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DesignedRegress[matrix, vector]
finds a least-squares fit given the design matrix matrix and response vector vector.
DesignedRegress[svd, vector]
finds a fit given the singular value decomposition svd of a design matrix.
  • A design matrix is a list containing the basis functions evaluated at the observed values of the independent variables, as returned by DesignMatrix.
  • DesignedRegress returns a list of rules for results and diagnostics specified by the option RegressionReport.
  • Exact numbers given as input to DesignedRegress are converted to approximate numbers with machine precision.
  • The following options can be given:
RegressionReportSummaryReportresults to be included in output
BasisNamesAutomaticnames of basis elements for table headings
WeightsAutomaticweights for each data point
MethodAutomaticmethod used to compute singular values
ToleranceAutomatictolerance to use in computing singular values
ConfidenceLevel0.95confidence level used for confidence intervals
Linear regression with one constant and one non-constant basis function:
Click for copyable input