Chapter 1: Picturing Distributions with Graphs
Individuals and variables
Categorical variables: pie charts and bar graphs
Quantitative variables: histograms
Interpreting histograms
Quantitative variables: stemplots
Time plots
Chapter 2: Describing Distributions with Numbers
Measuring center: the mean
Measuring center: the median
Comparing the mean and the median
Measuring spread: the quartiles
The five-number summary and boxplots
Spotting suspected outliers
Measuring spread: the standard deviation
Choosing measures of center and spread
Using technology
Organizing a statistical problem
Chapter 3: The Normal Distributions
Density curves
Describing density curves
Normal distributions
The 68-95-99.7 rule
The standard Normal distribution
Finding Normal proportions
Using the standard Normal table
Finding a value given a proportion
Chapter 4: Scatterplots and Correlation
Explanatory and response variables
Displaying relationships: scatterplots
Interpreting scatterplots
Adding categorical variables to scatterplots
Measuring linear association: correlation
Facts about correlation
Chapter 5: Regression
Regression lines
The least-squares regression line
Using technology
Facts about least-squares regression
Residuals
Influential observations
Cautions about correlation and regression
Association does not imply causation
Chapter 6: Two-Way Tables
Marginal distributions
Conditional distributions
Simpson's paradox
Chapter 7: Exploring Data: Part I Review
Part I summary
Test yourself
Supplementary exercises
PART II: FROM EXPLORATION TO INFERENCE
Chapter 8: Producing Data: Sampling
Population versus sample
How to sample badly
Simple random samples
Inference about the population
Other sampling designs
Cautions about sample surveys
The impact of technology
Chapter 9: Producing Data: Experiments
Observation versus experiment
Subjects, factors, treatments
How to experiment badly
Randomized comparative experiments
The logic of randomized comparative experiments
Cautions about experimentation
Matched pairs and other block designs
Commentary: Data Ethics
Institutional review boards
Informed consent
Confidentiality
Clinical trials
Behavioral and social science experiments
Chapter 10: Introducing Probability
The idea of probability
The search for randomness
Probability models
Probability rules
Discrete probability models
Continuous probability models
Random variables
Personal probability
Chapter 11: Sampling Distributions
Parameters and statistics
Statistical estimation and the law of large numbers
Sampling distributions
The sampling distribution of x
The central limit theorem
Chapter 12: General Rules of Probability
Independence and the multiplication rule
The general addition rule
Conditional probability
The general multiplication rule
Independence again
Tree diagrams
Chapter 13: Binomial Distributions
The binomial setting and binomial distributions
Binomial distributions in statistical sampling
Binomial probabilities
Using technology
Binomial mean and standard deviation
The Normal approximation to binomial distributions
Chapter 14: Confidence Intervals: The Basics
The reasoning of statistical estimation
Margin of error and confidence level
Confidence intervals for a population mean
How confidence intervals behave
Chapter 15: Tests of Significance: The Basics
The reasoning of tests of significance
Stating hypotheses
P-value and statistical significance
Tests for a population mean
Significance from a table*
Chapter 16: Inference in Practice
Conditions for inference in practice
Cautions about confidence intervals
Cautions about significance tests
Planning studies: sample size for confidence intervals
Planning studies: the power of a statistical test
Chapter 17: From Exploration to Inference: Part II Review
Part II summary
Review exercises
Test yourself
Supplementary exercises
PART III: INFERENCE ABOUT VARIABLES
Chapter 18: Inference about a Population Mean
Conditions for inference about a mean
The t distributions
The one-sample t confidence interval
The one-sample t test
Using technology
Matched pairs t procedures
Robustness of t procedures
Chapter 19: Two-Sample Problems
Two-sample problems
Comparing two population means
Two-sample t procedures
Using technology
Robustness again
Details of the t approximation
Avoid the pooled two-sample t procedures
Avoid inference about standard deviations
Chapter 20: Inference about a Population Proportion
The sample proportion p
Large-sample confidence intervals for a proportion
Accurate confidence intervals for a proportion
Choosing the sample size
Significance tests for a proportion
Chapter 21: Comparing Two Proportions
Two-sample problems: proportions
The sampling distribution of a difference between proportions
Large-sample confidence intervals for comparing proportions
Using technology
Accurate confidence intervals for comparing proportions
Significance tests for comparing proportions
Chapter 22: Inference about Variables: Part III Review
Part III summary
Test yourself
Supplementary exercises
PART IV: INFERENCE ABOUT RELATIONSHIPS
Chapter 23: Two Categorical Variables: The Chi-Square Test
Two-way tables
The problem of multiple comparisons
Expected counts in two-way tables
The chi-square test statistic
Cell counts required for the chi-square test
Using technology
Uses of the chi-square test
The chi-square distributions
The chi-square test for goodness of fit
Chapter 24: Inference for Regression
Conditions for regression inference
Estimating the parameters
Using technology
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 25: One-Way Analysis of Variance
Comparing Several Means
Comparing several means
The analysis of variance F test
Using technology The idea of analysis of variance
Conditions for ANOVA
F distributions and degrees of freedom
Some details of ANOVA
PART V: OPTIONAL COMPANION CHAPTERS (available on the BPS CD and online
Chapter 26: Nonparametric Tests
Comparing two samples: the Wilcoxon rank sum test
The Normal approximation for W
Using technology
What hypotheses does Wilcoxon test?
Dealing with ties in rank tests
Matched pairs: the Wilcoxon signed rank test
The Normal approximation for W+
Dealing with ties in the signed rank test
Comparing several samples: the Kruskal-Wallis test
Hypotheses and conditions for the Kruskal-Wallis test
The Kruskal-Wallis test statistic
Chapter 27: Statistical Process Control
Processes
Describing processes
The idea of statistical process control
x charts for process monitoring
s charts for process monitoring
Using control charts
Setting up control charts
Comments on statistical control
Don't confuse control with capability!
Control charts for sample proportions
Control limits for p charts
Chapter 28: Multiple Regression
Parallel regression lines
Estimating parameters
Using technology
Inference for multiple regression
Interaction
The multiple linear regression model
The woes of regression coefficients
A case study for multiple regression
Inference for regression parameters
Checking the conditions for inference
Chapter 29: More about Analysis of Variance
Beyond one-way ANOVA
Followup analysis: Tukey pairwise multiple comparisons
Followup analysis: contrasts
Two-way ANOVA: conditions, main effects, and interaction
Inference for two-way ANOVA
Some details of two-way ANOVA