Analyze Relationships

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.

Table 5.3 Relationship Types
X	Y	Section
Continuous	Continuous	Use Regression with One Predictor Use Regression with Multiple Predictors
Categorical	Continuous	Compare Averages for One Variable Compare Averages for Multiple Variables
Categorical	Categorical	Compare Proportions
Continuous	Categorical	Logistic regression is an advanced topic. See “Logistic Analysis” in Basic Analysis.

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