Table Of Contents:

• PREFACE
• ABOUT THE AUTHOR
• Chapter 1: Preparing Data for Analysis and Visualization in R: The R-Team and the Pot Policy Problem
• 1.1 Choosing and learning R
• 1.2 Learning R with publicly available data
• 1.3 Achievements to unlock
• 1.4 The tricky weed problem
• 1.5 Achievement 1: Observations and variables
• 1.6 Achievement 2: Using reproducible research practices
• 1.7 Achievement 3: Understanding and changing data types
• 1.8 Achievement 4: Entering or loading data into R
• 1.9 Achievement 5: Identifying and treating missing values
• 1.10 Achievement 6: Building a basic bar chart
• 1.11 Chapter summary
• Chapter 2: Computing and Reporting Descriptive Statistics: The R-Team and the Troubling Transgender Health Care Problem
• 2.1 Achievements to unlock
• 2.2 The transgender health care problem
• 2.3 Data, codebook, and R packages for learning about descriptive statistics
• 2.4 Achievement 1: Understanding variable types and data types
• 2.5 Achievement 2: Choosing and conducting descriptive analyses for categorical (factor) variables
• 2.6 Achievement 3: Choosing and conducting descriptive analyses for continuous (numeric) variables
• 2.7 Achievement 4: Developing clear tables for reporting descriptive statistics
• 2.8 Chapter summary
• Chapter 3: Data Visualization: The R-Team and the Tricky Trigger Problem
• 3.1 Achievements to unlock
• 3.2 The tricky trigger problem
• 3.3 Data, codebook, and R packages for graphs
• 3.4 Achievement 1: Choosing and creating graphs for a single categorical variable
• 3.5 Achievement 2: Choosing and creating graphs for a single continuous variable
• 3.6 Achievement 3: Choosing and creating graphs for two variables at once
• 3.7 Achievement 4: Ensuring graphs are well-formatted with appropriate and clear titles, labels, colors, and other features
• 3.8 Chapter summary
• Chapter 4: Probability Distributions and Inference: The R-Team and the Opioid Overdose Problem
• 4.1 Achievements to unlock
• 4.2 The awful opioid overdose problem
• 4.3 Data, codebook, and R packages for learning about distributions
• 4.4 Achievement 1: Defining and using the probability distributions to infer from a sample
• 4.5 Achievement 2: Understanding the characteristics and uses of a binomial distribution of a binary variable
• 4.6 Achievement 3: Understanding the characteristics and uses of the normal distribution of a continuous variable
• 4.7 Achievement 4: Computing and interpreting z-scores to compare observations to groups
• 4.8 Achievement 5: Estimating population means from sample means using the normal distribution
• 4.9 Achievement 6: Computing and interpreting confidence intervals around means and proportions
• 4.10 Chapter summary
• Chapter 5: Computing and Interpreting Chi-Squared: The R-Team and the Vexing Voter Fraud Problem
• 5.1 Achievements to unlock
• 5.2 The voter fraud problem
• 5.3 Data, documentation, and R packages for learning about chi-squared
• 5.4 Achievement 1: Understanding the relationship between two categorical variables using bar charts, frequencies, and percentages
• 5.5 Achievement 2: Computing and comparing observed and expected values for the groups
• 5.6 Achievement 3: Calculating the chisquared statistic for the test of independence
• 5.7 Achievement 4: Interpreting the chi-squared statistic and making a conclusion about whether or not there is a relationship
• 5.8 Achievement 5: Using Null Hypothesis Significance Testing to organize statistical testing
• 5.9 Achievement 6: Using standardized residuals to understand which groups contributed to significant relationships
• 5.10 Achievement 7: Computing and interpreting effect sizes to understand the strength of a significant chi-squared relationship
• 5.11 Achievement 8: Understanding the options for failed chi-squared assumptions
• 5.12 Chapter summary
• Chapter 6: Conducting and Interpreting t-Tests: The R-Team and the Blood Pressure Predicament
• 6.1 Achievements to unlock
• 6.2 The blood pressure predicament
• 6.3 Data, codebook, and R packages for learning about t-tests
• 6.4 Achievement 1: Understanding the relationship between one categorical variable and one continuous variable using histograms, means, and standard deviations
• 6.5 Achievement 2: Comparing a sample mean to a population mean with a one-sample t-test
• 6.6 Achievement 3: Comparing two unrelated sample means with an independent-samples t-test
• 6.7 Achievement 4: Comparing two related sample means with a dependent-samples t-test
• 6.8 Achievement 5: Computing and interpreting an effect size for significant t-tests
• 6.9 Achievement 6: Examining and checking the underlying assumptions for using the t-test
