SPSS Regression

Większa dokładność prognoz dzięki zaawansowanym procedurom regresji

IBM SPSS Regression umożliwia przewidywanie wyników kategoryzowanych i stosowanie szerokiej gamy procedur regresji nieliniowej.

Moduł IBM SPSS Regression znajduje zastosowanie w wielu projektach biznesowych i analitycznych, w których zwykłe techniki regresji okazują się zbyt ograniczone lub nieodpowiednie — na przykład w analizie zwyczajów zakupowych klientów, reakcji na leczenie, osiągnięć akademickich i ryzyka kredytowego.

• IBM SPSS Regression zawiera następujące procedury regresji:
• Wielomianowa regresja logistyczna: prognozowanie wyników kategoryzowanych z więcej niż dwiema kategoriami.
• Binarna regresja logistyczna: łatwe klasyfikowanie danych w dwie grupy.
• Regresja nieliniowa i regresja nieliniowa z ograniczeniami (CNLR — constrained nonlinear regression): estymacja parametrów modeli nieliniowych.
• Ważona metoda najmniejszych kwadratów: zwiększa wagę pomiarów w serii.
• Dwuetapowa metoda najmniejszych kwadratów: ułatwia kontrolę nad korelacją między predyktorami a składnikami błędów.
• Analiza probitowa: ocena wartości bodźca przy użyciu transformacji logitowej lub probitowej odsetka próby z odpowiedzią.
  

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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.