The overview window explains the mathematics behind multiple regression analysis and how it applies to the predictive analytics within the Excel solution. Variable titles are used within the multiple regression analysis equation and forecasting for referencing the data. 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. The number of variables and observations for the analysis is unlimited and variables can be turned on or off for the regression. 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. Alternatively, independent variable forecast input can be left empty to use external forecast data or alternative assumptions. 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.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.Statistics for isolated independent variables are useful for testing and removing variables which do not have a significant predictive relationship. 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.The multiple regression analysis results are displayed in user friendly explanations without requiring statistical knowledge to interpret and use.Configurable feature selection isolates the best combination of variables to best fit the regression for maximum accuracy and persistence of predictions.Categorical and logistic (binary) data variables are detected and proxy numerical equivalent mappings are used for regression analysis. The regression input data is analyzed and checked for numerical values before processing to ensure accuracy and avoid unobserved calculation errors.Common business applications for predictive analytics via multivariate regression include real estate or market valuation and sales forecasting.
#EXCEL LINEAR REGRESSION MULTIPLE VARIABLES TEMPLATE SERIES#
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. The simple and intuitive workflow allows for sound data forecasts to be developed in a timely manner.
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