The Fit Model platform gives you an efficient way to specify models that have complex effect structures. These effect structures are linear in the predictor variables. 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. Standard Model Types 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.
Refer to the section Examples of Model Specifications and Their Model Fits to see the clicking sequences that produce these model effects, plots of the model fits, and some examples.
 Type of Model Model Effects Simple Linear Regression X Polynomial in X to Degree k 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 A 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