• Originally published: 04/07/2018 14:32
Publication number: ELQ-78624-1
View all versions & Certificate # Multivariate Regression Analysis Excel Model with Forecasting

The Excel multivariate regression analysis performs multiple linear regression analysis for forecasting and prediction.

Description
The Excel multivariate regression analysis performs multiple linear regression analysis on large sets of variables to identify casual and influential relationships. Statistical tests are explained in simple text for fast interpretation and utilization for predictive analysis and forecasting. The identified and statistically robust prediction equation can be automatically applied to variable data to produce predictions and forecasts.

The Excel multivariate regression analysis provides the automatic identification of predictor variables through multiple regression analysis and advanced statistical tests. Regression results are presented in a simple and easy to understand format to quantify the relative influence of each input variable supporting both continuous and categorical variables. The work flow facilitates and iterative process to test, maintain and discard variables until a prediction regression equation can be established with maximum confidence. Supplementary statistical analysis to reveal underlying data relationships include autocorrelation under the Dubin-Watson statistic and multicollinearity between individual independent variables. Standard tests include F statistic confidence intervals, adjusted R-squared, standard errors, t-test statistics and p values. The resulting prediction regression equation can subsequently applied to integrated forecasting methods or custom data for the independent variables to produce predictions and forecasts of desired period length. Built-in forecasting options for predictive analysis include linear, polynomial and exponential methodologies.

This Best Practice includes
1 Excel file, online user guide

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## Further information

### Objectives

Forecast through statistically robust multivariate regression techniques

### Use it if

Create forecasts where observation data is available

### Don't use it if

Forecasting where relationships are non-linear or data is not available

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