TY  - BOOK
AU  - Wilson,J.Holton
AU  - Keating,Barry P.
AU  - Beal-Hodges,Mary
TI  - Regression analysis: understanding and building business and economic models using Excel 
T2  - Quantitative approaches to decision making collection,
SN  - 9781606494356 (electronic bk.)
AV  - QA278.2 .W557 2012
U1  - 519.536 23
PY  - 2012///
CY  - [New York, N.Y.] (222 East 46th Street, New York, NY 10017)
PB  - Business Expert Press
KW  - Microsoft Excel (Computer file)
KW  - Regression analysis
KW  - Econometric models
KW  - ordinary least squares (OLS)
KW  - time-series data
KW  - cross-sectional data
KW  - dependent variables
KW  - independent variables
KW  - point estimates
KW  - interval estimates
KW  - hypothesis testing
KW  - statistical significance
KW  - confidence level
KW  - significance level
KW  - p-value
KW  - R-squared
KW  - coefficient of determination
KW  - multicollinearity
KW  - correlation
KW  - serial correlation
KW  - seasonality
KW  - qualitative events
KW  - dummy variables
KW  - non-linear regression models
KW  - market share regression model
KW  - Abercrombie & Fitch Co
KW  - Electronic books
N1  - Part of: 2012 digital library; Includes index; 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 & Fitch Co. regression case study -- 11. The formal ordinary least squares (OLS) regression model -- Appendix. Some statistical background -- Index; Access restricted to authorized users and institutions; Also available in print; Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries
N2  - 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
UR  - https://ebookcentral.proquest.com/lib/bcsl-ebooks/detail.action?docID=1048404
ER  -