Introduction to the Practice of Statistics

David S. Moore is Shanti S. Gupta Distinguished Professor of Statistics, Emeritus, at Purdue University and was 1998 president of the American Statistical Association. He received his A.B. from Princeton and his Ph.D. from Cornell, both in mathematics.
George P. McCabe is the Associate Dean for Academic Affairs in the College of Science and a Professor of Statistics at Purdue University.
Bruce A. Craig is Professor of Statistics and Director of the Statistical Consulting Service at Purdue University. He is an active member of the American Statistical Association and was chair of its section on Statistical Consulting in 2009.

Introduction to the Practice of Statistics is the classic textbook for teaching statistics. This textbook shows students how to produce and interpret data from real-world contexts, guiding them through the type of data gathering and analysis that working statisticians do every day. With this phenomenally successful approach developed by David Moore and George McCabe, statistics is more than just a collection of techniques and formulas. Instead, students develop a way of thinking about data with a focus on problem-solving that helps them understand concepts and master statistical reasoning.

Part of the best-selling Moore family of statistics books, Introduction to the Practice of Statistics is designed for a two-semester 'introduction to statistics' course and offers a rigorous introduction to the subject. This textbook is available on LaunchPad, which combines an interactive ebook with multimedia content and assessment tools, including LearningCurve adaptive quizzing. See 'Instructor Resources' and 'Student Resources' for further information.

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1.1 Data.-

1.2 Displaying Distributions with Graphs.-

1.3 Describing Distributions with Numbers.-

1.4 Density Curves and Normal Distributions.-

2.1 Relationships.-

2.2 Scatterplots.-

2.3 Correlation.-

2.4 Least-Squares Regression.-

2.5 Cautions about Correlation and Regression.-

<2.6 Data Analysis for Two-Way Tables.-

2.7 The Question of Causation.-

3.1 Sources of Data.-

3.2 Design of Experiments.-

3.3 Sampling Design.-

3.4 Ethics.-

4.1 Randomness.-

4.2 Probability Models.-

4.3 Random Variables.-

4.4 Means and Variances of Random Variables.-

4.5 General Probability Rules.-

5.1 Toward Statistical Inference.-

5.2 The Sampling Distribution of a Sample Mean.-

5.3 Sampling Distributions for Counts and Proportions.-

6.1 Estimating with Confidence.-

6.2 Tests of Significance.-

6.3 Use and Abuse of Tests.-

6.4 Power and Inference as a Decision

Power.-

7.1 Inference for the Mean of a Population.-

7.2 Comparing Two Means.-

7.3 Additional Topics on Inference.-

8.1 Inference for a Single Proportion.-

8.2 Comparing Two Proportions.-

9.1 Inference for Two-Way Tables.-

10.1 Simple Linear Regression.-

10.2 More Detail about Simple Linear Regression.-

11.1 Inference for Multiple Regression.-

11.2 A Case Study.-

12.1 Inference for One-Way Analysis of Variance.-

12.2 Comparing the Means.-

13.1 The Two-Way ANOVA Model.-

14.1 The Logistic Regression Model.-

14.2 Inference for Logistic Regression.-

15.1 The Wilcoxon Rank Sum Test.-

15.2 The Wilcoxon Signed Rank Test.-

15.3 The Kruskal-Wallis Test.-

16.1 The Bootstrap Idea.-

16.2 First Steps in Using the Bootstrap.-

16.3 How Accurate Is a Bootstrap Distribution?.-

16.4 Bootstrap Confidence Intervals.-

16.5 Significance Testing Using Permutation Tests.-

17.1 Processes and Statistical Process Control.-

17.2 Using Control Charts.-

17.3 Process Capability Indexes.-

17.4 Control Charts for Sample Proportions.-