Analysis of Variance | JMP 13.2

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Fitting Linear Models • Standard Least Squares Report and Options • Regression Reports • Analysis of Variance

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Analysis of Variance

The Analysis of Variance report provides the calculations for comparing the fitted model to a model where all predicted values equal the response mean.

Note: If either a Frequency or a Weight variable is entered in the Fit Model launch window, the entries in the Analysis of Variance report are adjusted in keeping with the descriptions in Frequency and Weight.

The Analysis of Variance report contains the following columns:

Lists the three sources of variation: Model, Error, and C. Total (Corrected Total).

Gives the associated degrees of freedom (DF) for each source of variation.

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The C. Total DF is always one less than the number of observations.

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The C. Total DF is partitioned into degrees of freedom for the Model and Error:

The Model degrees of freedom is the number of parameters (other than the intercept) used to fit the model.

The Error DF is the difference between the C. Total DF and the Model DF.

Gives the associated Sum of Squares (SS) for each source of variation.

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The total (C. Total) SS is the sum of the squared differences between the response values and the sample mean. It represents the total variation in the response values.

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The Error SS is the sum of the squared differences between the fitted values and the actual values. It represents the variability that remains unexplained by the fitted model.

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The Model SS is the difference between C. Total SS and Error SS. It represents the variability explained by the model.

Lists the mean squares. Each Mean Square is the sum of squares divided by its corresponding DF.

Note: The square root of the Mean Square for Error is the same as RMSE in the Summary of Fit report.

Shows the model mean square divided by the error mean square. The F Ratio is the test statistic for a test of whether the model differs significantly from a model where all predicted values are the response mean.

Gives the p-value for the test. The Prob > F value measures the probability of obtaining an F Ratio as large as what is observed, given that all parameters except the intercept are zero. Small values of Prob > F indicate that the observed F Ratio is unlikely. Such values are considered evidence that there is at least one significant effect in the model.

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Help created on 9/19/2017