On your way to the cashier's office (to pay yet another dorm damage bill), you overhear U. R. Accepted (the Dean of Admissions) having a violent argument with I. M. Smart (the director of the Computer Center) about an equation that Smart built to understand the number of applications that the school received from high school seniors. In need of an outside opinion, they turn to you to help them evaluate the following regression results (standard errors in parentheses):

\begin{array} { c } \hat { \mathrm { N } } _ { \mathrm { t } } = 150 + 180 \mathrm {~A} _ { \mathrm { t } } + 1.50 \ln \mathrm { T } _ { \mathrm { t } } + 30.0 \mathrm { P } _ { \mathrm { t } } \\( 90 ) \quad ( 1.50 ) \quad ( 60.0 ) \\\overline { \mathrm { R } } ^ { 2 } = 0.50 \quad \mathrm {~N} = 22 \quad \text { (annual) }\end{array}

where: N_t =the number of high school seniors who apply for admission in year t
A_t=the number of people on the admission staff who visit high schools full time
spreading information about the school in year t
T_t =dollars of tuition in year t
P_t =the percent of the faculty in year t that had PhDs in year t
How would you respond if they asked you to:
(a) Discuss the expected signs of the coefficients.
(b) Compare these expectations with the estimated coefficients by using the appropriate tests.
(c) Evaluate the possible econometric problems that could have caused any observed differences between the estimated coefficients and what you expected.
(d) Determine whether the semilog function for T makes theoretical sense.
(e) Make any suggestions you feel are appropriate for another run of the equation.

Most of Chapter 6 Is Concerned with Specification Bias

Essay

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