The Multiple Regression Analysis and Forecasting template enables the confident identification of value drivers and forecasting business plan or scientific data. The multiple regression process utilizes commonly employed statistical measures to test the validity of the analysis and results are summarized in text form to be easily understood. When predictive relationships have been identified by the regression analysis, forecasting can be quickly accomplished based on a range of available methodologies and accompanying statistical strength. An intuitive stepwise work flow enables to develop strong forecasts for projects in a timely manner. The Multiple Regression Analysis and Forecasting model provides simple and flexible input with integrated help icons to facilitate utilization. Results and statistics are explained in a user friendly manner to be understood by users of all levels of statistical expertize. The Multiple regression analysis and forecasting template provides much more functionality than the Excel Analysis Toolpak such as individual regression of all independent variables, the actual level of confidence for the results, and tests of for autocorrelation and multicollinearity. The forecasting process provides options to employ 3rd polynomial, 2nd polynomial, exponential or linear trend lines on independent variables as well as the option to override independent variable forecast data with external analysis. The Multiple Regression Analysis and Forecasting template is compatible with Excel 97-2010 for Windows and Excel 2011 or 2004 for Mac as a cross platform regression and forecasting solution.
Platform Windows 95/98/ME
Operating Systems Windows 95/98/ME,Windows NT/2000,Mac,Windows XP,OS X - Macintosh,Windows NT/2000/2003/SBS2003,Windows Vista,Windows 7
System Requirements Excel 97 or higher for Windows. Excel 2004 or 2011 for Mac OS X.
Date added 10 Jan 2003
Last Updated 11 Apr 2012
Tags multiple, regression, forecasting, analysis, excel, business, planning, statistical, multicolinerity, autocorrelation, correlation, r-squared, value, drivers, identification