Statistical Methods for Managerial Decisions: AND Harvard Cases: A Case-Based Approach

Statistical Methods for Managerial Decisions: AND Harvard Cases: A Case-Based Approach

Mixed media product

By (author) Peter Klibanoff, By (author) Alvaro Sandroni, By (author) Boaz Moselle, By (author) Brett Saraniti

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  • Publisher: South-Western
  • Format: Mixed media product | 256 pages
  • Dimensions: 202mm x 250mm x 10mm | 558g
  • Publication date: 28 October 2005
  • Publication City/Country: Mason, OH
  • ISBN 10: 0324314469
  • ISBN 13: 9780324314465
  • Edition statement: International edition
  • Sales rank: 1,323,256

Product description

This book helps you discover everything you need to prepare for success in business statistics today with this advanced, case-based approach to regression analysis. You'll begin by reviewing basic probability before moving into a strong topical coverage of hypothesis testing and regression analysis with an emphasis on relevant examples, business cases, and applications. Leading Harvard Business School cases and numerous end-of-chapter cases and problems written by the authors illustrate the use of statistics and regression analysis in business today.

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1. Introduction to Probability Distributions: The Double E Case. 2. Hypothesis Testing: The Consumer Packaging Case. 3. Introduction to Regression: The Autorama Case. 4. Using Regression: The CAPM and Newspaper Cases. Case Insert 1 The Refrigerator Pricing Case: Introduction to Multiple Regression. 5. Dummy and Slope-Dummy Variables: The California Strawberries and CEO Seek Cases. 6. Graphical Analysis, Non-Linear Regression and Spurious Correlation: The Forestier Wine Case, Snowfall and Unemployment. 7. Multiple Regression, Multicollinearity and the Generalized F-test: The Hot Dog Case. Case Insert 2 Colonial Broadcasting: Multiple Regression and Omitted Variable Bias. 8. Non-Linear Regression, Logarithms and Heteroskedasticity: An Advertising Example, The Hot Dog Case Revisited. 9. Time and Seasonality in Multiple Regression: The Dada Soda and Harmon Foods Cases. Case Insert 3 Nopane Advertising Case: Multiple Regression and Interaction Variables. Case Insert 4 The Wrigley Case: Multiple Regression and Modeling. Appendices. A Kstat Mini-Manual. Prediction Intervals. Correlation Review. Simple Properties Of Logarithms.

Table of contents

1. Introduction to Probability Distributions: The Double E Case. 2. Hypothesis Testing: The Consumer Packaging Case. 3. Introduction to Regression: The Autorama Case. 4. Using Regression: The CAPM and Newspaper Cases. Case Insert 1 The Refrigerator Pricing Case: Introduction to Multiple Regression. 5. Dummy and Slope-Dummy Variables: The California Strawberries and CEO Seek Cases. 6. Graphical Analysis, Non-Linear Regression and Spurious Correlation: The Forestier Wine Case, Snowfall and Unemployment. 7. Multiple Regression, Multicollinearity and the Generalized F-test: The Hot Dog Case. Case Insert 2 Colonial Broadcasting: Multiple Regression and Omitted Variable Bias. 8. Non-Linear Regression, Logarithms and Heteroskedasticity: An Advertising Example, The Hot Dog Case Revisited. 9. Time and Seasonality in Multiple Regression: The Dada Soda and Harmon Foods Cases. Case Insert 3 Nopane Advertising Case: Multiple Regression and Interaction Variables. Case Insert 4 The Wrigley Case: Multiple Regression and Modeling. Appendices. A Kstat Mini-Manual. Prediction Intervals. Correlation Review. Simple Properties Of Logarithms.