Multiple linear regression equation

Reference no: EM1313554

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

Reference no: EM1313554

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