UNIT4 What is chisquare test Chisquare test is

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UNIT-4

UNIT-4

What is chi-square test? • Chi-square test is a useful measure of comparing experimentally

What is chi-square test? • Chi-square test is a useful measure of comparing experimentally obtained results with those expected theoretically and based on the hypothesis. • It is a non-parametric test.

What are uses of chi-square test? • To test the goodness of fit •

What are uses of chi-square test? • To test the goodness of fit • To test the independence of attributes • To test for single variance. • To test for homogeneity.

What are non-parametric tests? • Non-parametric tests are those do not require that the

What are non-parametric tests? • Non-parametric tests are those do not require that the underlying population to be normal. • These are also called distribution free tests.

Name some of the non-parametric tests. • • Sign test for paired samples and

Name some of the non-parametric tests. • • Sign test for paired samples and one sample. One sample run test Kolmogrov-Smirnov test Rank sum tests: – Mann-Whitney U test – Kruskal-Wallis H test

Mann-Whitney U test • This test is used to determine whether two independent samples

Mann-Whitney U test • This test is used to determine whether two independent samples have been drawn from same population

Kruskal-Wallis H test • It generalizes the analysis of variance to enable us to

Kruskal-Wallis H test • It generalizes the analysis of variance to enable us to dispense with the assumption that the population are normally distributed.

One sample run test • It is a method to determine the randomness with

One sample run test • It is a method to determine the randomness with which the sample items have been selected.

Define a run • A run is a subsequence of one or more identical

Define a run • A run is a subsequence of one or more identical symbols representing a common property of the data

Kolmogrov-Smirnov test • It is a method of determining the goodness of fit between

Kolmogrov-Smirnov test • It is a method of determining the goodness of fit between an observed sample and theoretical probability distribution.