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What's new in SPSS Regression Models
The following capabilities were added to the Multinomial Logistic Regression procedure which regresses a categorical dependent variable with more than two categories on a set of independent variables, in
SPSS Regression Models:
- Find the best predictor from dozens of possible predictors using stepwise functionality.
- Find predictors using one of four methods: forward entry, backward elimination, forward stepwise or backward stepwise.
- Opt to select a rule for effect entry or removal from the analysis.
- Base entry or removal on satisfying the hierarchy requirement for all effects, for factor-only effects or for satisfying the containment requirement for all effects.
- Optionally, perform entry or removal without satisfying the hierarchy or containment requirement for any effects in the model.
- Choose an option for setting the minimum and maximum numbers of terms included in the final model.
- Choose an option for setting the probabilities for variable entry and removal.
- Reach more accurate conclusions using a likelihood ratio-based method.
- Specify the reference category in the dependent variables through the interface (previously, this capability was available only through syntax).
Download the SPSS Regression Models 13.0 spec sheet (PDF - zipped) for more information on the statistics in SPSS Regression Models.
Back to the SPSS Regression Models page.
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