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ConoverTest

ConoverTest
tests whether the variances of and are equal.
ConoverTest
tests a dispersion measure against .
ConoverTest
returns the value of .
  • ConoverTest performs a hypothesis test on and with null hypothesis that the ratio of the true population variances against .
  • By default a probability value or -value is returned.
  • A small -value suggests that it is unlikely that is true.
  • The data must be univariate .
  • The argument can be any positive real number.
  • ConoverTest assumes the data is symmetric about a common median.
  • ConoverTest returns a HypothesisTestData object htd that can be used to extract additional test results and properties using the form htd["property"].
  • ConoverTest can be used to directly give the value of .
  • Properties related to the reporting of test results include:
"PValue"list of -values
"PValueTable"formatted table of -values
"ShortTestConclusion"a short description of the conclusion of a test
"TestConclusion"a description of the conclusion of a test
"TestData"list of pairs of test statistics and -values
"TestDataTable"formatted table of -values and test statistics
"TestStatistic"list of test statistics
"TestStatisticTable"formatted table of test statistics
  • The test statistic is based on a ratio of the sum of the squared ranks from the first sample to the pooled squared ranks, which is assumed to follow a NormalDistribution under .
  • ConoverTest is sometimes called the squared ranks test and is an alternative to the FisherRatioTest when the is not normally distributed.
  • The following options can be used:
AlternativeHypothesis"Unequal"the inequality for the alternative hypothesis
SignificanceLevel0.05cutoff for diagnostics and reporting
VerifyTestAssumptionsAutomaticset which diagnostic tests to run
  • For the ConoverTest, a cutoff is chosen such that is rejected only if . The value of used for the and properties is controlled by the SignificanceLevel option. This value is also used in diagnostic tests of assumptions, including tests for symmetry. By default is set to .
"Symmetry"verify that all data is symmetric
Test variances from two populations for equality:
Create a HypothesisTestData object for further property extraction:
Properties of the test:
Test the ratio of the variances of two populations against a particular value:
Perform the test with alternative hypothesis :
Test variances from two populations for equality:
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Create a HypothesisTestData object for further property extraction:
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Properties of the test:
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Test the ratio of the variances of two populations against a particular value:
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Perform the test with alternative hypothesis :
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Compare the variances of two populations:
The -values are typically large when the variances are equal:
The -values are typically small when the variances are not equal:
Test whether the ratio of the variances of two populations is a particular value:
The following forms are equivalent:
The order of the datasets should be considered when determining :
Create a HypothesisTestData object for repeated property extraction:
The properties available for extraction:
Extract some properties from a HypothesisTestData object:
The -value and test statistic:
Extract any number of properties simultaneously:
The -value and test statistic:
Tabulate test results:
The values from the table can be extracted using :
Tabulate -values or test statistics:
The -value from the table:
The test statistic from the table:
By default, a two-sided test is performed:
Perform a two-sided test or one of two one-sided alternatives:
Test versus :
Test versus :
Test versus :
Perform tests with one-sided alternatives when a null value is given:
Test versus :
Test versus :
Set the significance level for diagnostic tests:
By default, is used:
The significance level is also used for and :
Diagnostics can be controlled as a group using All or None:
Verify all assumptions:
Check no assumptions:
Diagnostics can be controlled independently:
Check for symmetry:
It is often useful to bypass diagnostic tests for simulation purposes:
The assumptions of the test hold by design, so a great deal of time can be saved:
The results are identical:
Compare the variance of daily point changes in the S&P 500 for the first and second half of the 1990s:
The data is clearly not normally distributed:
The amount of variation for the first half of the 1990s is significantly lower than the second:
Under the test statistic follows a NormalDistribution:
Unlike the FisherRatioTest, the Conover test does not assume normality:
The FisherRatioTest results in underestimation of -values:
The Conover test assumes the data is symmetric about a common median:
The distribution of the test statistic is not standard normal when the data is asymmetric:
The test statistic for the ConoverTest is rank based:
With no ties, Ordering can be used to compute ranks:
The test statistic:
The PearsonChiSquareTest is used to test data for symmetry about a common median:
The data is found to be symmetric and no warning is issued:
The -value in the warning matches that of the PearsonChiSquareTest:
The data should be symmetric about a common median:
The first two datasets are symmetric after accounting for a shift in location:
The last two datasets are not symmetric about their common median:
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