Interface ChiSquareTest

 All Known Subinterfaces:
UnknownDistributionChiSquareTest
 All Known Implementing Classes:
ChiSquareTestImpl
public interface ChiSquareTest
An interface for ChiSquare tests.This interface handles only known distributions. If the distribution is unknown and should be provided by a sample, then the
UnknownDistributionChiSquareTest
extended interface should be used instead.


Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description double
chiSquare(double[] expected, long[] observed)
double
chiSquare(long[][] counts)
Computes the ChiSquare statistic associated with a chisquare test of independence based on the inputcounts
array, viewed as a twoway table.double
chiSquareTest(double[] expected, long[] observed)
Returns the observed significance level, or pvalue, associated with a Chisquare goodness of fit test comparing theobserved
frequency counts to those in theexpected
array.boolean
chiSquareTest(double[] expected, long[] observed, double alpha)
Performs a Chisquare goodness of fit test evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance levelalpha
.double
chiSquareTest(long[][] counts)
Returns the observed significance level, or pvalue, associated with a chisquare test of independence based on the inputcounts
array, viewed as a twoway table.boolean
chiSquareTest(long[][] counts, double alpha)
Performs a chisquare test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2way table are independent of the rows, with significance levelalpha
.



Method Detail

chiSquare
double chiSquare(double[] expected, long[] observed) throws java.lang.IllegalArgumentException
Computes the ChiSquare statistic comparingobserved
andexpected
frequency counts.This statistic can be used to perform a ChiSquare test evaluating the null hypothesis that the observed counts follow the expected distribution.
Preconditions:
 Expected counts must all be positive.
 Observed counts must all be >= 0.
 The observed and expected arrays must have the same length and their common length must be at least 2.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
observed
 array of observed frequency countsexpected
 array of expected frequency counts Returns:
 chiSquare statistic
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not met

chiSquareTest
double chiSquareTest(double[] expected, long[] observed) throws java.lang.IllegalArgumentException, MathException
Returns the observed significance level, or pvalue, associated with a Chisquare goodness of fit test comparing theobserved
frequency counts to those in theexpected
array.The number returned is the smallest significance level at which one can reject the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts.
Preconditions:
 Expected counts must all be positive.
 Observed counts must all be >= 0.
 The observed and expected arrays must have the same length and their common length must be at least 2.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
observed
 array of observed frequency countsexpected
 array of expected frequency counts Returns:
 pvalue
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not metMathException
 if an error occurs computing the pvalue

chiSquareTest
boolean chiSquareTest(double[] expected, long[] observed, double alpha) throws java.lang.IllegalArgumentException, MathException
Performs a Chisquare goodness of fit test evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance levelalpha
. Returns true iff the null hypothesis can be rejected with 100 * (1  alpha) percent confidence.Example:
To test the hypothesis thatobserved
followsexpected
at the 99% level, usechiSquareTest(expected, observed, 0.01)
Preconditions:
 Expected counts must all be positive.
 Observed counts must all be >= 0.
 The observed and expected arrays must have the same length and their common length must be at least 2.

0 < alpha < 0.5
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
observed
 array of observed frequency countsexpected
 array of expected frequency countsalpha
 significance level of the test Returns:
 true iff null hypothesis can be rejected with confidence 1  alpha
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not metMathException
 if an error occurs performing the test

chiSquare
double chiSquare(long[][] counts) throws java.lang.IllegalArgumentException
Computes the ChiSquare statistic associated with a chisquare test of independence based on the inputcounts
array, viewed as a twoway table.The rows of the 2way table are
count[0], ... , count[count.length  1]
Preconditions:
 All counts must be >= 0.
 The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
 The 2way table represented by
counts
must have at least 2 columns and at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
counts
 array representation of 2way table Returns:
 chiSquare statistic
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not met

chiSquareTest
double chiSquareTest(long[][] counts) throws java.lang.IllegalArgumentException, MathException
Returns the observed significance level, or pvalue, associated with a chisquare test of independence based on the inputcounts
array, viewed as a twoway table.The rows of the 2way table are
count[0], ... , count[count.length  1]
Preconditions:
 All counts must be >= 0.
 The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
 The 2way table represented by
counts
must have at least 2 columns and at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
counts
 array representation of 2way table Returns:
 pvalue
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not metMathException
 if an error occurs computing the pvalue

chiSquareTest
boolean chiSquareTest(long[][] counts, double alpha) throws java.lang.IllegalArgumentException, MathException
Performs a chisquare test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2way table are independent of the rows, with significance levelalpha
. Returns true iff the null hypothesis can be rejected with 100 * (1  alpha) percent confidence.The rows of the 2way table are
count[0], ... , count[count.length  1]
Example:
To test the null hypothesis that the counts incount[0], ... , count[count.length  1]
all correspond to the same underlying probability distribution at the 99% level, usechiSquareTest(counts, 0.01)
Preconditions:
 All counts must be >= 0.
 The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
 The 2way table represented by
counts
must have at least 2 columns and at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
counts
 array representation of 2way tablealpha
 significance level of the test Returns:
 true iff null hypothesis can be rejected with confidence 1  alpha
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not metMathException
 if an error occurs performing the test

