As of Version 7.0, Regress has been superseded by LinearModelFit.


finds a leastsquares fit to a list of data as a linear combination of the functions funs of variables vars.

Details and Options

  • To use Regress, you first need to load the Linear Regression Package using Needs["LinearRegression`"].
  • The data can have the form {{x1,y1,,f1},{x2,y2,,f2},}, where the number of coordinates x,y, is equal to the number of variables in the list vars.
  • The data can also be of the form {f1,f2,}, with a single coordinate assumed to take values 1,2,.
  • The argument funs can be any list of functions that depend only on the variables vars.
  • Regress returns a list of rules for results and diagnostics specified by the option RegressionReport.
  • Regress always finds the linear combination of the functions in the list funs that minimize the sum of the squares of deviations from the values fi.
  • Exact numbers given as input to Regress are converted to approximate numbers with machine precision.
  • The following options can be given:
  • RegressionReport SummaryReportresults to be included in output
    IncludeConstant Truewhether to automatically include a constant as one of the functions
    BasisNames Automaticnames of basis elements for table headings
    Weights Automaticweights for each data point
    MethodAutomaticmethod used to compute singular values
    ToleranceAutomatictolerance to use in computing singular values
    ConfidenceLevel 0.95confidence level used for confidence intervals
  • With the option IncludeConstant->False, Regress gives the same fit as Fit does.
  • Possible settings for Weights are Automatic, a list of numbers with the same length as the data, or a pure function.
  • With the default setting Weights->Automatic, each data point is given a weight of 1.


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Basic Examples  (2)

Linear regression for a straight line:

Linear regression for a constant plus a sinusoid:

Options  (6)

BasisNames  (1)

Linear regression for a straight line with basis functions labeled b1 and b2:

ConfidenceLevel  (1)

Linear regression with .99 confidence level for confidence intervals:

IncludeConstant  (1)

Linear regression with constant term assumed to be 0:

RegressionReport  (1)

Linear regression with a specific list of report values:

Weights  (2)

Weighted regression with explicit weights for each data element:

Weighted regression with weights equal to the squares of the measured responses:

Wolfram Research (2007), Regress, Wolfram Language function,


Wolfram Research (2007), Regress, Wolfram Language function,


Wolfram Language. 2007. "Regress." Wolfram Language & System Documentation Center. Wolfram Research.


Wolfram Language. (2007). Regress. Wolfram Language & System Documentation Center. Retrieved from


@misc{reference.wolfram_2024_regress, author="Wolfram Research", title="{Regress}", year="2007", howpublished="\url{}", note=[Accessed: 12-July-2024 ]}


@online{reference.wolfram_2024_regress, organization={Wolfram Research}, title={Regress}, year={2007}, url={}, note=[Accessed: 12-July-2024 ]}