus Control Experiment
5.5.2 Extremely Small Effect Size
5.6 Estimating Power for a Given Sample Size
5.7 Power for Other Designs - The Principles Are the Same
5.7.1 Power for One-Way ANOVA
5.7.2 Converting R2 to f
5.8 Power for Correlations
5.9 Concluding Thoughts on Power
Exercises
Chapter 6 Analysis of Variance: Fixed Effects, Random Effects, Mixed Models, and Repeated Measures
6.1 Revisiting t-Tests
6.2 Introducing the Analysis of Variance (ANOVA)
6.2.1 Achievement as a Function of Teacher
6.3 Evaluating Assumptions
6.3.1 Inferential Tests for Normality
6.3.2 Evaluating Homogeneity of Variances
6.4 Performing the ANOVA Using aov()
6.4.1 The Analysis of Variance Summary Table
6.4.2 Obtaining Treatment Effects
6.4.3 Plotting Results of the ANOVA
6.4.4 Post Hoc Tests on the Teacher Factor
6.5 Alternative Way of Getting ANOVA Results via lm()
6.5.1 Contrasts in lm() versus Tukey's HSD
6.6 Factorial Analysis of Variance
6.6.1 Why Not Do Two One-Way ANOVAs?
6.7 Example of Factorial ANOVA
6.7.1 Graphing Main Effects and Interaction in the Same Plot
6.8 Should Main Effects Be Interpreted in the Presence of Interaction?
6.9 Simple Main Effects
6.10 Random Effects ANOVA and Mixed Models
6.10.1 A Rationale for Random Factors
6.10.2 One-Way Random Effects ANOVA in R
6.11 Mixed Models
6.12 Repeated-Measures Models
Exercises
Chapter 7 Simple and Multiple Linear Regression
7.1 Simple Linear Regression
7.2 Ordinary Least-Squares Regression
7.3 Adjusted R2
7.4 Multiple Regression Analysis
7.5 Verifying Model Assumptions
7.6 Collinearity Among Predictors and the Variance Inflation Factor
7.7 Model-Building and Selection Algorithms
7.7.1 Simultaneous Inference
7.7.2 Hierarchical Regression
7.7.2.1 Example of Hierarchical Regression
7.8 Statistical Mediation
7.9 Best Subset and Forward Regression
7.9.1 How Forward Regression Works
7.10 Stepwise Selection
7.11 The Controversy Surrounding Selection Methods
Exercises
Chapter 8 Logistic Regression and the Generalized Linear Model
8.1 The ``Why´´ Behind Logistic Regression
8.2 Example of Logistic Regression in R
8.3 Introducing the Logit: The Log of the Odds
8.4 The Natural Log of the Odds
8.5 From Logits Back to Odds
8.6 Full Example of Logistic Regression
8.6.1 Challenger O-ring Data
8.7 Logistic Regression on Challenger Data
8.8 Analysis of Deviance Table
8.9 Predicting Probabilities
8.10 Assumptions of Logistic Regression
8.11 Multiple Logistic Regression
8.12 Training Error Rate Versus Test Error Rate
Exercises
Chapter 9 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis
9.1 Why Conduct MANOVA?
9.2 Multivariate Tests of Significance
9.3 Example of MANOVA in R
9.4 Effect Size for MANOVA
9.5 Evaluating Assumptions in MANOVA
9.6 Outliers
9.7 Homogeneity of Covariance Matrices
9.7.1 What if the Box-M Test Had Suggested a Violation?
9.8 Linear Discriminant Function Analysis
9.9 Theory of Discriminant Analysis
9.10 Discriminant Analysis in R
9.11 Computing Discriminant Scores Manually
9.12 Predicting Group Membership
9.13 How Well Did the Discriminant Function Analysis Do?
9.14 Visualizing Separation
9.15 Quadratic Discriminant Analysis
9.16 Regularized Discriminant Analysis
Exercises
Chapter 10 Principal Component Analysis
10.1 Principal Component Analysis Versus Factor Analysis
10.2 A Very Simple Example of PCA
10.2.1 Pearson's 1901 Data
10.2.2 Assumptions of PCA
10.2.3 Running the PCA
10.2.4 Loadings in PCA
10.3 What Are the Loadings in PCA?
10.4 Properties of Principal Components
10.5 Component Scores
10.6 How Many Components to Keep?
10.6.1 The Scree Plot as an Aid to Component Retention
10.7 Principal Components of USA Arrests Data
10.8 Unstandardized Versus Standardized Solutions
Exercises
Chapter 11 Exploratory Factor Analysis
11.1 Common Factor Analysis Model
11.2 A Technical and Philosophical Pitfall of EFA
11.3 Factor Analysis Versus Principal Component Analysis on the Same Data
11.3.1 Demonstrating the Non-Uniqueness Issue
11.4 The Issue of Factor Retention
11.5 Initial Eigenvalues in Factor Analysis
11.6 Rotation in Exploratory Factor Analysis
11.7 Estimation in Factor Analysis
11.8 Example of Factor Analysis on the Holzinger and Swineford Data
11.8.1 Obtaining Initial Eigenvalues
11.8.2 Making Sense of the Factor Solution
Exercises
Chapter 12 Cluster Analysis
12.1 A Simple Example of Cluster Analysis
12.2 The Concepts of Proximity and Distance in Cluster Analysis
12.3 k-Means Cluster Analysis
12.4 Minimizing Criteria
12.5 Example of k-Means Clustering in R
12.5.1 Plotting the Data
12.6 Hierarchical Cluster Analysis
12.7 Why Clustering Is Inherently Subjective
Exercises
Chapter 13 Nonparametric Tests
13.1 Mann-Whitney U Test
13.2 Kruskal-Wallis Test
13.3 Nonparametric Test for Paired Comparisons and Repeated Measures
13.3.1 Wilcoxon Signed-Rank Test and Friedman Test
13.4 Sign Test
Exercises
References
Index
EULA

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