Essentials of business statistics
著者
書誌事項
Essentials of business statistics
McGraw-Hill/Irwin, c2012
4th ed., international student ed
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注記
"McGraw-Hill international edition" -- Cover
Includes bibliographical references (p. 657-658) and indexes
内容説明・目次
内容説明
The new edition of Essentials of Business Statisticsdelivers clear and understandable explanations of core business statistics concepts, making it ideal for a one-term course in business statistics. Containing continuing case studies that emphasize the theme of business improvement, the text offers real applications of statistics that are relevant to today's business students. The authors motivate students by showing persuasively how the use of statistical techniques in support of business decision-making helps to improve business processes. A variety of examples and exercises, and a robust, technology-based ancillary package are designed to help students master this subject. In addition, the authors have rewritten many of the discussions in this edition and have explained concepts more simply from first principles. The only prerequisite for this text is high school algebra.
目次
1. An Introduction to Business Statistics
2. Descriptive Statistics: Tabular and Graphical Methods
3. Descriptive Statistics: Numerical Methods
4. Probability
5. Discrete Random Variables
6. Continuous Random Variables
7. Sampling and Sampling Distributions
8. Confidence Intervals
9. Hypothesis Testing
10. Statistical Inferences Based on Two Samples
11. Experimental Design and Analysis of Variance
12. Chi-Square Tests
13. Simple Linear Regression Analysis
14. Multiple Regression and Model Building
Appendix A: Statistical Tables
Answers to Most Odd-Numbered Exercises
References
Photo Credits
Index
On the Website:
15. Process Improvement Using Control Charts
Appendix B: Properties of the Mean and the Variance of a Random Variable and the Co-variance
Appendix C: Derivatives of the Mean and Variance of x(bar) and p(hat)
Appendix D: Confidence Intervals for Parameters of Finite Populations
Appendix E: Logistic Regression
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