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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).

 

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