Car Poll.jmp (Help > Sample Data) Simple Logistic Regression Logistic regression is used to predict the probability of the occurrence of an event.
Example: Car Poll.jmp (Help > Sample Data) Simple Logistic Regression Logistic regression is used to predict the probability of the occurrence of an event. Logistic Regression Using Fit Y by X 1. From an open JMP® data table, select Analyze > Fit Y by X. 2. Click on a categorical variable from Select Columns, and click Y, Response (nominal variables have red bars, ordinal variables have green bars). 3. Click on a continuous variable, and click X, Factor (continuous variables have blue triangles). 4. Click OK to run the analysis. By default, JMP will provide the following results: • The logistic plot, with curves of cumulative predicted (fitted) probabilities. • The whole model test for model significance. • Parameter estimates for the fitted model. Tips: • When the response is nominal, a nominal logistic model will be fit. When the response is ordinal, as in this example, an ordinal logistic model will be fit. • To color points and add a legend, right-click in the graph and select Row Legend. Select a variable under Mark by Column, and select Markers to change the marker, and click OK. • To save the probability formula or request other options, click on the top red triangle and select the option. • To find the fitted probability for a given value of X, select the cross-hair tool ( ) from the toolbar or use the keyboard shortcut (C), and click on the graph. Notes: Simple nominal and ordinal logistic regression can also be performed from Analyze > Fit Model. For more details see the book Basic Analysis (under Help > Books) or search for “simple logistic regression” in the JMP Help. Interpretation (for this example, X = buying age and Y = car size): • The bottom curve represents the predicted probability that for a given age, someone will buy a large car. • The second curve represents the probability that someone will buy a large or medium car. • The distance between the two curves represents the probability that someone will buy a medium car. • The distance between 1.00 and the top curve represents the probability that someone will buy a small car. • The cross-hairs show that the predicted probability that someone aged 44.98 years will purchase a large car is 0.2373. jmp.com/learn rev 02/2014
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