jmp.com/learn rev 02/2014 Simple Linear Regression Simple linear regression is used to model the relationship between two continuous variables. Simple Linear Regression Using Fit Y by X Example: Big Class.jmp (Help > Sample Data) 1. From an open JMP data table, select Analyze > Fit Y by X. 2. Click on a continuous variable from Select Columns, and click Y, Response (continuous variables have blue triangles). 3. Select a second continuous variable, and click X, Factor. 4. Click OK to generate a scatterplot. 5. 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. Additional options, such as residual plots and confidence curves, are available from the red triangle next to Linear Fit (directly under the graph). Tips: For other fit options, such as polynomial, transformation (fit special) and spline, use the top red triangle. To add a legend, change markers, or make other changes to the graphical display, right-click on the graph. 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 and select Fit Line. JMP will fit separate lines and provide results for each level of the grouping variable. Notes: Simple linear regression can also be performed from Analyze > Fit Model. For more details on regression analysis, see the book Basic Analysis (under Help > Books) or search for \"regression\" in the JMP Help. jmp.com/learn rev 02/2014 Simple Logistic Regression Logistic regression is used to predict the probability of the occurrence of an event. Example: Car Poll.jmp (Help > Sample Data) 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. 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. 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. jmp.com/learn rev 02/2014 Clustering Use clustering to automatically group rows having similar characteristics. Hierarchical Clustering Example: Cereal.jmp (Help > Sample Data) 1. From an open JMP data table, select Analyze > Multivariate Methods > Cluster. 2. Select one or more variables from Select Columns and click Y, Columns. 3. If available, select a Label variable. 4. Select the desired method (bottom left corner) and click OK. JMP will generate: A dendrogram, showing the clusters formed at each step. A scree plot, showing the distance bridged each step. The clustering history, giving cluster statistics for each step. Tips: To color clusters, to mark or save clusters, or to request other options, click the top red triangle. To dynamically change the number of clusters, click and drag one of the black diamonds left or right. K-Means Clustering 1. From an open table, select Analyze > Multivariate Methods > Cluster. 2. Select one or more continuous variables from Select Columns and click Y, Columns (continuous variables have blue triangles). 3. Under Options, change Hierarchical to KMeans, and click OK. 4. In the resulting Control Panel, enter the number of clusters and click Go. JMP will generate: A summary of the cluster sizes. Tables of cluster means and standard deviations for each variable. Tips: To obtain biplots, parallel plots or request other options, click the red triangle for the K Means heading. To perform analyses for a range of cluster sizes: In the Control Panel, enter the lower limit in number of clusters and the upper limit in range of clusters, then click Go. To step through the formation of the clusters: In the Control Panel, check Single Step then click Go. To locate potential multivariate outliers, select Declutter in the Control Panel. Note: For more information on Declutter and additional discussion of these and other clustering methods, search for \"cluster\" in the JMP Help or see the book Multivariate Methods (under Help > Books). 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