Regression analysis : understanding and building business and economic models using Excel /
Wilson, J. Holton, 1942-,
Regression analysis : understanding and building business and economic models using Excel / J. Holton Wilson, Barry P. Keating, and Mary Beal. - Second edition. - New York, New York (222 East 46th Street, New York, NY 10017) : Business Expert Press, (c)2016. - 1 online resource (vi, 194 pages) - Quantitative approaches to decision making collection, .
1. Background issues for regression analysis -- 2. Introduction to regression analysis -- 3. The ordinary least squares (OLS) regression model -- 4. Evaluation of ordinary least squares (OLS) regression models -- 5. Point and interval estimates from a regression model -- 6. Multiple linear regression -- 7. A market share multiple regression model -- 8. Qualitative events and seasonality in multiple regression models -- 9. Nonlinear regression models -- 10. Abercrombie and Fitch and jewelry sales regression case studies -- 11. The formal ordinary least squares (OLS) regression model -- Appendix. Some statistical background -- Index.
This book covers essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. It is especially useful for those engaged in working with numbers--preparing forecasts, budgeting, estimating the effects of business decisions, and any of the forms of analytics that have recently become so useful. This book is a nontheoretical treatment that is accessible to readers with even a limited statistical background. This book specifically does not cover the theory of regression; it is designed to teach the correct use of regression, while advising the reader of its limitations and teaching about common pitfalls. It is useful for business professionals, MBA students, and others with a desire to understand regression analysis without having to work through tedious mathematical/statistical theory. This book describes exactly how regression models are developed and evaluated. Real data are used, instead of contrived textbook-like problems. The data used in the book are the kind of data managers are faced with in the real world. Included are instructions for using Microsoft Excel to build business/economic models using regression analysis with an appendix using screen shots and step-by-step instructions. Completing this book will allow you to understand and build basic business/economic models using regression analysis. You will be able to interpret the output of those models and you will be able to evaluate the models for accuracy and shortcomings. Even if you never build a model yourself, at some point in your career it is likely that you will find it necessary to interpret one; this book will make that possible.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat reader.
9781631573866
Microsoft Excel (Computer file)
Regression analysis.
Econometric models.
Regression analysis ordinary least squares (OLS) time-series data cross-sectional data dependent variables independent variables point estimates interval estimates hypothesis testing statistical significance confidence level significance level p-value R-squared coefficient of determination multicollinearity correlation serial correlation seasonality qualitative events dummy variables nonlinear regression models market share regression model Abercrombie & Fitch Co.
QA278 / .R447 2016
Regression analysis : understanding and building business and economic models using Excel / J. Holton Wilson, Barry P. Keating, and Mary Beal. - Second edition. - New York, New York (222 East 46th Street, New York, NY 10017) : Business Expert Press, (c)2016. - 1 online resource (vi, 194 pages) - Quantitative approaches to decision making collection, .
1. Background issues for regression analysis -- 2. Introduction to regression analysis -- 3. The ordinary least squares (OLS) regression model -- 4. Evaluation of ordinary least squares (OLS) regression models -- 5. Point and interval estimates from a regression model -- 6. Multiple linear regression -- 7. A market share multiple regression model -- 8. Qualitative events and seasonality in multiple regression models -- 9. Nonlinear regression models -- 10. Abercrombie and Fitch and jewelry sales regression case studies -- 11. The formal ordinary least squares (OLS) regression model -- Appendix. Some statistical background -- Index.
This book covers essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. It is especially useful for those engaged in working with numbers--preparing forecasts, budgeting, estimating the effects of business decisions, and any of the forms of analytics that have recently become so useful. This book is a nontheoretical treatment that is accessible to readers with even a limited statistical background. This book specifically does not cover the theory of regression; it is designed to teach the correct use of regression, while advising the reader of its limitations and teaching about common pitfalls. It is useful for business professionals, MBA students, and others with a desire to understand regression analysis without having to work through tedious mathematical/statistical theory. This book describes exactly how regression models are developed and evaluated. Real data are used, instead of contrived textbook-like problems. The data used in the book are the kind of data managers are faced with in the real world. Included are instructions for using Microsoft Excel to build business/economic models using regression analysis with an appendix using screen shots and step-by-step instructions. Completing this book will allow you to understand and build basic business/economic models using regression analysis. You will be able to interpret the output of those models and you will be able to evaluate the models for accuracy and shortcomings. Even if you never build a model yourself, at some point in your career it is likely that you will find it necessary to interpret one; this book will make that possible.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat reader.
9781631573866
Microsoft Excel (Computer file)
Regression analysis.
Econometric models.
Regression analysis ordinary least squares (OLS) time-series data cross-sectional data dependent variables independent variables point estimates interval estimates hypothesis testing statistical significance confidence level significance level p-value R-squared coefficient of determination multicollinearity correlation serial correlation seasonality qualitative events dummy variables nonlinear regression models market share regression model Abercrombie & Fitch Co.
QA278 / .R447 2016