Output for Independent Variables

Figure 1. Parameters
Parameters

The parameters table shows the estimated regression coefficients for each independent variable for predicting the dependent variable. Instead of the typical tests of model effects, look to the variable importance in the projection table for guidance on which predictors are most useful.

Figure 2. Variable importance in the projection
Variable importance in the projection

The variable importance in the projection (VIP) represents the contribution of each predictor to the model, cumulative by the number of factors in the model. For example, in the one-factor model, price loads heavily on the first factor and has a VIP of 2.088. As more factors are added, the cumulative VIP for price slowly drops to 1.946, presumably because it does not load very heavily on those factors. By contrast, engine_s has a VIP of 0.512 in the one-factor model, which rises to 0.932 in the five-factor model.

Figure 3. indepVars dataset
indepVars dataset

The parameter coefficients and VIP information is also saved to the indepVars dataset and can be used in further analysis of the data. The cumulative variable importance chart, for example, is created using this dataset.

Figure 4. Cumulative variable importance chart
Cumulative variable importance chart

The cumulative variable importance chart provides a visualization of the variable importance in the projection table. For information on the contribution of predictors to individual factors instead of the cumulative model, see the output for latent factors.

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