I completed the third assignment.

[SihanHe97]

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)

Completed assignment 3 following the instructions.

What did you learn from the changes you have made?

K-Means clustering is an useful tool to determine the stability of a sample mean estimate.

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

Not really.

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

The confidence interval in Bootstrapping represent different things compared to hypothesis testing, which confused me at the beginning. I reviewed the slides and talked with ChatGPT to clarify their distinctions.

How were these changes tested?

Through print() statement.

A reference to a related issue in your repository (if applicable)

NA

Checklist

• [Yes] I can confirm that my changes are working as intended

[PatelVishakh]

Assignment 3 Complete! Great work.

Recommended changes:

Q5)i) By taking many random samples from our data (with replacement), we see how the mean varies. This helps us know if our mean is stable or if it changes a lot.

Q5)iii) Point Estimate: The sample mean of Color Intensity is 5.06

90% Confidence Interval: We estimate that the true mean of Color intensity is between 4.78 and 5.35.