• 6.10 Achievement 7: Identifying and using alternate tests when t-test assumptions are not met
• 6.11 Chapter summary
• Chapter 7: Analysis of Variance: The R-Team and the Technical Difficulties Problem
• 7.1 Achievements to unlock
• 7.2 The technical difficulties problem
• 7.3 Data, codebook, and R packages for learning about ANOVA
• 7.4 Achievement 1: Exploring the data using graphics and descriptive statistics
• 7.5 Achievement 2: Understanding and conducting one-way ANOVA
• 7.6 Achievement 3: Choosing and using post hoc tests and contrasts
• 7.7 Achievement 4: Computing and interpreting effect sizes for ANOVA
• 7.8 Achievement 5: Testing ANOVA assumptions
• 7.9 Achievement 6: Choosing and using alternative tests when ANOVA assumptions are not met
• 7.10 Achievement 7: Understanding and conducting two-way ANOVA
• 7.11 Chapter summary
• Chapter 8: Correlation Coefficients: The R-Team and the Clean Water Conundrum
• 8.1 Achievements to unlock
• 8.2 The clean water conundrum
• 8.3 Data and R packages for learning about correlation
• 8.4 Achievement 1: Exploring the data using graphics and descriptive statistics
• 8.5 Achievement 2: Computing and interpreting Pearson’s r correlation coefficient
• 8.6 Achievement 3: Conducting an inferential statistical test for Pearson’s r correlation coefficient
• 8.7 Achievement 4: Examining effect size for Pearson’s r with the coefficient of determination
• 8.8 Achievement 5: Checking assumptions for Pearson’s r correlation analyses
• 8.9 Achievement 6: Transforming the variables as an alternative when Pearson’s r correlation assumptions are not met
• 8.10 Achievement 7: Using Spearman’s rho as an alternative when Pearson’s r correlation assumptions are not met
• 8.11 Achievement 8: Introducing partial correlations
• 8.12 Chapter summary
• Chapter 9: Linear Regression: The R-Team and the Needle Exchange Examination
• 9.1 Achievements to unlock
• 9.2 The needle exchange examination
• 9.3 Data, codebook, and R packages for linear regression practice
• 9.4 Achievement 1: Using exploratory data analysis to learn about the data before developing a linear regression model
• 9.5 Achievement 2: Exploring the statistical model for a line
• 9.6 Achievement 3: Computing the slope and intercept in a simple linear regression
• 9.7 Achievement 4: Slope interpretation and significance (b1, p-value, CI)
• 9.8 Achievement 5: Model significance and model fit
• 9.9 Achievement 6: Checking assumptions and conducting diagnostics
• 9.10 Achievement 7: Adding variables to the model and using transformation
• 9.11 Chapter summary
• Chapter 10: Binary Logistic Regression: The R-Team and the Perplexing Libraries Problem
• 10.1 Achievements to unlock
• 10.2 The perplexing libraries problem
• 10.3 Data, codebook, and R packages for logistic regression practice
• 10.4 Achievement 1: Using exploratory data analysis before developing a logistic regression model
• 10.5 Achievement 2: Understanding the binary logistic regression statistical model
• 10.6 Achievement 3: Estimating a simple logistic regression model and interpreting predictor significance and interpretation
• 10.7 Achievement 4: Computing and interpreting two measures of model fit
• 10.8 Achievement 5: Estimating a larger logistic regression model with categorical and continuous predictors
• 10.9 Achievement 6: Interpreting the results of a larger logistic regression model
• 10.10 Achievement 7: Checking logistic regression assumptions and using diagnostics to identify outliers and influential values
• 10.11 Achievement 8: Using the model to predict probabilities for observations that are outside the data set
• 10.12 Achievement 9: Adding and interpreting interaction terms in logistic regression
• 10.13 Achievement 10: Using the likelihood ratio test to compare two nested logistic regression models
• 10.14 Chapter summary
• Chapter 11: Multinomial and Ordinal Logistic Regression: The R-Team and the Diversity Dilemma in STEM
• 11.1 Achievements to unlock
• 11.2 The diversity dilemma in STEM
• 11.3 Data, codebook, and R packages for multinomial and ordinal regression practice
• 11.4 Achievement 1: Using exploratory data analysis for multinomial logistic regression
• 11.5 Achievement 2: Estimating and interpreting a multinomial logistic regression model
• 11.6 Achievement 3: Checking assumptions for multinomial logistic regression
• 11.7 Achievement 4: Using exploratory data analysis for ordinal logistic regression
• 11.8 Achievement 5: Estimating and interpreting an ordinal logistic regression model
• 11.9 Achievement 6: Checking assumptions for ordinal logistic regression
• 11.10 Chapter summary
• GLOSSARY
• REFERENCES
• INDEX