<DIV><DIV>Excellent text emphasizes inferential and decision-making statistics. Discusses calculus of probability, sampling procedures, bivariate problems, much more. Problems. Answers.</DIV></DIV>
Introduction to Statistical Inference
β Scribed by Jerome C.R. Li
- Year
- 1957
- Tongue
- English
- Leaves
- 561
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
- INTRODUCTION......Page 15
2. DESCRIPTIVE STATISTICS......Page 17
3.
THE NORMAL DISTRIBUTION......Page 28
4.
SAMPLING EXPERJMENTS......Page 37
5. SAMPLE MEAN......Page 43
6.
TEST OF HYPOTHESIS......Page 57
7. SAMPLE VARIANCE X2-DISTRIDUTION......Page 73
8.
STUDENT'S DISTRIBUTION......Page 101
9. VARIANCE-RATIO-F DISTRIBUTION......Page 119
10.
DIFFERENCE BETWEEN SAMPLE MEANS......Page 133
11.
CONFIDENCE INTERVAL......Page 155
12. ANALYSIS OF VARIANCE-ONE-WAY CLASSIFICATION......Page 165
13. REVIEW......Page 202
14. RANDOMIZED BLOCKS......Page 210
15. TESTS OF SPECIFIC HYPOTHESIS IN THE ANALYSIS OF VARIANCE......Page 235
16.
LINEAR REGRESSION-I......Page 258
17.
LINEAR REGRESSION-II......Page 288
18.
FACTORIAL EXPERIMENT......Page 323
19.
ANALYSIS OF COVARIANCE......Page 358
20.
REVIEW......Page 398
21. SAMPLING FROM BINOMIAL POPULATION......Page 404
22.
SAMPLING FROM MULTINOMIAL POPULATION......Page 446
23.
SOME COMMONLY USED TRANSFORMATIONS......Page 461
24. DISTRIBUTION-FREE METHODS......Page 483
App.: Tables......Page 501
INDEX TO SUBJECT MATTER......Page 549
π SIMILAR VOLUMES
This excellent text emphasizes the inferential and decision-making aspects of statistics. The first chapter is mainly concerned with the elements of the calculus of probability. The second chapter contains the essential statistical techniques of summarizing the data in a sample prior to making infer
<p>This book is based upon lecture notes developed by Jack Kiefer for a course in statistical inference he taught at Cornell University. The notes were distributed to the class in lieu of a textbook, and the problems were used for homework assignments. Relying only on modest prerequisites of probabi
<p><span>The complexity of large-scale data sets (βBig Dataβ) has stimulated the development of advanced <br>computational methods for analysing them. There are two different kinds of methods to aid this. The <br>model-based method uses probability models and likelihood and Bayesian theory, while th