Simple Linear Regression
Use to model the bivariate relationship between a continuous explanatory variable with a continuous outcome variable. Useful to describe the relationship between the variables and to predict an outcome for different values of the explanatory variable.
Simple Linear Regression Using Fit Y by X
- From an open JMP® data table, select Analyze > Fit Y by X.
- Click on a continuous variable from Select Columns, and click Y, Response (continuous variables have blue triangles).
- Select a second continuous variable, and click X, Factor.
- Click OK to generate a scatterplot.
- To fit a regression line, click on the red triangle and select Fit Line.
By default, JMP will provide the following results:
- The regression equation (under Linear Fit).
- The Summary of Fit.
- Lack of Fit (if the data table includes replicates of X values).
- The ANOVA table.
- The parameter estimates.
- For other fit options, such as polynomials, transformations (fit special) and spline (under flexible), use the top red triangle.
- To add a legend, change markers, or make other changes to the graphical display, right-click on the graph.
- Double-click on the axes to change axes settings.
- Other analysis steps common in linear regression (e.g., plotting residuals, adding confidence bands, saving predicted values and residuals to the data table, among others) can be found under the red triangle next to the title of the equation fit (e.g., “Linear Fit”).
- To fit separate lines for categories of a grouping variable, click on the top red triangle, select Group By, and choose a grouping variable. Then, click on the top red triangle again and select Fit Line. JMP will fit separate lines and provide results for each level of the grouping variable.
Visit Discovering JMP > Analyze Your Data > Analyze Relationships and Basic Analysis > Bivariate Analysis in JMP Help to learn more.