Contents
What sort of data is most appropriate for chi-square tests?
The Chi Square statistic is commonly used for testing relationships between categorical variables. The null hypothesis of the Chi-Square test is that no relationship exists on the categorical variables in the population; they are independent.
What kind of data Cannot use for chi-square tests?
The data should not consist of paired samples or groups or we can say the observations should be independent of each other. When more than 20% of the expected frequencies have a value of less than 5 then Chi-square cannot be used.
When to not use a chi square test?
The data should not consist of paired samples or groups or we can say the observations should be independent of each other When more than 20% of the expected frequencies have a value of less than 5 then Chi-square cannot be used. To tackle this problem: Either one should combine the categories only if it is relevant or obtain more data
Which is the right side of the chi square distribution?
Having degrees of freedom =1 (calculated with contingency table) and alpha =0.05 the Chi-Square value is 3.84. The Chi-Square values can be determined with the Chi-Square table. The chi-square distribution is the right side since the difference in Observed and Expected is large.
What is the critical value of chi square?
We can find this in the below chi-square table against the degrees of freedom (number of categories – 1) and the level of significance: In this case, the degrees of freedom are 5-1 = 4. So, the critical value at 5% level of significance is 9.49. Our obtained value of 32.5 is much larger than the critical value of 9.49.
How to perform the chi-square test for feature selection?
Gender of a customer with values as Male/Female as the predictor and Exited describes whether a customer is leaving the bank with values Yes/No as the response. In this test we will check is there any relationship between Gender and Exited. Steps to perform the Chi-Square Test: