Degrees of Freedom Formula

Oct 13, 2025 by pynkocean.com 46 views

Question 2: Color Code Personality Test

Problem Description

A color code personality test categorizes people into four colors: Red (Power), Blue (Intimacy), Green (Peace), and Yellow (Fun). In general, 10% of people are Red, 20% Blue, 30% Green, and 40% Yellow. We are given a sample of 100 people who took the test, and the results are as follows:

Color	Observed Frequency	Expected Frequency
Red	12	10
Blue	22	20
Green	30	30
Yellow	36	40

Step 1: State the Null and Alternative Hypotheses

The null hypothesis is that the observed frequencies are equal to the expected frequencies, i.e., the color code personality test is fair and accurately categorizes people into the four colors. The alternative hypothesis is that the observed frequencies are not equal to the expected frequencies, i.e., the color code personality test is biased and does not accurately categorize people into the four colors.

Step 2: Calculate the Chi-Square Statistic

To calculate the chi-square statistic, we need to calculate the following:

The difference between the observed and expected frequencies for each color
The squared difference between the observed and expected frequencies for each color
The sum of the squared differences divided by the expected frequency for each color

The chi-square statistic is calculated as follows:

χ² = Σ [(observed frequency - expected frequency)^2 / expected frequency]

Using the data provided, we can calculate the chi-square statistic as follows:

χ² = [(12-10)^2 / 10] + [(22-20)^2 / 20] + [(30-30)^2 / 30] + [(36-40)^2 / 40] χ² = (2^2 / 10) + (2^2 / 20) + (0^2 / 30) + (4^2 / 40) χ² = 0.4 + 0.2 + 0 + 0.4 χ² = 1.0

Step 3: Determine the Degrees of Freedom

The degrees of freedom for a chi-square test is calculated as follows:

df = (number of categories - 1)

In this case, we have 4 categories (Red, Blue, Green, and Yellow), so the degrees of freedom is:

df = (4 - 1) = 3

Step 4: Determine the Critical Region

The critical region for a chi-square test is determined by the alpha level, which is typically set at 0.05. The critical value for a chi-square distribution with 3 degrees of freedom and an alpha level of 0.05 is:

χ² = 7.81

Step 5: Make a Decision

Since the calculated chi-square statistic (1.0) is less than the critical value (7.81), we fail to reject the null hypothesis. This suggests that the observed frequencies are not significantly different from the expected frequencies, and the color code personality test is fair and accurately categorizes people into the four colors.

Conclusion

In this problem, we used a chi-square test to determine whether the observed frequencies of a color code personality test are equal to the expected frequencies. We calculated the chi-square statistic, determined the degrees of freedom, and made a decision based on the critical region. The results suggest that the color code personality test is fair and accurately categorizes people into the four colors.

Chi-Square Test Formula

χ² = Σ [(observed frequency - expected frequency)^2 / expected frequency]

Degrees of Freedom Formula

df = (number of categories - 1)

Critical Region Formula

χ² = χ²_(α, df)

where χ²_(α, df) is the critical value for a chi-square distribution with df degrees of freedom and an alpha level of α.

Chi-Square Test Example

Suppose we have a sample of 100 people who took a color code personality test, and the results are as follows:

Color	Observed Frequency	Expected Frequency
Red	15	10
Blue	25	20
Green	30	30
Yellow	30	40

We can calculate the chi-square statistic as follows:

χ² = [(15-10)^2 / 10] + [(25-20)^2 / 20] + [(30-30)^2 / 30] + [(30-40)^2 / 40] χ² = (5^2 / 10) + (5^2 / 20) + (0^2 / 30) + (10^2 / 40) χ² = 2.5 + 1.25 + 0 + 2.5 χ² = 6.25

Since the calculated chi-square statistic (6.25) is greater than the critical value (7.81), we reject the null hypothesis. This suggests that the observed frequencies are significantly different from the expected frequencies, and the color code personality test is biased and does not accurately categorize people into the four colors.

Chi-Square Test Interpretation

Question 2: Color Code Personality Test

Q&A: Chi-Square Tests

Q: What is a chi-square test?

A: A chi-square test is a statistical test used to determine whether the observed frequencies of a categorical variable are equal to the expected frequencies. It is used to test the null hypothesis that the observed frequencies are equal to the expected frequencies.

Q: What are the steps to perform a chi-square test?

A: The steps to perform a chi-square test are:

State the null and alternative hypotheses
Calculate the chi-square statistic
Determine the degrees of freedom
Determine the critical region
Make a decision based on the critical region

Q: What is the formula for the chi-square statistic?

A: The formula for the chi-square statistic is:

χ² = Σ [(observed frequency - expected frequency)^2 / expected frequency]

Q: What is the formula for the degrees of freedom?

A: The formula for the degrees of freedom is:

df = (number of categories - 1)

Q: What is the critical region for a chi-square test?

A: The critical region for a chi-square test is determined by the alpha level, which is typically set at 0.05. The critical value for a chi-square distribution with df degrees of freedom and an alpha level of 0.05 is:

χ² = χ²_(α, df)

Q: What is the interpretation of a chi-square test?

A: The chi-square test is used to determine whether the observed frequencies of a categorical variable are equal to the expected frequencies. If the calculated chi-square statistic is greater than the critical value, we reject the null hypothesis and conclude that the observed frequencies are significantly different from the expected frequencies.

Q: What are the assumptions of a chi-square test?

A: The assumptions of a chi-square test are:

The data must be categorical
The data must be independent
The expected frequencies must be greater than 5

Q: What are the limitations of a chi-square test?

A: The limitations of a chi-square test are:

It assumes that the data are categorical
It assumes that the data are independent
It assumes that the expected frequencies are greater than 5

Q: What are the advantages of a chi-square test?

A: The advantages of a chi-square test are:

It is a non-parametric test
It is easy to perform
It is a powerful test for categorical data

Q: When to use a chi-square test?

A: A chi-square test should be used when:

You want to determine whether the observed frequencies of a categorical variable are equal to the expected frequencies
You want to test the null hypothesis that the observed frequencies are equal to the expected frequencies
You have categorical data and want to perform a non-parametric test

Q: What are the common mistakes to avoid when performing a chi-square test?

A: The common mistakes to avoid when performing a chi-square test are:

Not checking the assumptions of the test
Not calculating the expected frequencies correctly
Not determining the critical region correctly

Q: What are the common applications of a chi-square test?

A: The common applications of a chi-square test are:

Testing the null hypothesis that the observed frequencies are equal to the expected frequencies
Determining whether the observed frequencies of a categorical variable are equal to the expected frequencies
Performing a non-parametric test for categorical data

Q: What are the common software packages used to perform a chi-square test?

A: The common software packages used to perform a chi-square test are:

R
Python
SPSS
SAS

Q: What are the common research questions that can be answered using a chi-square test?

A: The common research questions that can be answered using a chi-square test are:

Is there a significant difference between the observed frequencies of a categorical variable and the expected frequencies?
Is there a significant association between two categorical variables?
Is there a significant difference between the observed frequencies of a categorical variable and the expected frequencies in a specific population?