In Microsoft Excel and Google Sheets, the CHISQ.TEST function serves as a vital tool for conducting statistical analyses. This function is used to assess the compatibility of observed data frequencies with expected frequencies. It is commonly employed to test the independence between two categorical variables.

Syntax and Examples

The syntax for the CHISQ.TEST function is as follows:

CHISQ.TEST(actual_range, expected_range)

This function takes two parameters, each a range of data cells, and these ranges must be of equal dimensions:

• actual_range: This is the cell range that contains the actual observed frequencies.
• expected_range: This range contains the theoretical (expected) frequencies for each cell.

Example: To test the probability of each face of a die coming up equally over 60 rolls, the following results were obtained:

In this case, the expected frequency for each face is 10 (60/6). The formula in Excel would be:

=CHISQ.TEST(A2:A7, B2:B7)

This formula tests the hypothesis that the die is fair, returning a p-value as the result.

Practical Usage Scenarios

Fairness Test for Dice

Consider the previous example for testing whether a die is fair. If the calculated p-value exceeds, for instance, 0.05, it suggests that the die is fair. Otherwise, it indicates that the die may not be fair.

Evaluating Survey Results

The CHISQ.TEST function can also be used to check whether responses in a survey conform to an expected distribution. For example, suppose 100 people were asked about their favorite fruits, yielding the following results:

To test whether the survey results are random, you can use the following formula:

=CHISQ.TEST(C2:C5, D2:D5)

If the resulting p-value is less than 0.05 (or another significance level), this indicates that the observed distribution significantly differs from the expected distribution.

Daha fazla bilgi: https://support.microsoft.com/tr-tr/office/kİkare-test-işlevi-2e8a7861-b14a-4985-aa93-fb88de3f260f