The Multivariate Control Chart red triangle menu contains the following options:
T Square Chart
Shows the T2 chart. Hotelling’s T2 chart is a multivariate extension of the XBar chart that takes correlation into account.
T Square Partitioned
Constructs multivariate control charts based on the principal components of Y. Specify the number of major principal components for T2. See T Square Partitioned.
Set Alpha Level
Set the α level used to calculate the control limit. The default is α=0.05.
Show Covariance
Shows the Covariance report. Covariance is a measure of the linear relationship between two variables. When a subgroup variable is specified, the Pooled Covariance report is shown.
Show Correlation
Shows the Correlation report. When a subgroup variable is specified, the Pooled Correlation report is shown.
Show Inverse Covariance
Shows the Inverse Covariance report. If the inverse covariance is singular, a generalized inverse of the covariance matrix is reported. When a subgroup variable is specified, the Pooled Inverse Covariance report is shown.
Show Inverse Correlation
Shows the Inverse Correlation report. If the inverse correlation is singular, a generalized inverse of the correlation matrix is reported. When a subgroup variable is specified, the Pooled Inverse Correlation report is shown.
Show Means
Shows the Group Means report, which contains the means for each group.
Save T Square
Creates a new column in the data table containing T2 values.
Save T Square Formula
Creates a new column in the data table. Stores a formula in the column that calculates the T2 values.
Save Target Statistics
Creates a new data table containing target statistics for the process. Target statistics include: sample size, the number of samples, mean, standard deviation, and any correlations.
Change Point Detection
(Not available for sub-grouped data.) Shows a Change Point Detection plot of test statistics by row number and indicates the row number where the change point appears. See Change Point Detection.
Principal Components
Shows reports showing eigenvalues and their corresponding eigenvectors. Principal components help you understand which of the many variables you might be monitoring are primarily responsible for the variation in your process. See Principal Components.
Save Principal Components
Creates new columns in the data table that contain the scaled principal components.