Chi-square Test

1. Chi-square Test Swipe

2. Chi-square Test The Chi-square test is intended to test how likely it is that an observed distribution is due to chance. It is also called a "goodness of fit" statistic, because it measures how well the observed distribution of data fits with the distribution that is expected if the variables are independent.

3. Chi-square Test Chi-square test the same as a χ², χ is the Greek symbol Chi. If you have a single measurement variable, you use a Chi-square goodness of fit test. If you have two measurement variables, you use a Chi-square test of independence. There are other Chi-square tests, but these two are the most common.

4. Types of Chi-square tests You use a Chi-square test for hypothesis tests about whether your data is as expected. The basic idea behind the test is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. There are two commonly used Chi-square tests: the Chi-square goodness of fit test and the Chi- square test of independence. Both tests involve variables that divide your data into categories. As a result, people can be confused about which test to use.

5. Chi-Square Goodness of Fit Test The Chi-square goodness of fit test is a statistical hypothesis test 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. You can use the test when you have counts of values for a categorical variable. This test is same as Pearson’s Chi-square test.

6. Using the Chi-square goodness of fit test The Chi-square goodness of fit test checks whether your sample data is likely to be from a specific theoretical distribution. We have a set of data values, and an idea about how the data values are distributed. The test gives us a way to decide if the data values have a “good enough” fit to our idea, or if our idea is questionable.

7. Application Data values that are a simple random sample from the full population. Categorical or nominal data. The Chi-square goodness of fit test is not appropriate for continuous data. A data set that is large enough so that at least five values are expected in each of the observed data categories.

8. Chi-Square Test of Independence The Chi-square test of independence is a statistical hypothesis test used to determine whether two categorical or nominal variables are likely to be related or not. You can use the test when you have counts of values for two categorical variables. If you have only a table of values that shows frequency counts, you can use the test.

9. Using the Chi-square test of independence The Chi-square test of independence checks whether two variables are likely to be related or not. We have counts for two categorical or nominal variables. We also have an idea that the two variables are not related. The test gives us a way to decide if our idea is plausible or not.

10. Application Data values that are a simple random sample from the population of interest. Two categorical or nominal variables. Don't use the independence test with continous variables that define the category combinations. However, the counts for the combinations of the two categorical variables will be continuous. For each combination of the levels of the two variables, we need at least five expected values. When we have fewer than five for any one combination, the test results are not reliable.

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