SPSS Regression

Mayor precisión en los pronósticos mediante potentes procedimientos de regresión

IBM SPSS Regression le permite prever resultados categóricos y aplicar una amplia gama de procedimientos de regresión no lineal.

Puede aplicar IBM SPSS Regression a muchos proyectos empresariales y de análisis en los que las técnicas ordinarias de regresión estén limitadas o no sean adecuadas. Por ejemplo, el estudio de hábitos de compra de los consumidores o respuestas a tratamientos, la medición del logro académico y el análisis de los riesgos de crédito.

• IBM SPSS Regression incluye los procedimientos siguientes:
• Regresión logística multinomial: pronostique resultados categóricos con más de dos categorías
• Regresión logística binaria: clasifique fácilmente los datos en dos grupos
• Regresión no lineal y regresión no lineal restringida (CNLR): calcule parámetros de modelos no lineales
• Mínimos cuadrados ponderados: aumenta la ponderación a las medidas de una serie
• Mínimos cuadrados en dos etapas: ayuda a controlar las correlaciones entre las variables de predictor y los términos de error
• Análisis probit: evalúe el valor de los estímulos mediante una transformación logit o probit de la proporción respondida

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• Multinomial logistic regression (MLR): Regress a categorical dependent variable with more than two categories on a set of independent variables. This procedure helps you accurately predict group membership within key groups.
  
  You can also use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor from dozens of possible predictors. If you have a large number of predictors, Score and Wald methods can help you more quickly reach results. You can assess your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC; also called Schwarz Bayesian criterion, or SBC).
  
  

• Binary logistic regression: Group people with respect to their predicted action. Use this procedure if you need to build models in which the dependent variable is dichotomous (for example, buy versus not buy, pay versus default, graduate versus not graduate). You can also use binary logistic regression to predict the probability of events such as solicitation responses or program participation.
  
  With binary logistic regression, you can select variables using six types of stepwise methods, including forward (the procedure selects the strongest variables until there are no more significant predictors in the dataset) and backward (at each step, the procedure removes the least significant predictor in the dataset) methods. You can also set inclusion or exclusion criteria. The procedure produces a report telling you the action it took at each step to determine your variables.
  
  
  

• Nonlinear regression (NLR) and constrained nonlinear regression (CNLR): Estimate nonlinear equations. If you are you working with models that have nonlinear relationships, for example, if you are predicting coupon redemption as a function of time and number of coupons distributed, estimate nonlinear equations using one of two IBM SPSS Statistics procedures: nonlinear regression (NLR) for unconstrained problems and constrained nonlinear regression (CNLR) for both constrained and unconstrained problems.
  
  NLR enables you to estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms, while CNLR enables you to:
  • Use linear and nonlinear constraints on any combination of parameters
  • Estimate parameters by minimizing any smooth loss function (objective function)
  • Compute bootstrap estimates of parameter standard errors and correlations
  
  

• Weighted least squares (WLS): If the spread of residuals is not constant, the estimated standard errors will not be valid. Use Weighted Least Square to estimate the model instead (for example, when predicting stock values, stocks with higher shares values fluctuate more than low value shares.)
  
  

• Two-stage least squares (2LS): Use this technique to estimate your dependent variable when the independent variables are correlated with the regression error terms.
  
  For example, a book club may want to model the amount they cross-sell to members using the amount that members spend on books as a predictor. However, money spent on other items is money not spent on books, so an increase in cross-sales corresponds to a decrease in book sales. Two-Stage Least-Squares Regression corrects for this error.
  
  

• Probit analysis: Probit analysis is most appropriate when you want to estimate the effects of one or more independent variables on a categorical dependent variable.
  
  For example, you would use probit analysis to establish the relationship between the percentage taken off a product, and whether a customer will buy as the prices decreases. Then, for every percent taken off the price you can work out the probability that a consumer will buy the product.
  
  

• IBM SPSS Regression includes additional diagnostics for use when developing a classification table.