图书简介

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

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