Standard least squares is one of several analytic techniques that you can select in the Fit Model launch window.
This section describes how you select standard least squares as your fitting methodology in the Fit Model launch window. Options that are specific to this selection are also covered.
You can specify models with both fixed and random effects in the Fit Model launch window. The options differ based on the nature of the model that you specify.
To fit models using the standard least squares personality, select Analyze > Fit Model and then select Standard Least Squares from the Personality list. When you enter one or more continuous variables in the Y list, the Personality defaults to Standard Least Squares. Note, however, that other selections are available for continuous Y variables. When you specify only fixed effects for a Standard Least Squares fit, the Fit Model launch window appears as shown in Fit Model Launch Window for a Fixed Effects Model. This example illustrates the launch window using the Big Class.jmp sample data table.
When the Standard Least Squares personality is selected in the Personality list, an Emphasis option also appears. Emphasis options control the reports that are provided in the initial report window. Based on the model effects that are included, JMP infers which reports you are likely to want. However, any report not shown as part of the initial report can be shown by selecting the appropriate option from the default report’s red triangle menu.
If the specified model contains one or more random effects, then additional options become available in the Fit Model launch window. Consider the Machine.jmp sample data table. Each of six randomly chosen workers performs work at each of three machines and their output is rated. You are interested in estimating the variation in ratings across the workforce, rather than in determining whether these six specific workers’ ratings differ. You need to treat person and machine*person as random effects when you specify the model.
The Fit Model launch window for this model is shown in Fit Model Launch Window for a Model Containing a Random Effect. When the Random Effect attribute is applied to person, a Method option and two options relating to variance components appear in the Fit Model Launch window.
Fit Model Launch Window Options for Standard Least Squares Personality describes options appearing in the Fit Model launch window that are specific to the Standard Least Squares personality.
The three options in the Emphasis list control the types of plots and reports that you see as part of the initial report for the Standard Least Squares personality (Emphasis Options). JMP chooses a default emphasis based on the number of rows in the data table and the number of effects entered in the Construct Model Effects list. You can change this choice of emphasis based on your needs. For details about how JMP chooses the emphasis, see .
After the initial report opens, you can add other reports and plots from the platform’s red triangle menu.
Shows leverage and residual plots, as well as reports with details about the model fit. This option is useful when your main focus is model fitting.
Effect Leverage is the most comprehensive option. This emphasis divides reports into those that relate to the Whole Model and those that relate to individual model effects. The Whole Model reports are in the left corner of the report window under the Whole Model title, with effect reports to the right.
Displays a sorted or scaled parameter estimates report along with a graph (when appropriate), the Prediction Profiler, and reports with details about the model fit. This option is useful when you have many effects and your initial focus is to discover which effects are active, as in screening designs.
By default, rows that have missing values for Y or any model effects are excluded from the analysis.
Note: JMP provides an Informative Missing option in the Fit Model window under Model Specification. Informative Missing enables you to fit models using rows where model effects are missing. See in Model Specification for details.
When your model contains a random effect, Y values are fit separately by default. The individual reports appear in the Fit Group report.
Suppose that your model contains only fixed effects. You enter more than one Y response, and some of these Y variables have missing values. JMP prompts you to select one of the following options:
When you select Fit Separately, a Fit Group report organizes the individual reports for the Y variables. You can select profilers from the Fit Group red triangle menu to view all the Y variables in the same profiler. Alternatively, you can select a profiler from an individual Y variable report to view only that variable in the profiler.