Business Statistics for Competitive Advantage with Excel 2007: Basics, Model Building and Cases

Author:   Cynthia Fraser
Publisher:   Springer-Verlag New York Inc.
Edition:   2009
ISBN:  

9780387744025


Pages:   432
Publication Date:   01 October 2008
Replaced By:   9781441998569
Format:   Paperback
Availability:   Awaiting stock   Availability explained


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Business Statistics for Competitive Advantage with Excel 2007: Basics, Model Building and Cases


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Overview

This book reviews basics then develops modeling with numerous examples from decision making. Translation of results into business English insures both students' understanding of their ramifications as well as the use of results by decision makers without statistics backgrounds. Analyses are illustrated with graphics.

Full Product Details

Author:   Cynthia Fraser
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2009
Dimensions:   Width: 17.80cm , Height: 2.20cm , Length: 25.40cm
Weight:   1.650kg
ISBN:  

9780387744025


ISBN 10:   0387744029
Pages:   432
Publication Date:   01 October 2008
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Replaced By:   9781441998569
Format:   Paperback
Publisher's Status:   Out of Print
Availability:   Awaiting stock   Availability explained

Table of Contents

Statistics for decision making and competitive advantage.- Describing your data.- Hypthesis tests, confidence intervals and simulation to infer population characteristics.- Quantifying the influence of performance drivers and forecasting: regression.- Marketing segmentation with descriptive statistics, inference, hypothesis tests and regression.- Finance application: portfolio analysis with a market index as a leading indicator in simple linear regression.- Association between two categorical variables: contingency analysis with chi-square.- Building multiple regression models.- Model building and forecasting with multicollinear time series.- Indicator variables.- Nonlinear multiple regression models.- Indicator interactions for structural differences or changes in response.- Logit regression for bounded responses.- Index.

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