In 2016 Longwood University hosted the only Vice Presidential Debate. It was an expensive endeavor that, along with the significant educational benefits to our campus, was also meant to encourage interest and, ultimately, increase the number of applicants to Longwood. Suppose we have the following application data: Year Number of Applicants 2015 5000 2016 5100 2017 6000 2018 6800 2019 7800 Administration points to this post-debate increase in number of applicants as clear evidence that the debate expenses were well worth it - applications increased because of the debate. Why is this conclusion problematic, and why would multiple regression analysis address the problem you identified and, hence, lead to better evidence of a potential impact of the debate on number of applicants?

2. (1 points) Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn't evidence that the advertising is impacting sales. What side would you take and why? Note that this question squarely hits the idea that stats is part science/part art...

4. (6 points) Propose a GENERAL (do not put in numbers for coefficients) regression equation you would like to estimate. Write out the equation (again without specific numbers for coefficients) and define the dependent and independent variables, and indicate how you would measure them. What type of data would you use and, as a result, what is an observation in

your dataset? Finally, why would you want to estimate this regression - what would be the purpose/insight? Keep in mind that this question is worth 60% of this problem set!