Latest Updated 2026 (Graded A+) - Georgia Institute Of
Technology
Due Jun 16 at 11:59pm
Points 40
Questions 25
Available Jun 9 at 8am - Jun 16 at 11:59pm
Time Limit 60 Minutes
Allowed Attempts Unlimited
Instructions
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Attempt History
Attempt Time Score
KEPT Attempt 2 12 minutes 36 out of 40
LATEST Attempt 2 12 minutes 36 out of 40
Attempt 1 23 minutes 29.67 out of 40
Submitted Jun 13 at 4pm
Question 1
pts
How would we interpret Cook’s Distance of outliers for a large sample size (n>10,000)? (Hint: an
example was given in the lecture notes for Bike Rental Data)
We can split the data into batches and remove outliers with 4/n – k for each of the k batches.
Use a hard rule: Eliminate all points with a Cook’s Distance greater than 4/n.
Correct!
Look at the Cook's distances holistically even if some of them are larger than the threshold of 4/n.
Use a hard rule: Eliminate all data points less than 4/n.
Module 2 Lesson 2.13 - slide 8
Our rule of thumb, 4/n, does not hold for large n. We need to look at the Cook’s Distance in the lens of
the dataset. The values may end up being small and the 4/n is not always a hard rule to follow.
Question 2
pts
Which statement is true regarding the "Inflated Statistical Significance"?
It consists of building multiple linear regression models and adjusting the predictors to get the lowest p-values.
It’s the problem of drawing conclusions on statistical significance based on p-values and ignoring residual analysis.
Correct!
It can be resolved by sub-sampling and drawing conclusions from resulting empirical distributions of the regression
coefficients.
It can be resolved by outlier elimination and reducing the threshold for predictor selection.
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