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SPSS Regression Models Data Analysis
More statistics for data analysis
Expand SPSS Base's capabilities for the data analysis stage in the analytical process. Using SPSS Regression Models with SPSS Base
gives you an even wider range of statistics so you can get the most accurate response for specific data types. It easily plugs into SPSS Base so you can seamlessly work in the SPSS environment.
Statistical highlights for SPSS Regression Models:
Multinomial Logistic Regression:
Classify people into two or more groups. When a dependent variable includes two or more categories, the Multinomial Logistic Regression procedure gives you what's needed to accurately predict group membership within key groups. For example, a telecommunications company can build a model to predict if a customer will order caller ID, voice mail, three-way calling or multiple options. If the model predicts the customer is likely to order caller ID, it can send direct mail emphasizing caller ID to that customer. This means it won't waste resources advertising products or services that aren't likely to interest its customers.
You can also use stepwise functionality to find the best predictor from dozens of possible predictors. Select from one of four methods: forward entry, backward elimination, forward stepwise or backward stepwise. You
can opt to select a rule for effect entry or removal from analysis.
The Multinomial Logistic Regression procedure predicts a categorical outcome such as "primary
reason for Web use." The categories in this example are: a) work only, b) shopping only, c) both working and shopping, and d) neither (reference category). From the results above, we can see
that search engine use was a better predictor of "shopping only" than print media use.
Binary Logistic Regression: Group people with respect to their predicted action. Use if you need to build models in which the dependent variable is
dichotomous (for example, buy or not buy, pay or default, graduate or not graduate). Or maybe you want to predict the probability of events, such as
solicitation responses or program participation. For example, a utility company wants to know what predictors indicate failure to pay bills so it can create
special bill payment plans for customers needing assistance. The Binary Logistic Regression procedure empowers you to select the predictive model for dichotomous dependent variables.
Binary Logistic Regression gives you depth and flexibility to specify models and to choose predictor order inclusion. You can use six types of forward- or
backward-stepwise methods to select variables. This enables you to tell the procedure to find the best variables. Because you can work forward (the
procedure selects the strongest variables until there are no more significant predictors in the dataset) or backwards (at each step, the procedure removes
the least significant predictor in the dataset), you have the flexibility to select predictors the way you want to work. 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 Unconstrained 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 SPSS
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 empowers 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.
SPSS Regression Models gives you:
- Multinomial Logistic Regression
- Binary Logistic Regression
- Unconstrained Nonlinear Regression (NLR)
- Constrained Nonlinear Regression (CNLR)
- Two-Stage Least Squares
- Probit Analysis
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