smile.stat

Interface Hypothesis.t

Enclosing interface:

Hypothesis

public static interface Hypothesis.t

t-test.

Method Summary

Modifier and Type	Method and Description
static TTest	test(double[] x, double mean) Independent one-sample t-test whether the mean of a normally distributed population has a value specified in a null hypothesis.
static TTest	test(double[] x, double[] y) Test if the arrays x and y have significantly different means.
static TTest	test(double[] x, double[] y, java.lang.String option) Test if the arrays x and y have significantly different means.
static TTest	test(double r, int df) Test whether the Pearson correlation coefficient, the slope of a regression line, differs significantly from 0.

Method Detail

test

static TTest test(double[] x,
                  double mean)

Independent one-sample t-test whether the mean of a normally distributed population has a value specified in a null hypothesis. Small values of p-value indicate that the array has significantly different mean.

test

static TTest test(double[] x,
                  double[] y)

Test if the arrays x and y have significantly different means. The data arrays are assumed to be drawn from populations with unequal variances. Small values of p-value indicate that the two arrays have significantly different means.

test

static TTest test(double[] x,
                  double[] y,
                  java.lang.String option)

Test if the arrays x and y have significantly different means. Small values of p-value indicate that the two arrays have significantly different means.

Parameters:

option - "equal.var" if the data arrays are assumed to be drawn from populations with the same true variance. "unequal.var if the data arrays are allowed to be drawn from populations with unequal variances. "paired" if x and y are two values (i.e., pair of values) for the same samples.

test

static TTest test(double r,
                  int df)

Test whether the Pearson correlation coefficient, the slope of a regression line, differs significantly from 0. Small values of p-value indicate a significant correlation.

Parameters:

r - the Pearson correlation coefficient.

df - the degree of freedom. df = n - 2, where n is the number of samples used in the calculation of r.