Table of Contents

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. More on Observational Studies: Designing Surveys (Optional). Interpreting and Communicating the Results of Statistical Analyses.
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. 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. Logistic Regression (Optional). Interpreting and Communicating the Results of Statistical Analyses.
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 DISTRIBUTION.
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.
10. HYPOTHESIS TESTING USING A SINGLE SAMPLE.
Hypotheses and Test Procedures. Errors in Hypotheses Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypotheses Tests for a Population Mean. Power and Probability of Type II Error. Interpreting and Communicating the Results of Statistical Analyses.
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 a Difference Between Two Population or Treatment Proportions. Interpreting and Communicating the Results of Statistical Analyses.
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 (Optional). Inferences About the Population Correlation Coefficient (Optional). Interpreting and Communicating the Results of Statistical Analyses.
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).
Activity 14.1: Exploring the Relationship Between Number of Predictors and Sample Size.
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.

About the Author

Chris Olsen taught statistics at George Washington High School in Cedar Rapids, Iowa, for over 25 years as well as at Cornell College and 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 11 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. 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. She is cofounder and principal engineer at Geosynergy, specializing in the quantification of uncertainty in seismic risk. Jay Devore is Professor Emeritus of Statistics at California Polytechnic State University. He earned his undergraduate degree in Engineering Science from the University of California at Berkeley, spent a year at the University of Sheffield in England, and finished his Ph.D. in statistics at Stanford University. Jay previously taught at the University of Florida and at Oberlin College and has had visiting appointments at Stanford, Harvard, the University of Washington, New York University, and Columbia University. From 1998 to 2006, he served as Chair of the Statistics Department. In addition to this book, Jay has written several widely used engineering statistics texts and a book in applied mathematical statistics. He recently coauthored a text in probability and stochastic processes. He is the recipient of a distinguished teaching award from Cal Poly, is a Fellow of the American Statistical Association , and has served several terms as an Associate Editor of the "Journal of the American Statistical Association." In his spare time, he enjoys reading, cooking and eating good food, tennis, and travel to faraway places. He is especially proud of his wife, Carol, a retired elementary school teacher, his daughter Allison, who has held several high-level positions in nonprofit organizations in Boston and New York City, and his daughter Teresa, an ESL teacher in New York City. Roxy Peck is Emerita Associate Dean 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 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.