You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression you want to control for high school standing and so you run the following regression: GPA 3.4 0.03 HrsWrk 0.7* Frosh-0.3* Soph+ 0.1 * Junior (1.1) (0.013) (0.23) (0.14) (0.08) where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing. a) If you include a dummy variable for seniors, that would cause a Hint: type one word in each blank. For the rest of questions, type a number in one decimal place. b) The expected GPA of a Sophomore who works 10 hours per week is c) The expected GPA of a Senior who works 10 hours per week is d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a higher GPA than Sarah. problem.
You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression you want to control for high school standing and so you run the following regression: GPA 3.4 0.03 HrsWrk 0.7* Frosh-0.3* Soph+ 0.1 * Junior (1.1) (0.013) (0.23) (0.14) (0.08) where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing. a) If you include a dummy variable for seniors, that would cause a Hint: type one word in each blank. For the rest of questions, type a number in one decimal place. b) The expected GPA of a Sophomore who works 10 hours per week is c) The expected GPA of a Senior who works 10 hours per week is d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a higher GPA than Sarah. problem.
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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![You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression, you want to control for high school standing, and so you run the following regression:
\[ \text{GPA} = 3.4 - 0.03 * \text{HrsWrk} - 0.7 * \text{Frosh} - 0.3 * \text{Soph} + 0.1 * \text{Junior} \]
\[
\begin{align*}
(1.1) & \quad (0.013) & \quad (0.23) & \quad (0.14) & \quad (0.08)
\end{align*}
\]
where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing.
**a) If you include a dummy variable for seniors, that would cause a \_\_\_ \_\_\_\_ problem.**
*Hint: type one word in each blank.*
**For the rest of the questions, type a number in one decimal place.**
**b) The expected GPA of a Sophomore who works 10 hours per week is \_\_\_.**
**c) The expected GPA of a Senior who works 10 hours per week is \_\_\_.**
**d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a \_\_\_ higher GPA than Sarah.**
**e) Suppose you rewrite the regression as:**
\[ \text{GPA} = \beta_1\text{HrsWrk} + \beta_2\text{Frosh} + \beta_3\text{Soph} + \beta_3\text{Junior} + \beta_4\text{Senior} \]
where the intercept is dropped and the dummy variable for Senior is added. Given that you estimate this regression on the same sample as above, the coefficient estimate you will get for \(\beta_4\), the coefficient on Senior is \_\_\_.
**f) You run the same regression as in (e), the coefficient estimate you will get for \(\beta_2\), the coefficient on Frosh is \_\](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fcf605b71-789d-48e0-8055-d8207016854b%2F9cb6b9b2-f683-4cea-b78e-31f5f000f1c5%2Fg3v4fd_processed.jpeg&w=3840&q=75)
Transcribed Image Text:You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression, you want to control for high school standing, and so you run the following regression:
\[ \text{GPA} = 3.4 - 0.03 * \text{HrsWrk} - 0.7 * \text{Frosh} - 0.3 * \text{Soph} + 0.1 * \text{Junior} \]
\[
\begin{align*}
(1.1) & \quad (0.013) & \quad (0.23) & \quad (0.14) & \quad (0.08)
\end{align*}
\]
where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing.
**a) If you include a dummy variable for seniors, that would cause a \_\_\_ \_\_\_\_ problem.**
*Hint: type one word in each blank.*
**For the rest of the questions, type a number in one decimal place.**
**b) The expected GPA of a Sophomore who works 10 hours per week is \_\_\_.**
**c) The expected GPA of a Senior who works 10 hours per week is \_\_\_.**
**d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a \_\_\_ higher GPA than Sarah.**
**e) Suppose you rewrite the regression as:**
\[ \text{GPA} = \beta_1\text{HrsWrk} + \beta_2\text{Frosh} + \beta_3\text{Soph} + \beta_3\text{Junior} + \beta_4\text{Senior} \]
where the intercept is dropped and the dummy variable for Senior is added. Given that you estimate this regression on the same sample as above, the coefficient estimate you will get for \(\beta_4\), the coefficient on Senior is \_\_\_.
**f) You run the same regression as in (e), the coefficient estimate you will get for \(\beta_2\), the coefficient on Frosh is \_\
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You have the same regression as in (e) the coefficient estimate you will get for B2, the coefficient on Frosh is ?
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