/**
* @covers NumbersPHP\Statistic::covariance
*/
public function testCovariance()
{
$this->assertTrue(\NumbersPHP\Basic::numbersEqual(\NumbersPHP\Statistic::covariance(array(-5, -4, -1, 0, 5, 100), array(-6, 5, 2, 5, 2, 6)), 66.05555555555556, \NumbersPHP\Numbers::EPSILON));
}
<?php require_once __DIR__ . '/../vendor/autoload.php'; // Oh it's about to get interesting. echo '<pre>'; // Consider a data representing total follower count of a // variety of users. $followers = array(100, 50, 1000, 39, 283, 634, 3, 6123); // We can generate a report of summary statistics // which includes the mean, 1st and 3rd quartiles, // and standard deviation. $report = \NumbersPHP\Statistic::report($followers); echo 'Report = ' . var_export($report, true) . "\n"; // Maybe we decide to become a bit more curious about // trends in follower count, so we start conjecturing about // our ability to "predict" trends. // Let's consider the number of tweets those users have. $tweets = array(100, 10, 400, 5, 123, 24, 302, 2000); // Let's calculate the correlation. $correlation = \NumbersPHP\Statistic::correlation($tweets, $followers); echo 'Correlation between tweets and followers: ' . $correlation . "\n"; // Now let's create a linear regression. $linReg = \NumbersPHP\Statistic::linearRegression($tweets, $followers); // $linReg is actually a function we can use to map tweets // onto followers. We'll see that around 1422 followers // are expected if a user tweets 500 times. $estFollowers = $linReg(500); echo 'Estimated number of followers if a user tweets 500 times: ' . $estFollowers; echo '</pre>';
