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

Statistics With R

Solving Problems Using Real-World Data