Shows the T2 chart. Hotelling’s T2 chart is a multivariate extension of the Xbar chart that takes correlation into account.
Constructs multivariate control charts based on the principal components of Y. Specify the number of major principal components for T2. See T Square Partitioned.
Creates a new column in the data table. Stores a formula in the column that calculates the T2 values.
(Not available for subgrouped 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.
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.
The T Square Partitioned option is also useful when your covariance matrix is illconditioned. When this is the case, components with small eigenvalues, explaining very little variation, can have a large, and misleading, impact on T2. It is useful to separate out these less important components when studying process behavior.
The option creates two multivariate control charts: T Square with Big Principal Components and T Square with Small Principal Components. Suppose that you enter r as the number of major components when you first select the option. The chart with Big Principal Components is based on the r principal components corresponding to the r largest eigenvalues. These are the r components that explain the largest amount of variation, as shown in the Percent and Cum Percent columns in the Principal Components: on Covariances reports. The chart with Small Principal Components is based on the remaining principal components.
For a given subgroup, its T2 value in the Big Principal Components chart and its T2 value in the Small Principal Components chart sum to its overall T2 statistic presented in the T2 with All Principal Components report. For details about how the partitioned T2 values are calculated, see Kourti, T. and MacGregor, J. F., 1996.
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In the Example of Change Point Detection, data are divided into two groups. The first 24 observations are classified as the first group. The remaining observations are classified as the second group.

pvalue for the test.
For more information about principal components, see the Multivariate Methods book.