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The question in the image asks: 

"A researcher wants to determine the strength of the association between 50 students' Body Mass Index and their cholesterol levels. What test would you advise him to conduct?"

To determine the strength of the association between two continuous variables (Body Mass Index and cholesterol levels), the appropriate test would be **Pearson's correlation coefficient** if the data is normally distributed. This statistical test measures the linear correlation between the two variables, quantifying the strength and direction of their relationship with a value between -1 and 1.

If the data is not normally distributed, the researcher could use **Spearman's rank correlation** as a non-parametric alternative.

Would you like more details or have any further questions on this? 

Here are five related questions for expanding your knowledge:
1. What assumptions must be met to use Pearson's correlation coefficient?
2. How do you check if two variables are normally distributed?
3. What are the differences between Pearson's and Spearman's correlation tests?
4. How can we visualize the relationship between BMI and cholesterol levels before conducting the correlation test?
5. What does a correlation coefficient of 0, 1, or -1 imply?

**Tip:** Always plot the data first, like with a scatterplot, to visually inspect the relationship before performing a correlation test.

A researcher wants to determine the strength of the association between 50 students' Body Mass Index and their cholesterol levels. What test would you advise him to conduct?

Learn how to assess the relationship between Body Mass Index (BMI) and cholesterol levels for 50 students using Pearson's correlation coefficient. This statistical method determines the strength and direction of the association between two continuous variables. Suitable for students in advanced high school or undergraduate-level statistics courses.

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