MCordingley\Regression\StatisticsGatherer\Linear::getStandardError PHP Method

getStandardError() public method

Calculates the standard error of the regression. This is the average distance of observed values from the regression line. It's conceptually similar to the standard deviation.
public getStandardError ( ) : float
return float
    public function getStandardError() : float
    {
        return sqrt($this->getMeanSquaredError());
    }

Usage Example

Example #1
0
 public function testStatistics()
 {
     $observations = Observations::fromArray($this->getFeatures(), $this->getOutcomes());
     $coefficients = [1.0954970633022, 0.92451598868827];
     $predictor = new LinearPredictor($coefficients);
     $statisticsGatherer = new LinearStatisticsGatherer($observations, $coefficients, $predictor);
     static::assertEquals(4, $statisticsGatherer->getDegreesOfFreedomTotal());
     static::assertEquals(3, $statisticsGatherer->getDegreesOfFreedomError());
     static::assertEquals(1, $statisticsGatherer->getDegreesOfFreedomModel());
     static::assertEquals(1.94, round($statisticsGatherer->getFStatistic(), 2));
     static::assertEquals(0.39, round($statisticsGatherer->getRSquared(), 2));
     $stdErrorCoefficients = $statisticsGatherer->getStandardErrorCoefficients();
     static::assertEquals(1.51, round($stdErrorCoefficients[0], 2));
     static::assertEquals(0.66, round($stdErrorCoefficients[1], 2));
     static::assertEquals(1.42, round($statisticsGatherer->getStandardError(), 2));
     $tStatistics = $statisticsGatherer->getTStatistics();
     static::assertEquals(0.73, round($tStatistics[0], 2));
     static::assertEquals(1.39, round($tStatistics[1], 2));
 }