Quality Metrics for Linear Regression

Given a data set X = (xi ) that contains vectors of input variables xi = (xi1, …, xip ), respective responses zi = (zi1, …, zik ) computed at the prediction stage of the linear regression model defined by its coefficients βht , h = 1, ..., k, t = 1, ..., p, and expected responses yi = (yi1, …, yik ), i = 1, ..., n, the problem is to evaluate the linear regression model by computing the root mean square error, variance-covariance matrix of beta coefficients, various statistics functions, and so on. See Linear Regression for additional details and notations.

For linear regressions, the library computes statistics listed in tables below for testing insignificance of beta coefficients and one of the following values of QualityMetricsId:

For more details, see [Hastie2009].

For more complete information about compiler optimizations, see our Optimization Notice.
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