User Specified Regression, Least Squares
Fit arbitrary regression models using least squares estimation; you can specify a regression equation using standard notation (e.g., Var3=a+log(b*Var4)). Logical operators are also supported. Least squares estimation is aimed at minimizing the sum of squared deviations of the observed values for the continuous dependent variable from those predicted by the model. When using the least-squares criterion, the very efficient Levenberg-Marquardt algorithm can be used to estimate the parameters for arbitrary linear and nonlinear regression problems. For large data sets, when using the least-squares criterion, this is the recommended method for fitting nonlinear models. Note: If no model (syntax) is specified, Statistica will fit a simple linear model.
General
| Element Name | Description |
|---|---|
| Detail of computed results reported | Detail of results reported; if Minimal detail is requested, only the parameter estimates and a summary ANOVA table will be reported; if Comprehensive results is requested, the iteration history, and covariances and correlations of parameter estimates are also reported. If All results is requested, a plot of the fitted 2D or 3D function (if applicable) will also be reported, along with various descriptive graphs. Predicted and residual values can be requested as an option; if All results is requested, various plots of residuals will also be computed. |
| User-defined function | Specifies the regression equation. Specify the desired regression model in the general form for models: Dep.Var = Predictor Model On the left side of the equation, specify the dependent variable; on the right side, specify the expression including independent variables and the parameters to be estimated. Refer to variables either by their numbers (e.g., = v1 - v2) or name (e.g., = Retail - Cost) All names that are not recognized by Statistica as variable names or valid reserved keywords are interpreted to be parameters. Equations may contain logical expressions that involve constants, variables, parameters, or any mixture of the three. Example: v5=a+b*v5+log(c*v6). |
| Estimation method | Select the desired estimation method. The Levenberg-Marquardt method is recommended for most applications. |
| Missing data deletion | Missing data can be casewise deleted or substituted by the respective variable means. |
| Number of iterations | Specifies the maximum number of iterations to be performed during the parameter estimation. |
| Convergence criterion | Set the convergence criterion value (by default, 0.0001); refer to the Electronic Manual for details. |
| p, for highlighting | p value for highlighting significant results (parameter estimates) in results spreadsheets. |
| p, for conf. limits | Specifies a p value to be used for establishing confidence intervals for parameter estimates. |
| Residual analysis | Creates predicted and residual values; if the All results Level of detail is selected, then probability plots, surface plots, etc. are also reported. |
| Generates data source, if N for input less than | Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations. |