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Can we consider R-square to be an all-encompassing measure for linear regression model accuracy? When faced with a low R-square measurement, how do you assess the adequacy of the regression model?

What are type I & type II errors in hypothesis testing? Which one do you think is worse?

Share a time when you had to make a quick decision.

In your next job at AppDynamics, what are you looking for?

How would you define batch normalization and dropout within the context of machine learning?

What is the reason behind your search for a new job?

What steps would you take to generate 1000 normal distribution data points with mean=x, variance=y in R, summarize the data, and plot the density? Then, how would you achieve the same outcome using Python in the Codility code editor?

How did you handle a situation where you couldn't meet a deadline?

How can the principle of regression be conveyed to an executive without relying on statistical jargon?

Given a set of (date, duration) tuples, how would you filter by a specific date range and then compute the average duration and rolling sum for each date within that range?