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Regression Forecasting

Relationship driven cause & effect analysis and forecasting

...Statistically sound value driver identification for forecasting.

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The Excel Multiple Regression Analysis and Forecasting template provides a basis for identifying value drivers and relationships within multiple data series to provide a statistically sound forecasting solution. The Excel template employs a set of recognized statistical measures to ensure validity of the regression analysis. Regression results are summarized in explanatory text to facilitate interpretation. When causal relationships have been identified, A forecasting tool based on several tested methodologies can be utilized when satisfactory causal relationships have been identified from the analysis.

Key features of the Excel Multiple Regression Analysis and Forecasting template include:

An intuitive workflow with minimal input requirement enables strong forecasts to be developed in a timely manner.
The regression input data is analyzed and checked for numerical values before processing to ensure accuracy and avoid unobserved calculation errors.
User friendly regression analysis results are displayed and explained for the 'Non-statistician'.
The regression output analysis presents both multiple and individual regression statistics for each independent variable.
Tests for statistical significance, autocorrelation and multicollinearity are calculated, displayed and explained in an easy to understand manner.
The forecasting process is simplified with options to employ 3rd polynomial, 2nd polynomial, exponential or linear trend lines on independent variables based on calculated statistical strength. Alternatively, independent variable forecast cells can be left empty to input existing or alternative assumptions and data.


Requirements Windows: Excel 97-2010 Mac OS X: Excel 2004 or 2011	(US $14.00) Secure Processing

Customers who bought the Excel Multiple Regression Analysis and Forecasting template also bought: Investment and Business Valuation, Project Planning and Management
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