Part I: Collecting and Exploring Data Chapter 1 Picturing Distributions with Graphs Individuals and variables Identifying categorical and quantitative variables Categorical variables: pie charts and bar graphs Quantitative variables: histograms Interpreting histograms Quantitative variables: dotplots Time plots Discussion: (Mis)adventures in data entry Chapter 2 Describing Quantitative Distributions with Numbers Measures of center: median, mean Measures of spread: percentiles, standard deviation Graphical displays of numerical summaries Spotting suspected outliers* Discussion: Dealing with outliers Organizing a statistical problem Chapter 3 Scatterplots and Correlation Explanatory and response variables Relationship between two quantitative variables: scatterplots Adding categorical variables to scatterplots Measuring linear association: correlation Chapter 4 Regression The least-squares regression line Facts about least-squares regression Outliers and influential observations Working with logarithm transformations* Cautions about correlation and regression Association does not imply causation Chapter 5 Two-Way Tables Marginal distributions Conditional distributions Simpson's paradox Chapter 6 Samples and Observational Studies Observation versus experiment Sampling Sampling designs Sample surveys Cohorts and case-control studies Chapter 7 Designing Experiments Designing experiments Randomized comparative experiments Common experimental designs Cautions about experimentation Ethics in experimentation Discussion: The Tuskegee syphilis study Chapter 8 Collecting and Exploring Data: Part I Review Part I Summary Comprehensive Review Exercises Large Dataset Exercises Online Data Sources EESEE Case Studies Part II: From Chance to Inference Chapter 9 Essential Probability Rules The idea of probability Probability models Probability rules Discrete versus continuous probability models Random variables Risk and odds* Chapter 10 Independence and Conditional Probabilities* Relationships among several events Conditional probability General probability rules Tree diagrams Bayes's theorem Discussion: Making sense of conditional probabilities in diagnostic tests Chapter 11 The Normal Distributions Normal distributions The 68-95-99.7 rule The standard Normal distribution Finding Normal probabilities Finding percentiles Using the standard Normal table* Normal quantile plots* Chapter 12 Discrete Probability Distributions* The binomial setting and binomial distributions Binomial probabilities Binomial mean and standard deviation The Normal approximation to binomial distributions The Poisson distributions Poisson probabilities Chapter 13 Sampling Distributions Parameters and statistics Statistical estimation and sampling distributions The sampling distribution of the central limit theorem The sampling distribution of the law of large numbers* Chapter 14 Introduction to Inference Statistical estimation Margin of error and confidence level Confidence intervals for the mean Hypothesis testing P-value and statistical significance Tests for a population mean Tests from confidence intervals Chapter 15 Inference in Practice Conditions for inference in practice How confidence intervals behave How hypothesis tests behave Discussion: The scientific approach Planning studies: selecting an appropriate sample size Chapter 16 From Chance to Inference: Part II Review Part II Summary Comprehensive Review Exercises Advanced Topics (Optional Material) Online Data Sources EESEE Case Studies Part III: Statistical Inference Chapter 17 Inference about a Population Mean Conditions for inference The t distributions The one-sample t confidence interval The one-sample t test Matched pairs t procedures Robustness of t procedures Chapter 18 Comparing Two Means Comparing two population means Two-sample t procedures Robustness again Avoid the pooled two-sample t procedures* Avoid inference about standard deviations* Chapter 19 Inference about a Population Proportion The sample proportion Large-sample confidence intervals for a proportion Accurate confidence intervals for a proportion Choosing the sample size* Hypothesis tests for a proportion Chapter 20 Comparing Two Proportions Two-sample problems: proportions The sampling distribution of a difference between proportions Large-sample confidence intervals for comparing proportions Accurate confidence intervals for comparing proportions Hypothesis tests for comparing proportions Relative risk and odds ratio* Discussion: Assessing and understanding health risks Chapter 21 The Chi-Square Test for Goodness of Fit Hypotheses for goodness of fit The chi-square test for goodness of fit Interpreting chi-square results Conditions for the chi-square test The chi-square distributions The chi-square test and the one-sample z test* Chapter 22 The Chi-Square Test for Two-Way Tables Two-way tables The problem of multiple comparisons Expected counts in two-way tables The chi-square test Conditions for the chi-square test Uses of the chi-square test Using a table of critical values* The chi-square test and the two-sample z test* Chapter 23 Inference for Regression Conditions for regression inference Estimating the parameters Testing the hypothesis of no linear relationship Testing lack of correlation* Confidence intervals for the regression slope Inference about prediction Checking the conditions for inference Chapter 24 One-Way Analysis of Variance: Comparing Several Means Comparing several means The analysis of variance F test The idea of analysis of variance Conditions for ANOVA F-distributions and degrees of freedom The one-way ANOVA and the pooled two-sample t test* Details of ANOVA calculations* Chapter 25 Statistical Inference: Part III Review Part III Summary Review Exercises Supplementary Exercises EESEE Case Studies Part IV: Optional Companion Chapters Chapter 26 More about Analysis of Variance: Follow-up Tests and Two-Way ANOVA Beyond one-way ANOVA Follow up analysis: Tukey’s pairwise multiple comparisons Follow up analysis: contrasts* Two-way ANOVA: conditions, main effects, and interaction Inference for two-way ANOVA Some details of two-way ANOVA* Chapter 27 Nonparametric Tests Comparing two samples: the Wilcoxon rank sum test Matched pairs: the Wilcoxon signed rank test Comparing several samples: the Kruskal-Wallis test Chapter 28 Multiple and Logistic Regression Parallel regression lines Estimating parameters Conditions for inference Inference for multiple regression Interaction A case study for multiple regression Logistic regression Inference for logistic regression Notes and Data Sources Tables Answers to Selected Exercises Some Data Sets Recurring Across Chapters Index |