Scatterplots and other such graphs can help you visualize relationships between variables. Once you have visualized relationships, the next step is to analyze those relationships so that you can describe them numerically. That numerical description of the relationship between variables is called a model. Even more importantly, a model also predicts the average value of one variable (Y) from the value of another variable (X). The X variable is also called a predictor. Generally, this model is called a regression model.
With JMP, the Fit Y by X platform and the Fit Model platform creates regression models.
Note: Only the basic platforms and options are covered here. For explanations of all platform options, see Basic Analysis, Essential Graphing, and the documentation listed in About This Chapter.
Table 5.3 shows the four primary types of relationships.
X 
Y 
Section 

Continuous 
Continuous 

Categorical 
Continuous 

Categorical 
Categorical 

Continuous 
Categorical 
Logistic regression is an advanced topic. See Logistic Analysis in Basic Analysis. 