Linear Regression Package Symbol

DesignedRegress

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.

To use DesignedRegress, you first need to load the Linear Regression Package using Needs["LinearRegression`"].

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.

The argument svd is of the same form as returned that returned by SingularValueDecomposition.

Exact numbers given as input to DesignedRegress are converted to approximate numbers with machine precision.

RegressionReport	SummaryReport	results to be included in output
BasisNames	Automatic	names of basis elements for table headings
Weights	Automatic	weights for each data point
Method	Automatic	method used to compute singular values
Tolerance	Automatic	tolerance to use in computing singular values
ConfidenceLevel	0.95	confidence level used for confidence intervals

Needs["LinearRegression`"]

Linear regression with one constant and one non-constant basis function: