1. Below you are given a partial computer output based on a sample of 16 observations.
|
|
Coefficient
|
Standard Error
|
|
Constant
|
12.924
|
4.425
|
|
X1
|
-3.682
|
2.630
|
|
X2
|
45.216
|
12.560
|
Analysis of Variance:
|
Source of
|
Degrees
|
Sum of
|
Mean
|
|
|
Variation
|
of Freedom
|
Squares
|
Square
|
F
|
|
Regression
|
|
4,853
|
2,426.5
|
|
|
Error
|
|
|
485.3
|
|
The estimated regression equation is:
a. Y = β0 + β1X1 + β2X2 + ε
b. E(Y) = β0 + β1X1 + β2X2
c. = 12.924 - 3.682 X1 + 45.216 X2
d. = 4.425 + 2.63 X1 + 12.56 X2
2. A regression model involving 4 independent variables and a sample of 15 periods resulted in the following sum of squares.
SSR = 165; SSE = 60
The coefficient of determination is
a. 0.3636
b. 0.7333
c. 0.275
d. 0.5
3. The following estimated regression model was developed relating yearly income (Y in $1,000s) of 30 individuals with their age (X1) and their gender (X2) (0 if male and 1 if female).
= 30 + 0.7X1 + 3X2
Also provided are SST = 1,200 and SSE = 384. From the above function, it can be said that the expected yearly income of
a. males is $3 more than females
b. females is $3 more than males
c. males is $3,000 more than females
d. females is $3,000 more than males