Introduction to Statistics and Data Analysis,
7th Edition

1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS PROCESS: Why Study Statistics? The Nature and Role of Variability. Statistics and the Data Analysis Process. Types of Data and Some Simple Graphical Displays.
2. COLLECTING DATA SENSIBLY: Statistical Studies: Observation and Experimentation. Sampling. Simple Comparative Experiments. More on Experimental Design. Interpreting and Communicating the Results of Statistical Analyses. More on Observational Studies: Designing Surveys (online).
3. GRAPHICAL METHODS FOR DESCRIBING DATA: Displaying Categorical Data: Comparative Bar Charts and Pie Charts. Displaying Numerical Data: Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Displaying Bivariate Numerical Data. Bivariate and Multivariable Graphical Displays. Interpreting and Communicating the Results of Statistical Analyses.
4. NUMERICAL METHODS FOR DESCRIBING DATA: Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Variability: Chebyshev’s Rule, the Empirical Rule, and z Scores. Interpreting and Communicating the Results of Statistical Analyses.
5. SUMMARIZING BIVARIATE DATA: Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Interpreting and Communicating the Results of Statistical Analyses. Logistic Regression (online).
6. PROBABILITY: Chance Experiments and Events. Definition of Probability. Basic Properties of Probability. Conditional Probability. Independence. Some General Probability Rules. Estimating Probabilities Empirically Using Simulation.
7. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS: Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. Mean and Standard Deviation of a Random Variable. Binomial and Geometric Distributions. Normal Distributions. Checking for Normality and Normalizing Transformations. Using the Normal Distribution to Approximate a Discrete Distribution.
8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTIONS: Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion.
9. ESTIMATION USING A SINGLE SAMPLE: Point Estimation. Large-Sample Confidence Interval for a Population Proportion. Confidence Interval for a Population Mean. Interpreting and Communicating the Results of Statistical Analyses. Bootstrap Confidence Intervals for a Population Proportion (optional). Bootstrap Confidence Intervals for a Population Mean (optional).
10. HYPOTHESIS TESTING USING A SINGLE SAMPLE: Hypotheses and Test Procedures. Errors in Hypothesis Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypothesis Tests for a Population Mean. Power and Probability of Type II Error. Interpreting and Communicating the Results of Statistical Analyses. Exact Binomial Test and Randomization Test for a Population Proportion (optional). Randomization Test for a Population Mean (optional).
11. COMPARING TWO POPULATIONS OR TREATMENTS: Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples. Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples. Large-Sample Inferences Concerning the Difference Between Two Population or Treatment Proportions. Interpreting and Communicating the Results of Statistical Analyses. Randomization-Based Inference for a Difference in Proportions (optional). Randomization-Based Inference for a Difference in Means (optional).
12. THE ANALYSIS OF CATEGORICAL DATA AND GOODNESS-OF-FIT TESTS: Chi-Square Tests for Univariate Data. Tests for Homogeneity and Independence in a Two-way Table. Interpreting and Communicating the Results of Statistical Analyses.
13. SIMPLE LINEAR REGRESSION AND CORRELATION: INFERENTIAL METHODS: Simple Linear Regression Model. Inferences about the Slope of the Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (online). Inferences About the Population Correlation Coefficient (online). Interpreting and Communicating the Results of Statistical Analyses (online).
14. MULTIPLE REGRESSION ANALYSIS: Multiple Regression Models. Fitting a Model and Assessing Its Utility. Inferences Based on an Estimated Model (online). Other Issues in Multiple Regression (online). Interpreting and Communicating the Results of Statistical Analyses (online).
15. ANALYSIS OF VARIANCE: Single-Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment (online). Two-Factor ANOVA (online). Interpreting and Communicating the Results of Statistical Analyses (online).
16. NONPARAMETRIC (DISTRIBUTION-FREE) STATISTICAL METHODS (ONLINE): Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional). Distribution Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. Distribution-Free ANOVA.

• Roxy Peck

  Roxy Peck is Associate Dean Emerita of the College of Science and Mathematics, and Professor of Statistics Emerita at California Polytechnic State University, San Luis Obispo. A faculty member at Cal Poly from 1979 until 2009, Roxy served for six years as Chair of the Statistics Department before becoming Associate Dean, a position she held for 13 years. She received an M.S. in Mathematics and a Ph.D. in Applied Statistics from the University of California, Riverside. Roxy is nationally known in the area of statistics education, and she was presented with the Lifetime Achievement Award in Statistics Education at the U.S. Conference on Teaching Statistics in 2009. In 2003, she received the American Statistical Association’s Founder’s Award, recognizing her contributions to K–12 and undergraduate statistics education. She is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Roxy served for five years as the Chief Reader for the Advanced Placement (AP) Statistics Exam and has chaired the American Statistical Association’s Joint Committee with the National Council of Teachers of Mathematics on Curriculum in Statistics and Probability for Grades K–12 and the Section on Statistics Education. In addition to her texts in introductory statistics, Roxy is also co-editor of “Statistical Case Studies: A Collaboration Between Academe and Industry” and a member of the editorial board for “Statistics: A Guide to the Unknown, 4th Edition.” Outside the classroom, Roxy likes to travel and spends her spare time reading mystery novels. She also collects Navajo rugs and heads to Arizona and New Mexico whenever she can find the time.

