Loose-leaf Version for Discovering Statistics

Dan Larose’s Discovering Statistics is the ideal text for instructors who want to teach the basics of statistical computation as well as how to interpret and apply the results of those computations. Using real data, contemporary examples, step-by-step solutions, extensive pedagogy, and support for common statistical software options, the text familiarizes students with essential computational skills, while helping them build the conceptual understanding needed to interpret and explain their findings. As a result, Discovering Statistics helps students develop a solid and lasting statistical sense that will prove valuable throughout their academic and professional careers.

Since his days of collecting baseball cards as a youngster, Dan Larose has felt a lifelong passion for statistics. He completed his PhD in statistics from the University of Connecticut in Storrs in 1996. Today, Larose is Professor of Statistics in the Department of Mathematical Sciences at Central Connecticut State University. There, he designed, developed, and directs the world’s first online Master of Science degree in data mining. He has published three books on data mining, and is a consultant in statistics and data mining. His fondest wish is to impart a love of statistics to a new generation. Larose lives in Tolland, Connecticut, with his wife Debra, daughters Chantal and Ravel, and son, Tristan.

1. The Nature of Statistics

2. Describing Data Using Graphs and Tables

3. Describing Data Numerically

4. Correlation and Regression

5. Probability

6. Probability Distributions

7. Sampling Distributions

8. Confidence Intervals

9. Hypothesis Testing

10. Two-Sample Inference

11. Categorical Data Analysis

12. Analysis of Variance

13. Inference in Regression

Available only online: www.macmillanhighered.com/discostat3e

14. Nonparametric Statistics