PASW Regression
Improve Predictions with Powerful Nonlinear Regression Software
PASW Regression (formerly called SPSS Regression) enables you to apply more sophisticated models to your data using its wide range of nonlinear regression models. You can apply PASW Regression, to many disciplines,
including:
- Market research: Study consumer buying habits
- Medical research: Study response to dosages through probit analysis
- Institutional research: Measure academic achievement tests
- Loan assessment: Analyze good and bad credit risks
PASW Regression includes these procedures:
- Multinomial logistic regression (MLR): Predict categorical outcomes with more than two categories
- Binary logistic regression: Easily classify your data into two groups
- Nonlinear regression (NLR) and constrained nonlinear regression (CNLR): Estimate parameters of nonlinear models
- Probit analysis: Evaluate the value of stimuli using a logit or probit transformation of the proportion responding
Data Analysis
More Statistics for Data Analysis
Expand PASW Statistics Base's capabilities for the data analysis stage in the analytical process. Using PASW Regression with PASW Statistics Base
gives you an even wider range of statistics so you can get the most accurate response for specific data types. You can seamlessly work in the PASW Statistics environment.
PASW Regression includes additional diagnostics for use when developing a classification table.
Statistical Highlights for PASW Regression:
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. 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, 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
access 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 or not buy, pay or default, graduate or not graduate). You can also use binary logistic regression 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. This procedure enables you to select the predictive model for dichotomous dependent variables.
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 PASW 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
PASW Regression Includes:
- Multinomial logistic regression
- Binary logistic regression
- Nonlinear regression (NLR)
- Constrained nonlinear regression (CNLR)
- Weighted least squares (WLS)
- Two-stage least squares (2LS)
- Probit 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.
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