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
 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

estimateRegressionParametersStandardErrors

double[] estimateRegressionParametersStandardErrors()
Returns the standard errors of the regression parameters.

Returns:
standard errors of estimated regression parameters


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