6. A loan officer is interested in examining the determinants of the total dollar value of residential loans made during a month. The officer used Y = B1 + B2X2 + B3X3 + B4X4 +1 to model the relationship, where 

Y is the total dollar value of residential loans in a month (in millions of dollars), 

X2 is the number of loans, 

X3 is the interest rate, and 

X4 is the dollar value of expenditures of the bank on advertising (in thousands of dollars)

Using monthly data from the past 24 months, she obtained the following results: y= 5.7 +0.189x2 - 1.3x3 + 0.32X4

The standard errors 

se(bk) are : se(b1)= 3.2, se(b2)= 0.03, se(b3)=0.062, se(b4)= 0.17. 

Also, R2 = 0.46, adjusted R2 = 0.41, and the reported F statistic is 3.19. 

We wish to test the statistical significance of advertising on the dollar value of residential loans at a=5%. 

a) The test statistic is

b) and we conclude that advertising does have a significant impact on the value of residential loans. 

Enter 1=yes and O=no. 

A. A 1% increase in square footage (SIZE) is associated with a 70% increase in price holding constant the values of the other explanatory variables.

B. A 1% increase in square footage (SIZE) is associated with a 0.7% increase in price holding constant the values of the other explanatory variables.

C. A 1 unit in square footage (SIZE) is associated with a 0.7% increase in price holding constant the values of the other explanatory variables.

D. A 1 foot increase in square footage (SIZE) is associated with a $700 increase in house price holding constant the values of the other explanatory variables.