# 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.
• ## Method Summary

Modifier and Type
Method
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 Details

• ### 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