# Multiple Regression Forecasting

## Excel multiple regression analysis and forecasting template

The Excel Multiple Regression Analysis and Forecasting Template provides a basis for identifying causal and predictive relationships within series of datasets to provide statistically confident and reliable forecasting solutions. The multiple regression process employs a set of established statistical measures to ensure the empirical validity of the regression analysis. Regression results are summarized in explanatory text to facilitate interpretation and simplify the identification appropriate predictive relationships. The forecasting solution provides analysis on several methodologies that can then be utilized for forecasting independent variables for predicting and forecasting with the predictive analytics and regression equation.

Key features of the Excel Multiple Regression Analysis and Forecasting template include:
• The simple and intuitive workflow allows for sound data forecasts to be developed in a timely manner. The generic nature of the design allows any type of data to be regressed and forecast including business and financial data, time series and scientific data. Common business applications for predictive analytics via multivariate regression include real estate or market valuation and sales forecasting.
• The regression input data is analyzed and checked for numerical values before processing to ensure accuracy and avoid unobserved calculation errors. Categorical and logistic (binary) data variables are detected and proxy numerical equivalent mappings are used for regression analysis.
• Configurable feature selection isolates the best combination of variables to best fit the regression for maximum accuracy and persistence of predictions.
• The multiple regression analysis results are displayed in user friendly explanations without requiring statistical knowledge to interpret and use.
• The regression output analysis presents detailed results for both multiple regression and individual regression statistics for each independent variable including standard errors t-statistics and p-values. Statistics for isolated independent variables are useful for testing and removing variables which do not have a significant predictive relationship.
• Statistical tests for significance, autocorrelation and multicollinearity are calculated, displayed and explained to be easily interpreted and acted on in order to understand and optimize the validity of the regression analysis.
• The forecasting process is streamlined 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 input can be left empty to use external forecast data or alternative assumptions.
 Try It Get It Help Requirements - Excel 97-2016 - Excel 2004, 2011 or 2016 USD 19.00 Secure Processing (Download updated on 2018-03-30) User Guide

### Multiple Regression Data Input

Variable data for the Excel multiple regression analysis and forecasting template is entered in the provided input area with the first column for the dependent variable to be predicted and subsequent columns for independent variables. The number of variables and observations for the analysis is unlimited and variables can be turned on or off for the regression. Automatic feature selection analyzes relationships using a specified Variance Inflation Factor (VIF) to optimize the combination of variables that produces the most robust predictive outcome. Variable titles are used within the multiple regression analysis equation and forecasting for referencing the data. The overview window explains the mathematics behind multiple regression analysis and how it applies to the predictive analytics within the Excel solution.

### Multiple Regression Analysis Results

The multiple regression analysis results are summarized into distinct sections with textual commentary to facilitate interpretation and utilization for predictive analysis. The equation parameters convey the statistical significance and level of confidence that can be attributed to the analysis. The significance level based on the F-statistic is set at 95% confidence to quickly determine whether the analysis can be accepted at this level. The level of maximum confidence is displayed to supplement the determining of the accepted validity for the regression equation. Each independent variable in the regression analysis equation is broken down into detailed analysis for its contributory effect on the movements of the dependent variable. Further analysis reveals the potential levels of autocorrelation and multicollinearity so that independent variables can be dropped and added on an iterative basis toward the optimal predictive equation. The actual and predicted dependent variable is plotted in a chart in order to visualize the strength of the overall relationship.

### Regression Equation Predictive Forecasting

During the multiple regression analysis process each independent variable is analyzed for underlying trends to provide forecasting options for predictive analysis under the regression equation. The results of this analysis show a comparison of linear, polynomial and exponential methods with the relative strength so that they can be chosen and employed for the forecasting process. The independent variable forecast can also be left empty where forecasts have been undertaken or acquired from external sources. The number of periods can be set for forecasting the dependent variable by applying the multiple regression equation and forecasts of independent variables. The resulting output displays predicted value for the independent variable and input values from each independent variable. The prediction equation is used as formula in the column for predicted values so that data can be modified and new predictions analyzed. The forecast output can be exported to a new workbook using the button provided for further analysis, presentation or integration with external systems.

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