Mastering the Chi-Square Test in R: From Theory to Practice | CodeSignal Learn

Mastering the Chi-Square Test in R: From Theory to Practice | CodeSignal Learn

Introduction to the Chi-Square Test

Hello, learners! Today's topic is a powerful statistical test known as the Chi-Square Test . This test allows us to assess whether significant differences exist between observed and expected frequencies in one or more categories. The test is often used in fields such as health sciences, business, and market research. Are you ready to delve into the Chi-Square Test? Here we go!

What is the Chi-Square Test?

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A Bag of Marbles

Let's say we have documented the color of each marble drawn from a bag of marbles. Given a predicted distribution of marble colors, we want to know whether our observations align with our expectations. Let's use R to examine this situation using the Chi-Square Test! R # Define the colours and their observed and expected counts colors <- c('Red', 'Blue', 'Green', 'Yellow', 'Purple') observed <- c(30, 20, 15, 10, 25) expected <- c(20, 20, 20, 20, 20) # Create a dataframe to store the data data <- data.frame(colors, observed, expected) We now have our observed and expected color distribution for the marbles drawn.

Organizing Data

We can extract the 'Observed' and 'Expected' vectors from the data frame like this: R # Extract observed and expected frequencies observed_frequencies <- data$observed expected_frequencies <- data$expected By denoting the data frame name followed by the $ sign and the column name, we can extract the specified columns.

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Performing the Chi-Square Test

Let's now perform the Chi-Square Test to verify if our observations significantly differ from our assumptions: R # Perform Chi-Square Test chi_square_test <- chisq.test(observed_frequencies, p = expected_frequencies / sum(expected_frequencies)) The chisq.test() function in R computes the chi-square test. It takes the real observed frequencies and frequencies we expect to see. Also, we normalize our expected frequencies by their sum to get a probability distribution (This is a requirement of R's chisq.test() function). R # Print the chi-square statistic and P-value print(paste("Chi-Square Statistic:", chi_square_test$statistic)) # 12.5 print(paste("P-value:", chi_square_test$p.value)) # 0.014 The function provides us with a chi-square statistic and a P-value, which assist us in interpreting the test results.

Calculating Expected Frequencies

Now, let's learn how to calculate expected frequencies, which is useful when we do not have predefined expectations. Suppose you have a bag with 100 different colored marbles. You randomly draw a certain number of marbles from this bag, recording the color of each marble drawn. The attained frequencies of each color are stored in a vector called observed . R # generate a vector of observed frequencies observed <- round(runif(100, min=10, max=50)) If each marble color was equally likely to be selected, the expected frequency for each color would be equal. We calculate this as the total number of draws divided by the number of different marble colors. We create a vector, expected, where each item is this computed expected frequency. Here it is: R # Compute expected frequency expected <- rep(sum(observed) / length(observed), length(observed)) Now, just as we did with the previous marble example, you can compare these observed and expected frequencies using the Chi-Square Test.