2.2 Regression Classes
The Business Analysis Module contains classes for multiple linear regression and for logistic regression. Both kinds of classes operate on similar input data. The data are in the form of a matrix of values for the predictor variables, and a vector of values for the observation variable. The Regression Classes are contained in Table 1.
Table 1: The Regression Classes
| Class Name | Description |
Constructs a linear regression model from a matrix of predictor variable data and a vector of observation variable data. | |
ANOVA stands for analysis of variance. The analyzed variance is the variance of residual errors in a linear regression model, also known as the regression's goodness of fit. | |
Tests that the estimated parameters in a linear regression model are equal to a hypothesized vector of values. | |
Receives data input in the form of a matrix of values for the predictor variables, and a vector of values for the observations. | |
An abstract base class defines the interface for the regression classes in this module. Classes derived from this class include RWLinearRegression and RWLogisticRegression. |
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