Business applications of multiple regression [electronic resource] / Ronny Richardson.

By:

Richardson, Ronny

Publisher number: 2 | BEPSeries: Quantitative approaches to decision making collection[New York, N.Y.] (222 East 46th Street, New York, NY 10017) : Business Expert Press, 2011Edition: 1st edDescription: 1 electronic text (194 p.) : ill., digital fileContent type:

Media type:

Carrier type:

ISBN:

9781606492321 (electronic bk.)

Subject(s):

Additional physical formats: Print version:: No titleDDC classification:

658.4033

LOC classification:

HD30.215 .R524 2011

Online resources:

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Available additional physical forms:

Also available in print.

Contents:

Introduction -- Chapter 1.Correlation analysis -- Chapter 2.Simple regression -- Chapter 3.Multiple regression -- Chapter 4.Model building -- Notes -- Index.

Abstract: This book describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in detail to better prepare the reader for working with actual data in a business environment. This book will be a useful guide to managers at all levels who need to understand and make decisions based on data analysis performed using multiple regression. It also provides the beginning analyst with the detailed understanding required to use multiple regression to analyze data sets.

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Includes bibliographical references (p. 183-186) and index.

Introduction -- Chapter 1.Correlation analysis -- Chapter 2.Simple regression -- Chapter 3.Multiple regression -- Chapter 4.Model building -- Notes -- Index.

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This book describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in detail to better prepare the reader for working with actual data in a business environment. This book will be a useful guide to managers at all levels who need to understand and make decisions based on data analysis performed using multiple regression. It also provides the beginning analyst with the detailed understanding required to use multiple regression to analyze data sets.