The adjusted R 2 statistic a. is always greater than R2 unadjusted b. is equal to...
- The adjusted R 2 statistic
|
a. |
is always greater than R2 unadjusted |
|
|
b. |
is equal to the value of unadjusted R2 |
|
|
c. |
accounts for the parameters in the multiple regression model |
|
|
d. |
adjusts R2 for the degrees of freedom in the multiple regression model |
- The objective function in regression analysis is
|
a. |
|
|
|
b. |
|
|
|
c. |
|
|
|
d. |
|
|
In regression analysis, the total variation is:
|
a. |
the sum of the explained variation and unexplained variation |
|
|
b. |
the sum of the squared deviations of each value of y from the mean of x |
|
|
c. |
equal to R2 |
|
|
d. |
the standard error of the forecast |
- The regression residuals are computed as
|
a. |
|
|
|
b. |
|
|
|
c. |
|
|
|
d. |
|
Homework Answers
The adjusted R squared statistic is a metric which adjusts R squared for the degrees of freedom in the multiple regression model.
Thus, if there are new variables being added without adding much value, the adjusted R squared captures it effectively.
The objective function in regression analysis is option d.
|
|
In regression analysis, we always look to minimize the squared difference between the actual values and the predicted values.
In regression analysis, the total variation is the sum of the explained variation and unexplained variation.
The sum of these variations is the total error in the regression analysis.
The regression residuals are computed as option b.
|
|
This is essentially the difference between the actual value of y and the predicted value of y.
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A) r^2adj=
B) About BLANK % (number value) of the variation in y can be
explained by the relationship between variables
C) The value of r^2adj is larger than/smaller than/equal to the
value of r^2.
The calculation of the coefficient of determination r2 depends on the number of independent variables. An adjusted value of r2 based on the number of degrees of freedom is calculated using the formula shown below, where n is...
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2011.4 log()0.02 r2 0.06 Model D: Model E: y = 5.4 + 0.82i --3.4 55.1 log(0.020 2 + 1.2r2 0.2 (1x2) Ceteris Paribus: (a) In Model A: If x1 increases 6 to 8 by 2 units, then the predicted change in y is Δy =...
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