Statistics

var min = selections.min();

var max = selections.max();

var range = selections.range();

var mean = selections.mean();

var geometricMean = selections.geometricMean();

var harmonicMean = selections.harmonicMean();

var min = selections.quadraticMean();

var min = selections.median();

var firstQuartile = selections.quartile(1);

var firstDecile = selections.decile(1);

var firstPercentile = selections.percentile(1);

var sum = selections.sum();

var product = selections.product();

var sumOfSquares = selections.sumOfSquares();

var sumOfLogs = selections.sumOfLogs();

var variance = selections.variance();

var standardDeviation = selections.standardDeviation();

var skewness = selections.skewness();

var kurtosis = selections.kurtosis();

var pearsonsCorrelation = selections.pearsonsCorrelation("xKey","yKey");

var spearmansCorrelation = selections.spearmansCorrelation("xKey","yKey");

var kendallsCorrelation = selections.kendallsCorrelation("xKey","yKey");

var covariance = selections.covariance("xKey", "yKey");

var regression = diafunc.analyze()
    .regress()
    .linear()
    .simple()
    .observe(1, 1)
    .observe(2, 2)
    .observe(3, 3)
    .build();
var prediction = regression.predict(4);

var regression = selections.analyze()
    .regress()
    .linear()
    .simple()
    .x("xKey")
    .y("yKey")
    .build();
var prediction = regression.predict(42);

var regressionBuilder = diafunc.analyze()
    .regress()
    .linear()
    .multiple();
for(var i=1;i<=100;i++){
    regressionBuilder = regressionBuilder.observe(diafunc.create().tuple().add(i).add(i).add(i), i);
}
var regression = regressionBuilder.build();
regression.predict(diafunc.create().tuple().add(101).add(101).add(101));

var regression = selections.analyze()
    .regress()
    .linear()
    .multiple()
    .x("x1Key")
    .x("x2Key")
    .x("x3Key")
    .y("yKey")
    .build();
var features = diafunc.create().tuple().add(1).add(2).add(3);
var prediction = regression.predict(features);

var result = diafunc.infer()
    .tTest()
    .oneSample()
    .observe(1)
    .observe(2)
    .observe(3)
    .mu(2.5)
    .build();
var tStatistic = result.statistic();
var alpha = 0.1;
var test = result.test(alpha);

var result = selections.infer()
    .tTest("key", 2.5);
var tStatistic = result.statistic();
var alpha = 0.1;
var test = result.test(alpha);

var nullHypothesis = 0.5; // Fair coin
var numberOfSuccesses = 9; // Number of heads (biased coin)
var numberOfTrials = 10;

var result = diafunc.infer()
    .binomialTest()
    .twoSided() // Two sided = Represents a right-sided test. H0: p ≤ p0, H1: p > p0.
    .trials(numberOfTrials)
    .successes(numberOfSuccesses)
    .probability(nullHypothesis)
    .build();

// Significance level, or p-value, associated with a binomial test.
var significanceLevel = result.test();

var alpha = 0.03; // Significance level of the test

// Returns whether the null hypothesis can be rejected with the given confidence level.
// true if signficanceLevel < alpha
var rejected = result.test(alpha);

var distribution = diafunc.distribution().normal();
var sample = distribution.sample();