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Basic business statistics: Concepts and applications/ by Mark L Berenson and David M. Levine.

By: Contributor(s): Material type: TextTextPublication details: --U.S.A: Prentice-Hall International, Inc., c1996.Edition: Sixth editionDescription: xxv, I-7 28.8cmISBN:
  • 0-13-317737-8
DDC classification:
  • Sixth edition Bcir. 519.5 B425b 1996
Contents:
Chapter 1. Introduction --2. Data collection --3. Presenting numerical data in tables and charts --4. Summarizing and describing numerical data --5. Presenting categorical data in tables and charts --6. Basic probability --7. Some important discrete probability distributions --8. The normal distribution --9. Sampling distributions --10. Estimation --11. Fundamentals of hypothesis testing --12. One- sample tests with numerical data --13. Two-sample tests with numerical data --14. ANOVA and other c-sample tests with numerical data --15. Hypothesis testing with categorical data --16. Statistical applications in quality and productivity management --17. The simple linear regression model and correlation --18. Multiple regression models --19. Time series forecasting.
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Item type Home library Call number Status Date due Barcode
Bansalan Circulation1 UM Bansalan College LIC BCir. 519.5 B425b 1996 (Browse shelf(Opens below)) Available 1938

Includes appendices and index

Chapter 1. Introduction --2. Data collection --3. Presenting numerical data in tables and charts --4. Summarizing and describing numerical data --5. Presenting categorical data in tables and charts --6. Basic probability --7. Some important discrete probability distributions --8. The normal distribution --9. Sampling distributions --10. Estimation --11. Fundamentals of hypothesis testing --12. One- sample tests with numerical data --13. Two-sample tests with numerical data --14. ANOVA and other c-sample tests with numerical data --15. Hypothesis testing with categorical data --16. Statistical applications in quality and productivity management --17. The simple linear regression model and correlation --18. Multiple regression models --19. Time series forecasting.

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