catalog
Home
>

Higher Education

product-details

Mathematical Statistics and Data Analysis

Author(s): John Rice

ISBN: 9788131519547

3rd Edition

Copyright: 2013

India Release: 2013

₹835

Binding: Paperback

Pages: 684

Trim Size: 279 x 216 mm

Refer Book

Order Inspection Copy

'This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings

  • Introduction to the bootstrap method as a simple yet powerful tool that is integrated with general inferential procedures (Monte Carlo methods are also introduced.)
  • Many exercises that enrich the book. (Some are relatively simple and reinforce calculations. Others concern bootstrap and Monte Carlo methods and theoretical material on survey sampling. Many incorporate use of the computer.)
  • Nearly 100 new problems, including many that are substantial enough to form the basis for computer labs.
  • Examples from contemporary, high interest areas such as genomics and financial statistics complement already interesting existing applications (e.g., probability of AIDS infection, state lotteries, polygraph testing) and graphical displays.
  • There is new treatment of the topic of loglinear smoothing.
  • Treatment of Bayesian inference is now presented in parallel with frequentist methods.

1. Probability.

2. Random variables.

3. Joint distributions.

4. Expected values.

5. Limit theorems.

6. Distributions derived from the normal distribution.

7. Survey sampling.

8. Estimation of parameters and fitting of probability distributions.

9. Testing hypotheses and assessing goodness of fit.

10. Summarizing data.

11. Comparing two samples.

12. The analysis of variance.

13. The analysis of categorical data.

14. Linear least squares.

15. Decision theory and bayesian inference.

Appendix A. Common Distributions.

Appendix B. Tables.

Bibliography.

Answers to Selected Problems.

Author Index.

Index to Data Sets.

Subject Index.

John A. Rice

University of California, Berkeley