User manual SPSS REGRESSION MODELS 12.0

[. . . ] SPSS Regression Models 12. 0 TM For more information about SPSS® software products, please visit our Web site at http://www. spss. com or contact SPSS Inc. 233 South Wacker Drive, 11th Floor Chicago, IL 60606-6412 Tel: (312) 651-3000 Fax: (312) 651-3668 SPSS is a registered trademark and the other product names are the trademarks of SPSS Inc. No material describing such software may be produced or distributed without the written permission of the owners of the trademark and license rights in the software and the copyrights in the published materials. The SOFTWARE and documentation are provided with RESTRICTED RIGHTS. [. . . ] This criterion is ignored unless the backward elimination, forward stepwise, or backward stepwise method is selected. Minimum Stepped Effect in Model. When using the backward elimination or backward stepwise methods, this specifies the minimum number of terms to include in the model. The intercept is not counted as a model term. Maximum Stepped Effect in Model. When using the forward entry or forward stepwise methods, this specifies the maximum number of terms to include in the model. The intercept is not counted as a model term. Hierarchically constrain entry and removal of terms. This option allows you to choose whether to place restrictions on the inclusion of model terms. Hierarchy requires that for any term to be included, all lower order terms that are a part of the term to be included must be in the model first. For example, if the hierarchy requirement is in effect, the factors Marital status and Gender must both be in the model before the Marital Status*Gender interaction can be added. The three radio button options determine the role of covariates in determining hierarchy. Multinomial Logistic Regression Save Figure 3-7 Multinomial Logistic Regression Save dialog box 23 Multinomial Logistic Regression The Save dialog box allows you to save variables to the working file and export model information to an external file. Saved variables. These are the estimated probabilities of classifying a factor/covariate pattern into the response categories. There are as many estimated probabilities as there are categories of the response variable; up to 25 will be saved. Predicted category. This is the response category with the largest expected probability for a factor/covariate pattern. Predicted category probabilities. This is the maximum of the estimated response probabilities. Actual category probability. This is the estimated probability of classifying a factor/covariate pattern into the observed category. Export model information to XML file. Parameter estimates and (optionally) their covariances are exported to the specified file. SmartScore and future releases of WhatIf?will be able to use this file. NOMREG Command Additional Features The SPSS command language also allows you to: Specify the reference category of the dependent variable. Customize hypothesis tests by specifying null hypotheses as linear combinations of parameters. Chapter Probit Analysis 4 This procedure measures the relationship between the strength of a stimulus and the proportion of cases exhibiting a certain response to the stimulus. It is useful for situations where you have a dichotomous output that is thought to be influenced or caused by levels of some independent variable(s) and is particularly well suited to experimental data. This procedure will allow you to estimate the strength of a stimulus required to induce a certain proportion of responses, such as the median effective dose. Example. How effective is a new pesticide at killing ants, and what is an appropriate concentration to use?You might perform an experiment in which you expose samples of ants to different concentrations of the pesticide and then record the number of ants killed and the number of ants exposed. [. . . ] Polynomial contrasts are especially useful in tests of trends and for investigating the nature of response surfaces. You can also use polynomial contrasts to perform nonlinear curve fitting, such as curvilinear regression. Repeated Compares adjacent levels of an independent variable. The general matrix form is mean df(1) df(2) . df(k­1) (0 ( 1/k (1 (0 1/k ­1 1 . [. . . ]