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.