Common approaches include examining residual plots, using lack-of-fit tests, and viewing the correlation between predictors using the Variance Inflation Factor (VIF). It is best to avoid imperfectly high correlations (multicollinearity) as well.īecause OLS regression will provide the best estimates only when all the assumptions are met, it is very important to test them.

No predictor variable is perfectly correlated (r=1) with a different predictor variable.Residuals are not correlated with each other (serial correlation).We are prolific in most of the statistical soft wares such as SPSS, R, Minitab, STATA, Excel and SAS amongst others. All predictors are uncorrelated with the residuals. Data analysis Our team of experts at Statistics Guru can help you or conduct the regression analysis themselves.Inclusion of a constant in the model will force the mean to equal zero. Here, the predictor variable, X, is squared in order to model the curvature.

You must specify the correct functional form in order to model any curvature.

Least squares can model curvature by transforming the variables (instead of the coefficients). After this information appears MINITAB produces an analysis of variance box.

The regression model is linear in the coefficients. A simple equation specifies the two variable linear regression model.

OLS regression provides the most precise, unbiased estimates only when the following assumptions are met:

Regression analysis minitab