1) The unstandardized residuals from any type of regression

1) The unstandardized residuals from any type of regression will always sum to 0.

True
False

2) Mr. and Mrs. Bulb are successful photographers looking to open a second studio somewhere within a 100 mile radius of their first studio. Mr. Bulb thinks that they should locate in the wealthiest city, while Mrs. bulb thinks that they should locate in the city with the most teenagers (since many of their clients come in for the Senior Portraits). They decide to settle the question with real data, collected on the other photo shops in the 21 cities they are considering, and using a multiple regression of mean annual photo shop sales (Y, in $1000) on population under age 16 (X1, in 100s) and mean family income (X2, in $1000).
Mr. Bulb drinks a lot of coffee and gets a little shakey after cup number two. If he spilled some coffee on the only paper copy of the table below, would Mrs. Bulb be able to reconstruct the missing information using only the other information in the table and the basic knowledge that N=21? (NOTE: the “table” includes the footnotes).

Yes
No

3) For a bivariate linear regression, will a test of the hypothesis H0: B1 = 0 provide evdience that using the regression equation to make predictions will result in smaller errors than using the mean of Y as the best estimate?

Yes
No

4) TRUE/FALSE: H0: F = 0 is an acceptable specification of the null hypothesis for a 3-variable multiple regression global test (ANOVA test).

True
False

5) When examining a scatterplot of standardized residuals (ZRESID on the Y axis) against standardized predicted values (ZPRED on the X axis) to check model assumptions, which characteristics should the plot display if the regression is fully valid?

ONLY *ZPRED scores ranging from about -3 to +3

ONLY *ZRESID scores ranging from 0 to +3

BOTH *ZPRED scores ranging from about -3 to +3 AND *ZRESID scores ranging from 0 to +3

None of these are required for the regression to be valid

6) Mr. and Mrs. Bulb are successful photographers looking to open a second studio somewhere within a 100 mile radius of their first studio. Mr. Bulb thinks that they should locate in the wealthiest city, while Mrs. bulb thinks that they should locate in the city with the most teenagers (since many of their clients come in for the Senior Portraits). They decide to settle the question with real data, collected on the other photo shops in the 21 cities they are considering, and using a multiple regression of mean annual photo shop sales (Y, in $1000) on population under age 16 (X1, in 100s) and mean family income (X2, in $1000).
Referring to the accompanying output, if the Bulbs need to gross at least $200,000 per year to turn a profit (that’s Y=200), should they consider opening a shop in a city with 6,000 teenagers and a mean income of $18,000 (X1= 60, X2 = 18) based upon their best estimate of average profits?