Published by Pearson (October 21, 2016) © 2017
Ronald Walpole | Raymond Myers | Sharon Myers | Keying YeProduct Information
For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.
This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.
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- Preface
- 1. Introduction to Statistics and Data Analysis
- 2. Probability
- 3. Random Variables and Probability Distributions
- 4. Mathematical Expectation
- 5. Some Discrete Probability Distributions
- 6. Some Continuous Probability Distributions
- 7. Functions of Random Variables (Optional)
- 8. Sampling Distributions and More Graphical Tools
- 9. One- and Two-Sample Estimation Problems
- 10. One- and Two-Sample Tests of Hypotheses
- 11. Simple Linear Regression and Correlation
- 12. Multiple Linear Regression and Certain Nonlinear Regression Models
- 13. One-Factor Experiments: General
- 14. Factorial Experiments (Two or More Factors)
- 15. 2k Factorial Experiments and Fractions
- 16. Nonparametric Statistics
- 17. Statistical Quality Control
- 18 Bayesian Statistics
- Bibliography
- A. Statistical Tables and Proofs
- B. Answers to Odd-Numbered Non-Review Exercises
- Index