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    Managerial Statistics: 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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    DescriptionDiscover 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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  • Full bibliographic data for Managerial Statistics: AND Harvard Cases

    Title
    Managerial Statistics: AND Harvard Cases
    Subtitle
    A Case-based Approach
    Authors and contributors
    By (author) Peter Klibanoff, By (author) Alvaro Sandroni, By (author) Boaz Moselle, By (author) Brett Saraniti
    Physical properties
    Format: Mixed media product
    Number of pages: 256
    Width: 202 mm
    Height: 250 mm
    Thickness: 10 mm
    Weight: 558 g
    Language
    English
    ISBN
    ISBN 13: 9780324314465
    ISBN 10: 0324314469
    Classifications

    BIC E4L: BUS
    Nielsen BookScan Product Class 3: S4.1
    BIC subject category V2: PBT, KJMD
    BISAC V2.8: MAT029000
    DC22: 658.4033
    Thema V1.0: KJMD
    Edition statement
    International ed
    Publisher
    Cengage Learning, Inc
    Imprint name
    South-Western
    Publication date
    27 November 2005
    Publication City/Country
    Mason, OH
    Review quote
    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.