(a)	In chi-square test there are at least two test procedures, mention two of them

In chi-square testing, two common procedures are:

1. **Chi-Square Test of Independence**: This test determines whether there is a significant association between two categorical variables. It is often used in contingency tables to assess if the distribution of sample categorical data matches an expected distribution.

2. **Chi-Square Goodness-of-Fit Test**: This test assesses how well the observed frequencies of a single categorical variable match the expected frequencies. It helps to determine if the sample data fits a particular distribution.

Would you like more details on these tests or have any questions?

### Related Questions:
1. What are the key assumptions behind the Chi-Square Test of Independence?
2. How do you calculate the degrees of freedom in a Chi-Square Goodness-of-Fit test?
3. What are some examples of real-world applications for the Chi-Square Test of Independence?
4. How do you interpret the p-value in a Chi-Square test?
5. Can the Chi-Square test be used with small sample sizes?

**Tip:** When using chi-square tests, ensure your sample size is large enough to meet the test's assumptions, particularly that expected frequencies should generally be 5 or more.

In chi-square test there are at least two test procedures, mention two of them.

Explore the two main chi-square test procedures: the Chi-Square Test of Independence and the Chi-Square Goodness-of-Fit Test. Understand their applications, the key formulas involved, and how to interpret the results. This topic is ideal for undergraduate statistics students.

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