Shows the Correlation Probability report, which is a matrix of pvalues. Each pvalue corresponds to a test of the null hypothesis that the true correlation between the variables is zero. This is a test of no linear relationship between the two response variables. The test is the usual test for significance of the Pearson correlation coefficient.


The default confidence coefficient is 95%. Use the Set α Level option to change the confidence coefficient.


The diagonal elements of the matrix are a function of how closely the variable is a linear function of the other variables. In the inverse correlation, the diagonal is 1/(1 – R2) for the fit of that variable by all the other variables. If the multiple correlation is zero, the diagonal inverse element is 1. If the multiple correlation is 1, then the inverse element becomes infinite and is reported missing.


Hotelling’s T2 Test

Gives the value of the test statistic. If you have n rows and k variables, the F ratio is given as follows:
The pvalue for the test. Under the null hypothesis the F ratio has an F distribution with n and n  k degrees of freedom.

Shows statistics that correspond to the estimation method selected in the launch window. If the REML, ML, or Robust method is selected, the mean vector and covariance matrix are estimated by that selected method. If the Rowwise method is selected, all rows with at least one missing value are excluded from the calculation of means and variances. If the Pairwise method is selected, the mean and variance are calculated for each column.


Set α Level


The Color Map menu contains three types of color maps.
Produces a cell plot that shows the significance of the correlations on a scale from p = 0 (red) to p = 1 (blue).


This menu contains options to show or hide a principal components report. You can select correlations, covariances, or unscaled. Selecting one of these options when another of the reports is shown changes the report to the new option. Select None to remove the report. This option is off by default.
Principal components is a technique to take linear combinations of the original variables. The first principal component has maximum variation, the second principal component has the next most variation, subject to being orthogonal to the first, and so on. For details, see the chapter Principal Components.


This menu contains options that each shows or hides an item reliability report. The reports indicate how consistently a set of instruments measures an overall response, using either Cronbach’s α or standardized α. These options are off by default.


Contains options that are available to all platforms. See the Using JMP book.

The Nonparametric Correlations menu offers three nonparametric measures:
is based on the number of concordant and discordant pairs of observations. A pair is concordant if the observation with the larger value of X also has the larger value of Y. A pair is discordant if the observation with the larger value of X has the smaller value of Y. There is a correction for tied pairs (pairs of observations that have equal values of X or equal values of Y).
A scatterplot matrix helps you visualize the correlations between each pair of response variables. The scatterplot matrix is shown by default, and can be hidden or shown by selecting Scatterplot Matrix from the red triangle menu for Multivariate.
When you look for patterns in the scatterplot matrix, you can see the variables cluster into groups based on their correlations. Clusters of Correlations shows two clusters of correlations: the first two variables (top, left), and the next four (bottom, right).
Shows or hides the 95% density ellipses in the scatterplots. Use the Ellipse α menu to change the αlevel.


Colors each ellipse. Use the Ellipses Transparency and Ellipse Color menus to change the transparency and color.


Shows either horizontal or vertical histograms in the label cells. Once histograms have been added, select Show Counts to label each bar of the histogram with its count. Select Horizontal or Vertical to either change the orientation of the histograms or remove the histograms.


Ellipse α

Sets the αlevel used for the ellipses. Select one of the standard αlevels in the menu, or select Other to enter a different one.

Sets the transparency of the ellipses if they are colored. Select one of the default levels, or select Other to enter a different one. The default value is 0.2.


Sets the color of the ellipses if they are colored. Select one of the colors in the palette, or select Other to use another color. The default value is red.


The Outlier Analysis menu contains options that show or hide plots that measure distance in the multivariate sense using one of these methods:
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T2 statistic

In Example of an Outlier, Point A is an outlier because it is outside the correlation structure rather than because it is an outlier in any of the coordinate directions.
The Mahalanobis Outlier Distance plot shows the Mahalanobis distance of each point from the multivariate mean (centroid). The standard Mahalanobis distance depends on estimates of the mean, standard deviation, and correlation for the data. The distance is plotted for each observation number. Extreme multivariate outliers can be identified by highlighting the points with the largest distance values. See Mahalanobis Distance Measures for more information.
The Jackknife Distances plot shows distances that are calculated using a jackknife technique. The distance for each observation is calculated with estimates of the mean, standard deviation, and correlation matrix that do not include the observation itself. The jackknifed distances are useful when there is an outlier. In this case, the Mahalanobis distance is distorted and tends to disguise the outlier or make other points look more outlying than they are. See Jackknife Distance Measures for more information.
T2 Statistic
The T2 plot shows distances that are the square of the Mahalanobis distance. This plot is preferred for multivariate control charts. The plot includes the value of the calculated T2 statistic, as well as its upper control limit. Values that fall outside this limit might be outliers. See T2 Distance Measures for more information.
You can save any of the distances to the data table by selecting the Save option from the red triangle menu for the plot.
Note: There is no formula saved with the jackknife distance column. This means that the distance is not recomputed if you modify the data table. If you add or delete columns, or change values in the data table, select Analyze > Multivariate Methods > Multivariate again to compute new jackknife distances.
Item reliability indicates how consistently a set of instruments measures an overall response. Cronbach’s α (Cronbach 1951) is one measure of reliability. Two primary applications for Cronbach’s α are industrial instrument reliability and questionnaire analysis.
Cronbach’s α is based on the average correlation of items in a measurement scale. It is equivalent to computing the average of all splithalf correlations in the data table. The Standardized α can be requested if the items have variances that vary widely.
Note: Cronbach’s α is not related to a significance level α. Also, item reliability is unrelated to survival time reliability analysis.
To look at the influence of an individual item, JMP excludes it from the computations and shows the effect of the Cronbach’s α value. If α increases when you exclude a variable (item), that variable is not highly correlated with the other variables. If the α decreases, you can conclude that the variable is correlated with the other items in the scale. Nunnally (1979) suggests a Cronbach’s α of 0.7 as a ruleofthumb acceptable level of agreement.
To impute missing data, select Impute Missing Data from the red triangle menu for Multivariate. A new data table is created that duplicates your data table and replaces all missing values with estimated values.