Chi-squared test in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the Chi-squared test?

The Chi-squared test checks if there is a significant association between two categorical variables by comparing observed data with expected data under the assumption of independence.

beginner

In R, which function is commonly used to perform a Chi-squared test on a contingency table?

The chisq.test() function is used to perform the Chi-squared test on a contingency table in R.

beginner

What kind of data is required to use the Chi-squared test?

The data must be categorical and organized in a contingency table showing counts or frequencies for each category combination.

intermediate

What does a low p-value from a Chi-squared test indicate?

A low p-value (usually less than 0.05) suggests that the observed data is unlikely under the assumption of independence, so we reject the null hypothesis and conclude there is an association between variables.

intermediate

How do you interpret the Chi-squared statistic value?

The Chi-squared statistic measures how much the observed counts deviate from the expected counts. Larger values indicate greater difference and stronger evidence against independence.

Which R function performs the Chi-squared test?

A t.test()

B chisq.test()

C lm()

D cor()

The chisq.test() function is designed for Chi-squared tests on categorical data.

What type of data is needed for a Chi-squared test?

A Categorical data in a contingency table

B Numerical continuous data

C Time series data

D Text data

Chi-squared tests require categorical data arranged in a contingency table showing counts.

A p-value less than 0.05 in a Chi-squared test means:

A Variables are associated

B Data is numerical

C Test is invalid

D Variables are independent

A low p-value means we reject independence and conclude variables are associated.

What does the Chi-squared statistic measure?

A Correlation coefficient

B Mean of the data

C Difference between observed and expected counts

D Variance of the data

It measures how much observed counts differ from expected counts under independence.

Which of these is NOT a requirement for the Chi-squared test?

A Data must be categorical

B Expected counts should be sufficiently large

C Data arranged in a contingency table

D Data must be normally distributed

Normal distribution is not required for the Chi-squared test.

Explain how to perform a Chi-squared test in R using a contingency table.

Think about how you organize data and call the test function.

Describe what a significant Chi-squared test result tells you about two categorical variables.

Focus on what the test result means for the relationship between variables.