MultipleLinearRegression (Apache Commons Math 3.6.1 API)

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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

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
  - estimateRegressionParametersStandardErrors
```
double[] estimateRegressionParametersStandardErrors()
```
    Returns the standard errors of the regression parameters.
    
    Returns:
    standard errors of estimated regression parameters

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