As of Version 7.0, DesignedRegress has been superseded by LinearModelFit.
finds a least–squares fit given the design matrix matrix and response vector 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 that returned by SingularValueDecomposition.
- Exact numbers given as input to DesignedRegress are converted to approximate numbers with machine precision.
- The following options can be given:
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