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Describe regression to an executive with no background in statistics.

Discuss the process for finding the expected value of a random variable with a basic distribution. Additionally, how would you construct a confidence interval for this value?

How do you determine the sample size for an A/B test, and what factors do you consider? How do you interpret the results of an A/B test, and what factors do you consider when deciding whether to implement the changes?

In what scenarios would you prefer a confusion matrix over an ROC curve for performance assessment, and vice versa?

Do you have any specific tricks you use to minimize residuals in statistical models?

In your opinion, which features of a data visualization are necessary for it to be informative?

You have a friend who has two kids. They tell you that they have at least one boy. What do you think are the odds that the other child is also a boy?

As an expert on linear regression, can you explain the fundamental assumptions of this modeling technique and why they are necessary for an accurate interpretation of results?

Let's assume a family has two kids; you are told that one of them is a boy. What is the probability that the other one is a boy too?

In which part of a neural network does the vanishing gradient tend to be a bigger issue: the initial layers or the ones at the end?