STATISTICS FOR DATA ANALYTICS - 19
CHI SQUARE TEST
A Chi-Square test is a statistical test used to determine if there is a statistically significant difference between expected frequencies and observed frequencies in one or more categories.
Purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to actual relationship between the variables.
Type of Chi-Square test
Independence test
Goodness of fit.
Chi-Square test
This is used to determine if there is a significant relationship between two categorical variables from a single population.
Independence test
They need to estimate whether two random variables are independent.
Goodness of fit.
THIS TEST IS USED TO DETERMINE WHETHER A VARIABLE IS LIKELY TO COME FROM A SPECIFIED DISTRIBUTION OR NOT.
It is often used to evaluate whether sample data is representative of the full population.
They need to estimate how closely an observed matches and expected distribution. This is referred to as a “ goddess-of-fit” test.
THE CHI SQUARE TEST FOR GOODNESS OF FIT TEST CLAIMS ABOUT POPULATION PROPORTIONS { CATEGORICAL VARIABLES }
IT IS A NON PARAMETRIC TEST THAT IS PERFORMED ON CATEGORICAL [ ORDINAL, NOMINAL ] DATA.
Theory data ( theory categorical distribution ).
Sample data. ( observed categorical distribution ).
With these two data we use Goodness of fit.
How do we use data ?
Step 1 ➖
Taking
Expected data.
Observed data.
Sum of ( excepted data ) must be equal to observed data.
Step 2 ➖
We get chi square test value and p value.
We have to find a critical point by entering a significant value.
Step 3 ➖
Checking
Chi square test value < critical value — reject the null hypothesis.
Chi square test value > critical value — Fail to reject the null hypothesis.
Goodness of fit test :-
In a student class of 100 students, 30 are right handed. Does this class fit the theory that 12% of people are right handed?
It also uses a degree of freedom.
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