public class BivariateDataStatisticsCalculator
extends java.lang.Object
Constructor and Description |
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BivariateDataStatisticsCalculator(DoubleTensor xData,
DoubleTensor yData) |
Modifier and Type | Method and Description |
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double |
estimatedGradient()
Calculate the estimate of the gradient
The regression coefficients (gradient and intercept) can be treated as random variables and estimated from the data.
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double |
estimatedIntercept()
Calculate the estimate of the intercept
The regression coefficients (gradient and intercept) can be treated as random variables and estimated from the data.
|
double |
meanSquaredError()
Calculate the MSE (mean squared error) which is an unbiased estimate of the variance of Y
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long |
size() |
double |
standardErrorForGradient()
Calculate the standard error, which is the unbiased estimate of the standard deviation of the gradient
From https://www2.isye.gatech.edu/~yxie77/isye2028/lecture12.pdf
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double |
standardErrorForIntercept()
Calculate the standard error, which is the unbiased estimate of the standard deviation of the intercept
From https://www2.isye.gatech.edu/~yxie77/isye2028/lecture12.pdf
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double |
xMean() |
double |
yMean() |
public BivariateDataStatisticsCalculator(DoubleTensor xData, DoubleTensor yData)
xData
- the data set for the predictor variable XyData
- the data set for the dependent variable Ypublic long size()
public double xMean()
public double yMean()
public double estimatedGradient()
public double estimatedIntercept()
public double meanSquaredError()
public double standardErrorForGradient()
public double standardErrorForIntercept()