1. Using the data in HPRICE1.RAW to estimate the model

price = B _o + B ₁ sqrft + B ₂ bdrms + u,

where price is the house price measured in thousands of dollars.

1. Write out the results in equation form.
2. What is the estimated increase in price for a house with one more bedroom, holding square footage constant?
3. What is the estimated increase in price for a house with an additional bedroom that is 140 square feet in size? Compare this to your answer in part (ii).
4. What percentage of the variation in price is explained by square footage and number of bedrooms?
5. The first house in the sample has sqrft = 2,438 and bdrms = 4. Find the predicted selling price for this house from the OLS (ordinary least square) regression line.
6. The actual selling price of the first house in the sample was $300,000 (so price = 300). Find the residual for this house. Does it suggest that the buyer underpaid or overpaid for the house?

2. Using the data set in WAGE2.RAW for this problem. As usual, be sure all of the following regressions contain an intercept.

1. Run a simple regression of IQ on educ to obtain the slope coefficient, say ₁ .
2. Run the simple regression of log ( wage ) on educ, and obtain the slope coefficient, ₁ .
3. Run the multiple regression of log ( wage ) on educ and IQ and obtain the slope coefficients, beta1 hat + beta2 hat, respectively.
4. Verify that ₁ = beta1 hat + beta2 hat ( ₁ )

Price: $9.21

Solution: The downloadable solution consists of 4 pages, 521 words and 8 charts.
Deliverable: Word Document