Метод |
Описание |
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CI ( number $x, $p ) : number |
The confidence interval of the regression for Simple Linear Regression
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1 (x - x̄)²
CI(x,p) = t * sy * / - + --------
√ n SSx |
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DFFITS ( ) : array |
DFFITS
Measures the effect on the regression if each data point is excluded. |
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FProbability ( ) : number |
The probabilty associated with the regression F Statistic |
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FStatistic ( ) : number |
The F statistic of the regression (F test) |
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PI ( number $x, number $p, integer $q = 1 ) : number |
The prediction interval of the regression
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1 1 (x - x̄)²
PI(x,p,q) = t * sy * / - + - + --------
√ q n SSx |
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coefficientOfDetermination ( ) : number |
R² - coefficient of determination |
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cooksD ( ) : array |
Cook's Distance
A measures of the influence of each data point on the regression. |
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correlationCoefficient ( ) : number |
R - correlation coefficient (Pearson's r) |
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createDesignMatrix ( mixed $xs ) : Matrix |
The Design Matrix contains all the independent variables needed for the least squares regression |
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degreesOfFreedom ( ) : number |
The degrees of freedom of the regression |
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errorSD ( ) : number |
Error Standard Deviation |
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getProjectionMatrix ( ) : Matrix |
Project matrix (influence matrix, hat matrix H)
Maps the vector of response values (dependent variable values) to the vector of fitted values (or predicted values). |
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leastSquares ( array $ys, array $xs, integer $order = 1, integer $fit_constant = 1 ) : Matrix |
Linear least squares fitting using Matrix algebra (Polynomial). |
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leverages ( ) : array |
Regression Leverages
A measure of how far away the independent variable values of an observation are from those of the other observations. |
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meanSquareRegression ( ) : number |
Mean square regression
MSR = SSᵣ / p |
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meanSquareResidual ( ) : number |
Mean of squares for error
MSE = SSₑ / ν |
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meanSquareTotal ( ) : number |
Mean of squares total
MSTO = SSOT / (n - 1) |
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r ( ) : number |
R - correlation coefficient
Convenience wrapper for correlationCoefficient |
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r2 ( ) : number |
R² - coefficient of determination
Convenience wrapper for coefficientOfDetermination |
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regressionVariance ( number $x ) : number |
Regression variance |
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residuals ( ) : array |
Get the regression residuals
eᵢ = yᵢ - ŷᵢ
or in matrix form
e = (I - H)y |
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standardErrors ( ) : array |
Standard error of the regression parameters (coefficients) |
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sumOfSquaresRegression ( ) : number |
SSreg - The Sum Squares of the regression (Explained sum of squares) |
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sumOfSquaresResidual ( ) : number |
SSres - The Sum Squares of the residuals (RSS - Residual sum of squares) |
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sumOfSquaresTotal ( ) : number |
SStot - The total Sum Squares |
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tProbability ( ) : array |
The probabilty associated with each parameter's t value |
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tValues ( ) : array |
The t values associated with each of the regression parameters (coefficients) |
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