# Statistical Advisor, General Nonlinear Regression

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GENERALIZED LINEAR MODEL (GLZ*)*. This chapter discusses an implementation of the generalized linear model and how to compute a standard, stepwise, or best subset multiple regression analysis, in which one (non-normally distributed) dependent (criterion, endogenous) variable is linearly or non-linearly (see *Link Function*) related to multiple independent (predictor, exogenous) variables.

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NONLINEAR ESTIMATION. This chapter discusses techniques for estimating any type of regression equation. In most general terms, all multiple regression equations, that is, relationships between variables, can be expressed as y = F(x). In other words, the dependent variable y is a function of one or more variables x. The nature of the function may be linear, exponential, logarithmic, or even discontinuous.

For example, we could explicitly specify a so-called breakpoint regression model. That is, we may hypothesize that the relationship between x and y is generally linear, but that the slope is different in different regions of the x variable. Thus, the number of bedrooms in a house may be related to the price of the house; however, the relationship may not be as strong for large houses that have more than 5 bedrooms.

NONLINEAR ESTIMATION allows you to type in any kind of regression equation, and to specify how it is to be estimated (least squares, weighted least squares, etc.)

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GRAPHICAL ANALYTIC TECHNIQUES: Graphical analytic techniues include various facilities for 2D and 3D fitting of lines or surfaces to observed data. These graphs offer a wide variety of methods to visualize relationships between variables.