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Description of Samples and Populations Data types Visualizing categorical data Visualizing quantitative data Statistical summaries What is a probability? Linear Regression Fitting a regression line When is linear regression appropriate? The correlation coefficient Perspective Comparison of Groups Graphical and simple numerical comparison Between-group variation and within-group variation Populations, samples, and expected values Least squares estimation and residuals Paired and unpaired samples Perspective The Normal Distribution Properties One sample Are the data (approximately) normally distributed? The central limit theorem Statistical Models, Estimation, and Confidence Intervals Statistical models Estimation Confidence intervals Unpaired samples with different standard deviations Hypothesis Tests Null hypotheses t-tests Tests in a one-way ANOVA Hypothesis tests as comparison of nested models Type I and type II errors Model Validation and Prediction Model validation Prediction Linear Normal Models Multiple linear regression Additive two-way analysis of variance Linear models Interactions between variables Probabilities Outcomes, events, and probabilities Conditional probabilities Independence The Binomial Distribution The independent trials model The binomial distribution Estimation, confidence intervals, and hypothesis tests Differences between proportions Analysis of Count Data The chi-square test for goodness-of-fit 2 × 2 contingency table Two-sided contingency tables Logistic Regression Odds and odds ratios Logistic regression models Estimation and confidence intervals Hypothesis tests Model validation and prediction Case Exercises Case
1: Linear modeling Case
2: Data transformations Case
3: Two sample comparisons Case
4: Linear regression with and without intercept Case
5: Analysis of variance and test for linear trend Case
6: Regression modeling and transformations Case
7: Linear models Case
8: Binary variables Case
9: Agreement Case
10: Logistic regression Appendix A: Summary of Inference Methods Statistical concepts Statistical analysis Model selection Appendix B: Introduction to R Working with R Data frames and reading data into R Manipulating data Graphics with R Reproducible research Installing R Exercises Appendix C: Statistical Tables The x2distribution The normal distribution The t distribution The Fdistribution Bibliography Index R Commands and Output and Exercises appear at the end of each
chapter.