General Linear Model

Overview | How to | Example

 

 

Overview

General Linear Model (GLM) is used to perform univariate analysis of variance with balanced and unbalanced designs and regression for the response variable. It is commonly used to analyze data of experimental designs. The residuals and fitted values of the response are shown in Columns AA:AB.

Response variables and terms must be presented in columns. Covariates must be numeric and factors must have at least 2 distinct levels. The Model can include factors, covariates, interactions and nested term. Multiple terms must be separated by commas. Use * to represent interactions and () to represent nesting. The model must be hierachical, if an interaction term is included, all lower order interactions and main terms must also be included.

 

 

How to

At the Excel Menu (For Excel 2007, go to Add-ins first)

  1. Choose ProcessMA > Statistics > ANOVA > General Linear Model

  2. In Response, select the column containing the response data

  3. In Term 1 to Term 10 , select the column containing data for the respective model terms and choose if the term is a covariate or a factor (Covariate: Numeric)

  4. In Model, enter the terms to be included in the model. Multiple terms must be separated by commas. Use * to represent interactions and () to represent nesting. Model must be hierachical

  5. Check Plot Residuals, to display residual plots

  6. Click OK

 

 

Example

You conducted an experiment to study the effect of material type and blade size on the wind generated by an electric fan. There are 3 types of materials and 3 blade sizes. The material type is a fixed factor and the blade size is a covariate.

  1. Open data worksheet by choosing ProcessMA > Tools > Data

  2. Choose ProcessMA > Statistics > ANOVA > General Linear Model

  3. In Response, select BD - Wind

  4. In Term 1, select BE - Material and choose Factor

  5. In Term 2, select BF - BladeSize and choose Covariate

  6. In Model, enter Material, BladeSize, Material*BladeSize

  7. Click OK

 

Results & Interpretation

From the ANOVA table, all the p-values are less than 0.005 which indicate that there are significant evidences on the effects. The R-sq shows that the model explains 89.7% of the variance in wind.

 

 

Copyright © ProcessMA