Data Analysis, Regression and Forecasting / Edition 1

Data Analysis, Regression and Forecasting / Edition 1

by David E. Bell, Arthur Schleifer
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Cengage Learning

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Data Analysis, Regression and Forecasting / Edition 1

This book contains many classic Harvard cases and offers contemporary concept development. Its low cost makes it an ideal bundle with other Duxbury titles. It is appropriate for short courses in MBA-level statistics and as a supplement in more comprehensive courses. Emphasizing the practice of data analysis, the authors teach the methodology needed to solve a variety of commonly occurring real-world problems that managers encounter daily. Readers learn how to make inferences from limited data, forecast sales in appropriate ways, and avoid potentially disastrous errors of caustic reasoning.

Product Details

ISBN-13: 9781565272736
Publisher: Cengage Learning
Publication date: 10/28/1994
Series: Managerial Decision Analysis Ser.
Edition description: New Edition
Pages: 272
Product dimensions: 8.06(w) x 9.97(h) x 0.50(d)

Table of Contents

1. DATA ANALYSIS AND STATISTICAL DESCRIPTION. Sources and Arrangements of Data. Purposes of Data Analysis. Description of One Variable. Description of Two or More Variables. Age as an Independent Variable: Life-Cycle vs. Cohort Effects. Logarithms and Multiplicative Effects. Appendix: Measures of Centrality as Solutions to Decision Problems. Exercises on Interpreting Data. Worked Examples in Data Analysis Using Spreadsheets. Boston Edison vs. City of Boston. Hygiene Industries. The Stride-Rite Corporation (A). 2. SAMPLING AND STATISTICAL INFERENCE. Introduction. Sampling in the Real World. Appendix: Elements of Sampling Theory. Exercises on Sampling and Statistical Inference. 3. TIME SERIES. Introduction. Concepts Used in Data Generation Rules. Two Data Generation Rules. What Comes Next? Exercises on Time Series. The Boston Gas Company: Winter 1980-81. Appendix: The Moving-Average Temperature Distribution. 4. FORECASTING WITH REGRESSION ANALYSIS. Indistinguishable and Distinguishable Data. A Regression Model. Inputs to a Regression Analysis. Outputs from a Regression Analysis. Forecasts. Measures of Goodness of Fit. Transformed Variables. Using the Regression Utility. Doing Regression Analysis. Exercises on Forecasting with Regression. Chemplan Corporation: Paint-Rite Division. Harmon Foods, Inc. Highland Park Wood Company. CENEX. CFS Site Selection at Shell Canada Ltd. Firestone Tire & Rubber Company: The Industry Replacement Passenger Tire Forecast. Data Resources, Inc.: Note on Econometric Models. 5. CAUSAL INFERENCE. Introduction. What is Causation? Observational Data. An Example. Which Independent Variables Should Be Included? How to Identify theRelevant Independent Variables. Exercise on Causal Inference. The Gotham Giants. Nopane Advertising Strategy. Lincoln Community Hospital. 6. MULTIPLICATIVE REGRESSION MODELS. Introduction. An Example. Problems with the Linear Models. Summary. Exercise. Barbara J. Key vs. The Gillette Company (A). Barbara J. Key vs. The Gillette Company (B).

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Data Analysis, Regression and Forecasting 1 out of 5 based on 0 ratings. 1 reviews.
Guest More than 1 year ago
Book is built around spreadsheets on a data diskette. Book states on pg xxiv in the section 'To the Student' 'If your instructor does not provide you with the disk, it may be obtained from...., by calling...or sending a FAX ....' Current (parent) publisher is Thomson who will not honor books written message making book useless with regard to its intent. Don't waste your time or money.