broadwick.statistics.regression

Class SimpleLinearRegression

java.lang.Object

broadwick.statistics.regression.SimpleLinearRegression

public class SimpleLinearRegression
extends Object

Estimates an ordinary least squares regression model with one independent variable. y = intercept + slope * x

Field Summary

Fields

Modifier and Type	Field and Description
private org.apache.commons.math3.stat.regression.SimpleRegression	regression

Constructor Summary

Constructors

Constructor and Description
SimpleLinearRegression() Create an empty SimpleLinearRegression instance.

Method Summary

Modifier and Type	Method and Description
void	addData(double x, double y) Adds the observation (x,y) to the regression data set.
double	getIntercept() Returns the intercept of the estimated regression line.
double	getInterceptStdErr() Returns the standard error of the intercept estimate, usually denoted s(b0).
long	getN() Returns the number of observations that have been added to the model.
double	getR() Returns Pearson's product moment correlation coefficient, usually denoted r.
double	getSlope() Returns the slope of the estimated regression line.
double	getSlopeStdErr() Returns the standard error of the slope estimate.
double	predict(double x) Returns the "predicted" y value associated with the supplied x value, based on the data that has been added to the model when this method is activated.
void	removeData(double x, double y) Removes the observation (x,y) from the regression data set, mirroring the addData method.
String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

regression

private org.apache.commons.math3.stat.regression.SimpleRegression regression

Constructor Detail

SimpleLinearRegression

public SimpleLinearRegression()

Create an empty SimpleLinearRegression instance.

Method Detail

addData

public final void addData(double x,
                          double y)

Adds the observation (x,y) to the regression data set. Uses updating formulas for means and sums of squares defined in "Algorithms for Computing the Sample Variance: Analysis and Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. 1983, American Statistician, vol. 37, pp. 242-247, referenced in Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985. Note: this uses the apache commons math library.

Parameters:

x - independent variable value

y - dependent variable value

removeData

public final void removeData(double x,
                             double y)

Removes the observation (x,y) from the regression data set, mirroring the addData method. The method has no effect if there are no points of data (i.e. n=0)

Parameters:

x - independent variable value

y - dependent variable value

getSlope

public final double getSlope()

Returns the slope of the estimated regression line. At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:

the slope of the regression line

getIntercept

public final double getIntercept()

Returns the intercept of the estimated regression line. At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:

the intercept of the regression line

getN

public final long getN()

Returns the number of observations that have been added to the model.

Returns:

n number of observations that have been added.

predict

public final double predict(double x)

Returns the "predicted" y value associated with the supplied x value, based on the data that has been added to the model when this method is activated.

predict(x) = intercept + slope * x

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Parameters:

x - input x value

Returns:

predicted y value

getR

public final double getR()

Returns Pearson's product moment correlation coefficient, usually denoted r. At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:

Pearson's r

getInterceptStdErr

public final double getInterceptStdErr()

Returns the standard error of the intercept estimate, usually denoted s(b0). If there are fewer that three observations in the model, or if there is no variation in x, this returns Double.NaN.

Returns:

standard error associated with intercept estimate

getSlopeStdErr

public final double getSlopeStdErr()

Returns the standard error of the slope estimate. If there are fewer that three data pairs in the model, or if there is no variation in x, this returns Double.NaN.

Returns:

standard error associated with slope estimate

toString

public final String toString()

Overrides:

toString in class Object