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This guide is for an old version of Prism. Browse the latest version or update Prism

1. Create a data table

From the Welcome or New Table dialog, choose to create a multiple variable data table.

If you are just getting started, choose the sample data for multiple linear regression

2. Enter data

Each row represents a different individual, animal, experiment or something.

Each column represents a different observational unit, for example an individual, animal, or experimental replicate. All variables must be entered as numbers. Usually these will be continuous variables.

If you want to enter a categorical variable with two possible values you must encode the values (perhaps 1 for Male, 0 for Female) and can not enter category names directly.

If a categorical variable has three or more possible values, you'll have to do some extra work. The simplest approach is called dummy coding (also called indicator coding or reference coding), but there are other alternative codings such as effects coding. One good source to learn about these coding methods is Glantz and Slinker, cited below.

Note that there is no need to code interaction(s) manually. Prism will allow you to add interaction(s) automatically in the parameters dialog.

3. Run multiple regression

Click Analyze, choose multiple linear regression from the list of analyses for multiple variable tables. the multiple regression dialog has five tabs:

Model. Choose which variable is the dependent variable and which other variables to include as independent variables Also choose any interactions or transforms you wish to include in the model.

Compare. Choose a second model and specify how the fit of the two models should be compared.

Weighting. Usually all data are weighted equally, but you can specify another weighting scheme.

Diagnostics. Specify which results Prism should report.

Residuals. Plot the residuals (the difference between actual and predicted Y values) in several different ways.


Glantz and Slinker, Primer of Applied Regression and Analysis of Variance, 3rd edition, Chapter “Using linear regression to do one-way analysis of variance with any number of treatments”, page 391

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