org.apache.commons.math3.stat.regression

## Interface MultipleLinearRegression

• All Known Implementing Classes:
AbstractMultipleLinearRegression, GLSMultipleLinearRegression, OLSMultipleLinearRegression

public interface MultipleLinearRegression
The multiple linear regression can be represented in matrix-notation.
  y=X*b+u

where y is an n-vector regressand, X is a [n,k] matrix whose k columns are called regressors, b is k-vector of regression parameters and u is an n-vector of error terms or residuals. The notation is quite standard in literature, cf eg Davidson and MacKinnon, Econometrics Theory and Methods, 2004.
Since:
2.0
Version:
$Id: MultipleLinearRegression.java 1416643 2012-12-03 19:37:14Z tn$
• ### Method Summary

Methods
Modifier and Type Method and Description
double estimateRegressandVariance()
Returns the variance of the regressand, ie Var(y).
double[] estimateRegressionParameters()
Estimates the regression parameters b.
double[] estimateRegressionParametersStandardErrors()
Returns the standard errors of the regression parameters.
double[][] estimateRegressionParametersVariance()
Estimates the variance of the regression parameters, ie Var(b).
double[] estimateResiduals()
Estimates the residuals, ie u = y - X*b.
• ### Method Detail

• #### estimateRegressionParameters

double[] estimateRegressionParameters()
Estimates the regression parameters b.
Returns:
The [k,1] array representing b
• #### estimateRegressionParametersVariance

double[][] estimateRegressionParametersVariance()
Estimates the variance of the regression parameters, ie Var(b).
Returns:
The [k,k] array representing the variance of b
• #### estimateResiduals

double[] estimateResiduals()
Estimates the residuals, ie u = y - X*b.
Returns:
The [n,1] array representing the residuals
• #### estimateRegressandVariance

double estimateRegressandVariance()
Returns the variance of the regressand, ie Var(y).
Returns:
The double representing the variance of y
double[] estimateRegressionParametersStandardErrors()