MultipleLinearRegression (Apache Commons Math 3.6.1 API)

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

- - ### 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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