|
SOLUTIONS
|
-
Functions
- AlternativeHypothesis
- AndersonDarlingTest
- BlomqvistBetaTest
- BrownForsytheTest
- ConoverTest
- CorrelationTest
- CramerVonMisesTest
- DistributionFitTest
- FisherRatioTest
- GoodmanKruskalGammaTest
- HoeffdingDTest
- HypothesisTestData
- IndependenceTest
- JarqueBeraALMTest
- KendallTauTest
- KolmogorovSmirnovTest
- KuiperTest
- LeveneTest
- LocationEquivalenceTest
- LocationTest
- LogRankTest
- MannWhitneyTest
- MardiaCombinedTest
- MardiaKurtosisTest
- MardiaSkewnessTest
- PairedTTest
- PairedZTest
- PearsonChiSquareTest
- PearsonCorrelationTest
- PillaiTraceTest
- ShapiroWilkTest
- SiegelTukeyTest
- SignedRankTest
- SignificanceLevel
- SignTest
- SpearmanRankTest
- TTest
- UnitRootTest
- VarianceEquivalenceTest
- VarianceTest
- VerifyTestAssumptions
- WatsonUSquareTest
- WilksWTest
- ZTest
- Related Guides
Hypothesis Tests
Hypothesis tests give quantitative answers to common questions, such as how good the fit is between data and a particular distribution, whether these distributions have the same mean or median, and whether these datasets have the same variability. Mathematica provides high-level functions for these types of questions and will automatically select the tests applicable for the data and distributions given. The high-level functions typically run more than one test and are able to produce full reports, but there are also specific named hypothesis tests such as the Kolmogorov-Smirnov goodness-of-fit test, or paired
-test. These give more direct control over settings and performance for specific tests.
Featured ExamplesFeatured Examples |
-
Analyze Time Series Model Residuals
-
Choose Parametric Tests or Their Nonparametric Counterparts
-
Compare Maximum-Likelihood and Cramér-von Mises Estimates
-
Compare Survival Rates of Treatment and Control Groups
-
Estimate Parameters and Test Goodness of Fit
-
Experimental Drug Efficacy
-
Global Warming
-
Perform Tests of Independence and Correlation
-
Perform Tests of Location and Scale Simultaneously on Multiple Datasets
-
Test for Goodness of Fit to Any Distribution or Dataset
-
Test for Integrated Time Series
-
Use Hoeffding's D to Quantify and Test Non-monotonic Dependence
ReferenceReference
Goodness-of-Fit Tests
DistributionFitTest — test for goodness-of-fit to a distribution of data
LogRankTest — test whether hazard functions are equal
AndersonDarlingTest ▪ CramerVonMisesTest ▪ JarqueBeraALMTest ▪ KolmogorovSmirnovTest ▪ KuiperTest ▪ MardiaCombinedTest ▪ MardiaKurtosisTest ▪ MardiaSkewnessTest ▪ PearsonChiSquareTest ▪ ShapiroWilkTest ▪ WatsonUSquareTest
Location Tests
LocationTest — test means or mean differences of one or two datasets
LocationEquivalenceTest — compare means or medians of two or more datasets
MannWhitneyTest ▪ PairedTTest ▪ PairedZTest ▪ SignTest ▪ SignedRankTest ▪ TTest ▪ ZTest
Variance Tests
VarianceTest — test variances or variance ratios of one or two datasets
VarianceEquivalenceTest — compare variances of two or more datasets
ConoverTest ▪ BrownForsytheTest ▪ FisherRatioTest ▪ LeveneTest ▪ SiegelTukeyTest
Dependency Tests
IndependenceTest — test whether vectors are independent
CorrelationTest — test for particular correlations between vectors
PearsonCorrelationTest ▪ SpearmanRankTest ▪ KendallTauTest ▪ HoeffdingDTest ▪ GoodmanKruskalGammaTest ▪ BlomqvistBetaTest ▪ WilksWTest ▪ PillaiTraceTest
Other Tests
LogRankTest — test whether hazard rates are equivalent
UnitRootTest — test whether time series data is stationary
Options and Objects for Hypothesis Tests
HypothesisTestData — hypothesis test data generated by hypothesis tests
SignificanceLevel — significance level to use for reporting test conclusions
AlternativeHypothesis — alternative hypothesis for location and variance tests
VerifyTestAssumptions — whether to verify data assumptions through diagnostic tests
