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999 _c2815
_d2815
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008 220701b ||||| |||| 00| 0 eng d
020 _a9789353940645
082 _a519.502465
_bSTI
100 _aStine, Robert A.
_97192
245 _aStatistics for business: decision making and analysis
250 _a3rd
260 _bPearson India Education Services Pvt. Ltd.
_aNoida
_c2022
300 _axxii, 830 p.
365 _aINR
_b950.00
504 _aTable of Content I. Variation 1. Introduction 2. Data 3. Describing Categorical Data 4. Describing Numerical Data 5. Association Between Categorical Variables 6. Association Between Quantitative Variables II. Probability 7. Probability 8. Conditional Probability 9. Random Variables 10. Association Between Random Variables 11. Probability Models for Counts 12. The Normal Probability Model III. Inference 13. Samples and Surveys 14. Sampling Variation and Quality 15. Confidence Intervals 16. Statistical Tests 17. Comparison 18. Inference for Counts IV. Regression Models 19. Linear Patterns 20. Curved Patterns 21. The Simple Regression Model 22. Regression Diagnostics 23. Multiple Regression 24. Building Regression Models 25. Categorical Explanatory Variables 26. Analysis of Variance 27. Time Series
520 _aThe 3rd Edition of Statistics for Business: Decision Making and Analysis emphasizes an application-based approach, in which students learn how to work with data to make decisions. In this contemporary presentation of business statistics, students learn how to approach business decisions through a 4M Analytics decision making strategy-motivation, method, mechanics and message-to better understand how a business context motivates the statistical process and how the results inform a course of action. Each chapter includes hints on using Excel, Minitab Express, and JMP for calculations, pointing the student in the right direction to get started with analysis of data.
650 _aCommercial statistics
_91041
650 _aBusiness planning
_9967
650 _aIndustrial management--Statistical methods
_91901
650 _aProblem solving--Statistical methods
_92097
942 _2ddc
_cBK