Table of Contents

PART I. DESIGNS OF EXPERIMENTS
Chapter 1. Controlled Experiments
Chapter 2. Observational Studies
PART II. DESCRIPTIVE STATISTICS
Chapter 3. The Histogram
Chapter 4. The Average Standard Deviation
Chapter 5. The Normal Approximation for Data
Chapter 6. Measurement Error
Chapter 7. Plotting Points and Lines
PART III. CORRELATION AND REGRESSION
Chapter 8. Correlation
Chapter 9. More about Correlation
Chapter 10. Regression
Chapter 11. The R.M.S. Error for Regression
Chapter 12. The Regression Line
PART IV. PROBABILITY
Chapter 13. What Are the Chances
Chapter 14. More about Chance
Chapter 15. The Binomial Formula
PART V. CHANCE VARIABILITY
Chapter 16. The Law of Averages
Chapter 17. The Expected Value and Standard Error
Chapter 18. The Normal Approximation for Probablity
PART VI. SAMPLING
Chapter 19. Sample Surveys
Chapter 20. Chance Errors in Sampling
Chapter 21. The Accuracy of Percentages
Chapter 22. Measuring Employment and Significance Unemployment
Chapter 23. The Accuracy of Averages
PART VII. CHANCE MODELS
Chapter 24. Model for Measurement Error
Chapter 25. Chance Models in Genetics
PART VIII. TESTS OF SIGNIFICANCE
Chapter 26. Tests of Significance
Chapter 27 More Tests for Averages
Chapter 28. The Chi-Square Test
Chapter 29. A Closer Look at Tests of Significance

About the Author

David Freedman received his B.Sc. from McGill and his Ph.D. from Princeton. He has worked as a consultant for the City of San Francisco, the County of Los Angeles, the U.S. Department of Energy, and the U.S. Department of Justice. He has written several previous books and numerous technical papers. He is a member of the American Academy of Arts and Sciences and teaches at the University of California, Berkeley.
Robert Pisani received his B.A. and Ph.D. at the University of California, Berkeley. His research interests include probability models of market-price behavior and the statistical valuation of financial instruments.
Roger Purves received his B.A. at the University of British Columbia and his Ph.D. at the University of California, Berkeley, where he currently teaches. His research interests are in the mathematical foundations of probability theory.