https://file.aiquickdraw.com/mathbot/file/1730082843277d1kt8tm5.xlsx

Research question 1: we are interested in studying the association between the employment status and the sex of guessers in the population under consideration.
QUESTIONS:

Report the number of the degrees of freedom [integer]:	

The dataset contains columns that describe individual characteristics, including `sex` and `employment` status, and it includes a contingency table showing the counts of different employment statuses for each sex. 

To address the degrees of freedom in examining the association between employment status and sex, we calculate it based on the contingency table dimensions, specifically:

\[
\text{Degrees of Freedom} = (\text{rows} - 1) \times (\text{columns} - 1)
\]

I'll extract this information and calculate the degrees of freedom.The number of degrees of freedom for examining the association between employment status and sex in this dataset is **3**.

Would you like more details on the calculation or additional assistance? 

Here are some related questions for further exploration:
1. How would changing the categories within employment status affect the degrees of freedom?
2. What statistical test can we apply to determine if there's an association between employment status and sex?
3. How does sample size impact the power of the association test?
4. What are some potential confounding variables in this analysis?
5. How can we interpret a significant association between employment status and sex?

**Tip:** Always check the assumptions of the chi-squared test, especially for sample size and expected counts per cell.

Research question 1: we are interested in studying the association between the employment status and the sex of guessers in the population under consideration.

Explore the statistical association between employment status and sex using a chi-square test for independence. This problem includes key concepts in categorical data analysis, with a focus on calculating degrees of freedom and understanding the chi-square test assumptions. Suitable for college-level statistics students.

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