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Dec 12, 2025
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Looks good except some aspects of Question 5.
- The following code is not required. The comment "# This is the entire sample" is misleading. If nothing is passed to sample method, it returns one random sample.
one_sample = wine_df.sample() # This is the entire sample
one_sample['color_intensity'].mean()
# Generate the first bootstap point estimate from the wine sample as a proxy for the population
boot1 = wine_df.sample(frac=1, replace=True) # First bootstrap sample
- mean_color_intensity is calculated from bootstrapped data. It should be calculated from the original dataset. Please correct that.
Question 5 (third sub-question) - The corrected sample mean that you get should be looked at with respect to the Confidence interval.
anjali-deshpande-hub
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Dec 19, 2025
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What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)
Added code, refactored existing code, and interpreted code output.
What did you learn from the changes you have made?
I learned that writing even simple code, following a coding example during the class, can be challenging both in terms of syntax understanding (i.e., what each line of code accomplishes) and precision.
Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?
None.
Were there any challenges? If so, what issue(s) did you face? How did you overcome it?
I needed to ask whether the wine dataframe was a sample (i.e., of all wines) or a population (i.e., a group of specific wines) to know if I was running the bootstrap method on all 178 wine observations. I met online with Julia.
How were these changes tested?
I reran my code, and the CI narrowed once I included all 178 wine observations. So, I could see that the code was working.
A reference to a related issue in your repository (if applicable)
I met online during last week's Friday work session and received assistance cleaning up my assignment_three branch.
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