The question suggests a scenario where there might be a potential issue with a regression analysis. Let's break down each option:

A. The prices should be expressed in cents instead of dollars and the regression performed again.

• Changing units typically does not affect regression results significantly, as the model will just scale accordingly. This option seems unnecessary unless there is a strong reason to work in smaller units.

B. The capacity should be expressed in megabytes instead of terabytes and the regression performed again.

• Similar to option A, switching from terabytes to megabytes would scale the numbers, but it would not change the overall outcome of the regression unless working with smaller units improves model interpretability.

C. The high influence point should be removed and the regression performed again.

• A high influence point refers to an outlier or a data point that significantly affects the regression results. If such a point exists, it may skew the regression line, so removing it and redoing the regression can help improve accuracy. This seems like a valid issue.

D. There are no issues with the regression.

• This would be true only if there were no outliers or influential points and the regression model was performing as expected.

Given this reasoning, the best choice is likely C, since a high influence point could distort the regression results.

Would you like more details or further clarification?

Here are 5 follow-up questions:

1. What is an influential point in regression analysis?
2. How do outliers affect regression models?
3. When is it appropriate to change units (e.g., dollars to cents) in a regression model?
4. How do you identify a high influence point in your dataset?
5. What are common methods to deal with outliers in regression?

Tip: Always visualize your data with a scatterplot to spot any potential outliers before running a regression analysis.