Use General Linear Model to determine whether the means of two or more groups differ. You can include random factors, covariates, or a mix of crossed and nested factors. You can also use stepwise regression to help determine the model.

To study the effect of temperature, glass type, and the interaction between these two factors, the engineer uses a general linear model. Open the sample data, LightOutput.MTW . Choose Stat > ANOVA > General Linear Model > Fit General Linear Model .

General Linear Model uses a regression approach to fit the model that you specify. First Minitab creates a design matrix, from the factors and covariates, and the model that you specify. The columns of this matrix are the predictors for the regression.

Use Fit General Linear Model to fit least squares models when you have a continuous response, categorical factors, and optional covariates. You can include interaction and polynomial terms, crossed and nested factors, and fixed and random factors.

In a general linear model, factors can be either fixed or random. In general, if the investigator controls the levels of a factor, the factor is fixed. On the other hand, if the investigator randomly sampled the levels of a factor from a population, the factor is random.

Complete the following steps to interpret a general linear model. Key output includes the p-value, the coefficients, R 2, and the residual plots.

General Linear Model uses a regression approach to fit the model that you specify. First Minitab creates a design matrix, from the factors and covariates, and the model that you specify. The columns of this matrix are the predictors for the regression.

The model should provide a good fit to the data. If the model does not fit the data, the results can be misleading. In the output, use the residual plots, the diagnostic statistics for unusual observations, and the model summary statistics to determine how well the model fits the data.

Choose Stat > ANOVA > General Linear Model > Fit General Linear Model. In Responses, enter C3. In Factors, enter Factor1 Factor2. Click Model. In the field under Factors and covariates select both 'Factor 1' and 'Factor 2'. Verify that 2 is selected in the field beside Interactions through order. Click Add, then click OK. Click Storage. Check Fits.

Both AICc and BIC assess the likelihood of the model and then apply a penalty for adding terms to the model. The penalty reduces the tendency to overfit the model to the sample data. This reduction can yield a model that performs better in general.