• Chris Olsen

  Chris Olsen taught statistics at George Washington High School in Cedar Rapids, Iowa, for over 25 years and currently teaches at Grinnell College. Chris is a past member (twice) of the AP Statistics Test Development Committee and has been a table leader at the AP Statistics reading for 14 years. He is a long-time consultant to the College Board and has led workshops and institutes for AP Statistics teachers in the United States and internationally. Chris was the Iowa recipient of the Presidential Award for Excellence in Science and Mathematics Teaching in 1986, a regional awardee of the IBM Computer Teacher of the Year in 1988, and received the Siemens Award for Advanced Placement in Mathematics in 1999. Chris is a frequent contributor to and is moderator of the AP Teacher Community online. He is currently a member of the editorial board of “Teaching Statistics.” Chris graduated from Iowa State University with a major in mathematics and philosophy, and while acquiring graduate degrees at the University of Iowa, he concentrated on statistics, computer programming, and psychometrics. In his spare time he enjoys reading and hiking. He and his wife have a daughter, Anna, a Caltech graduate in Civil Engineering.

• ACCESSIBLE NARRATIVE. A more informal writing style accommodates a broader range of student reading levels.

• UPDATED EXAMPLES AND EXERCISES. In a continuing effort to keep things interesting and relevant, the 6th Edition contains many updated examples and exercises that use data from recent journal articles, newspapers, and the web on topics of interest to students.

• NEW SECTIONS ON RANDOMIZATION-BASED INFERENCE METHODS. Research indicates that randomization-based instruction in statistical inference may help learners to better understand the concepts of confidence and significance. The 6th Edition includes new optional sections on randomization-based inference methods. These methods are also particularly useful in that they provide an alternative method of analysis that can be used when the conditions required for normal distribution-based inference are not met. Each of the inference methods (Chapters 9 through 11) include new optional sections on randomization-based inference that include bootstrap methods for simulation-based confidence intervals and randomization tests of hypotheses. These new sections are accompanied by online Shiny apps, which can be used to construct bootstrap confidence intervals and to carry out randomization tests.

• NEW COAUTHOR. Tom Short joins the author team for the 6th edition. Tom is an Associate Professor at West Chester University of Pennsylvania, and brings a wealth of experience in teaching introductory statistics.

• HELPFUL HINTS. Helpful hints in exercises direct students to relevant examples in the text and help students who may be having trouble getting started.

• REAL DATA. Authentic scenarios with real data help students understand statistical concepts in interesting contexts that relate to their own lives.

• Margin Notes, including "Understanding the context," "Consider the data," "Formulate a plan," "Do the work," and "Interpret the results" appear in appropriate places in the examples to highlight the importance of context and to increase student awareness of the steps in the data analysis process.

• "Interpreting and Communicating the Results of Statistical Analysis" sections--which emphasize the importance of being able to interpret statistical output and communicate its meaning to non-statisticians--have assignable end-of-section questions associated with them.

• The book emphasizes graphical display as a necessary component of data analysis and provides broad coverage of sampling, survey design, experimental design and transformations, and nonlinear regression.

• Online material on logistic regression and nonparametric (distribution-free) methods give you the option of covering these topics if you wish. There is also expanded coverage of advanced topics in multiple regression and analysis of variance that can be used to support a more extensive coverage of the material currently appearing in print in Chapters 14 and 15.

• Chapter-ending Technology Notes on JMP, Minitab, SPSS, Microsoft Excel 2007, TI-83/84, and TI-nspire provide helpful hints and guidance on completing tasks associated with a particular chapter, as well as display screens to help students visualize and better understand the steps. More complete technology manuals are available on the text website.

• For instructors who prefer a briefer and more informal treatment of probability, two chapters previously from the book, "Statistics: The Exploration and Analysis of Data" by Roxy Peck and Jay Devore are available as a custom option. Please contact your Cengage Learning Consultant for more information about this alternative and other alternative customized options available to you. (See the information listed below under the heading “Alternate TOC” for specific coverage in the two chapters.)

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

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