Fitting Linear Models > Model Specification > Overview of the Fit Model Platform
Publication date: 08/13/2020

Overview of the Fit Model Platform

The Fit Model platform gives you an efficient way to specify models that have complex effect structures. These effect structures are linear in the model parameters. Once you have specified your model, you can select the appropriate fitting technique from a number of fitting personalities. Once you choose a personality, the Fit Model window provides choices that are relevant for the chosen personality. This chapter focuses on the elements of the Model Specification window that are common to most personalities. For a description of all personalities, see Fit Model Launch Window Elements.

Fit Model can be used to specify a wide variety of models that can be fit using various methods. Table 2.1 lists some typical models that can be defined using Fit Model. In the table, the effects X and Z represent columns with a continuous modeling type, while A, B, and C represent columns with a nominal or ordinal modeling type.

See Examples of Model Specifications and Their Model Fits for the clicking sequences that produce these model effects, plots of the model fits, and some examples.

Table 2.1 Standard Model Types

Type of Model

Model Effects

Simple Linear Regression


Polynomial in X to Degree k

X, X*X,..., Xk

Polynomial in X and Z to Degree k

X, X*X,..., Xk, Z, Z*Z,..., Zk

Multiple Linear Regression

X, Z, and other continuous columns

One-Way Analysis of Variance


Two-Way Analysis of Variance

A, B

Two-Way Analysis of Variance with Interaction

A, B, A*B

Three-Way Full Factorial

A, B, C, A*B, A*C, B*C, A*B*C

Analysis of Covariance, Equal Slopes

A, X

Analysis of Covariance, Unequal Slopes

A, X, A*X

Two-Factor Nested Random Effects Model

A, B[A]&Random

Three-Factor Fully Nested Random Effects Model

A, B[A]&Random, C[A,B]&Random

Simple Split Plot or Repeated Measures Model

A, B[A]&Random, C, C*A

Two-Factor Response Surface Model

X&RS, Z&RS, X*X, X*Z, Z*Z

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