MIS 175 Section 4 - Second Midterm Examination
DS 101 Version R082 – Sample Exam Questions
Simple Linear Regression Questions
1. In regression analysis, the model in the form [pic] is called
|a. |regression equation |
|b. |correlation equation |
|c. |estimated regression equation |
|d. |regression model |
2. The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = β0 + β1x, is known as
|a. |regression equation |
|b. |correlation equation |
|c. |estimated regression equation |
|d. |regression model |
3. The model developed from sample data that has the form of [pic] is known as
|a. |regression equation |
|b. |correlation equation |
|c. |estimated regression equation |
|d. |regression model |
4. In regression analysis, the unbiased estimate of the variance is
|a. |coefficient of correlation |
|b. |coefficient of determination |
|c. |mean square error |
|d. |slope of the regression equation |
5. The interval estimate of the mean value of y for a given value of x is
|a. |prediction interval estimate |
|b. |confidence interval estimate |
|c. |average regression |
|d. |x versus y correlation interval |
6. The standard error is the
|a. |t-statistic squared |
|b. |square root of SSE |
|c. |square root of SST |
|d. |square root of MSE |
7. If MSE is known, you can compute the
|a. |r square |
|b. |coefficient of determination |
|c. |standard error |
|d. |all of these alternatives are correct |
8. In regression analysis, which of the following is not a required assumption about the error term ε?
|a. |The expected value of the error term is one. |
|b. |The variance of the error term is the same for all values of X. |
|c. |The values of the error term are independent. |
|d. |The error term is normally distributed. |
9. Larger values of r2 imply that the observations are more closely grouped about the
|a. |average value of the independent variables |
|b. |average value of the dependent variable |
|c. |least squares line |
|d. |origin |
10. In a regression and correlation analysis if r2 = 1, then
|a. |SSE must also be equal to one |
|b. |SSE must be equal to zero |
|c. |SSE can be any positive value |
|d. |SSE must be negative |
11. In a regression and correlation analysis if r2 = 1, then
|a. |SSE = SST |
|b. |SSE = 1 |
|c. |SSR = SSE |
|d. |SSR = SST |
12. The coefficient of correlation
|a. |is the square of the coefficient of determination |
|b. |is the square root of the coefficient of determination |
|c. |is the same as r-square |
|d. |can never be negative |
13. In regression analysis, if the independent variable is measured in pounds, the dependent variable
|a. |must also be in pounds |
|b. |must be in some unit of weight |
|c. |cannot be in pounds |
|d. |can be any units |
14. A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation
[pic] = 50,000 - 8X
The above equation implies that an
|a. |increase of $1 in price is associated with a decrease of $8 in sales |
|b. |increase of $8 in price is associated with an increase of $8,000 in sales |
|c. |increase of $1 in price is associated with a decrease of $42,000 in sales |
|d. |increase of $1 in price is associated with a decrease of $8000 in sales |
15. Regression analysis was applied between sales (in $1000) and advertising (in $100) and the following regression function was obtained.
[pic] = 500 + 4 X
Based on the above estimated regression line if advertising is $10,000, then the point estimate for sales (in dollars) is
|a. |$900 |
|b. |$900,000 |
|c. |$40,500 |
|d. |$505,000 |
Multiple Regression Questions
1. The mathematical equation relating the expected value of the dependent variable to the value of the independent variables, which has the form of E(y) = [pic] is
|a. |a simple linear regression model |
|b. |a multiple nonlinear regression model |
|c. |an estimated multiple regression equation |
|d. |a multiple regression equation |
2. The estimate of the multiple regression equation based on the sample data, which has the form of E(y) = [pic]
|a. |a simple linear regression model |
|b. |a multiple nonlinear regression model |
|c. |an estimated multiple regression equation |
|d. |a multiple regression equation |
3. The mathematical equation that explains how the dependent variable y is related to several independent variables x1, x2, ..., xp and the error term ε is
|a. |a simple nonlinear regression model |
|b. |a multiple regression model |
|c. |an estimated multiple regression equation |
|d. |a multiple regression equation |
4. A measure of the effect of an unusual x value on the regression results is called
|a. |Cook’s D |
|b. |Leverage |
|c. |odd ratio |
|d. |unusual regression |
5. In a multiple regression model, the error term ε is assumed to be a random variable with a mean of
|a. |zero |
|b. |-1 |
|c. |1 |
|d. |any value |
6. A regression model in which more than one independent variable is used to predict the dependent variable is called
|a. |a simple linear regression model |
|b. |a multiple regression model |
|c. |an independent model |
|d. |None of these alternatives is correct. |
7. A multiple regression model has the form
[pic]
As x1 increases by 1 unit (holding x2 constant), y is expected to
|a. |increase by 9 units |
|b. |decrease by 9 units |
|c. |increase by 2 units |
|d. |decrease by 2 units |
8. A multiple regression model has the form
[pic]
As X increases by 1 unit (holding W constant), Y is expected to
|a. |increase by 11 units |
|b. |decrease by 11 units |
|c. |increase by 6 units |
|d. |decrease by 6 units |
Exhibit 15-2
A regression model between sales (Y in $1,000), unit price (X1 in dollars) and television advertisement (X2 in dollars) resulted in the following function:
[pic]
For this model SSR = 3500, SSE = 1500, and the sample size is 18.
9. Refer to Exhibit 15-2. The coefficient of the unit price indicates that if the unit price is
|a. |increased by $1 (holding advertising constant), sales are expected to increase by $3 |
|b. |decreased by $1 (holding advertising constant), sales are expected to decrease by $3 |
|c. |increased by $1 (holding advertising constant), sales are expected to increase by $4,000 |
|d. |increased by $1 (holding advertising constant), sales are expected to decrease by $3,000 |
10. Refer to Exhibit 15-2. The coefficient of X2 indicates that if television advertising is increased by $1 (holding the unit price constant), sales are expected to
|a. |increase by $5 |
|b. |increase by $12,000 |
|c. |increase by $5,000 |
|d. |decrease by $2,000 |
Exhibit 15-4
a. [pic]
b. [pic]
c. [pic]
d. [pic]
11. Which equation describes the multiple regression model?
|a. |Equation A |
|b. |Equation B |
|c. |Equation C |
|d. |Equation D |
12. Which equation gives the estimated regression line?
|a. |Equation A |
|b. |Equation B |
|c. |Equation C |
|d. |Equation D |
13. Which equation describes the multiple regression equation?
|a. |Equation A |
|b. |Equation B |
|c. |Equation C |
|d. |Equation D |
Exhibit 15-5
Below you are given a partial Minitab output based on a sample of 25 observations.
| |Coefficient |Standard Error |
|Constant |145.321 |48.682 |
|X1 |25.625 |9.150 |
|X2 |-5.720 |3.575 |
|X3 |0.823 |0.183 |
14. Refer to Exhibit 15-5. The estimated regression equation is
|a. |[pic] |
|b. |[pic] |
|c. |[pic] |
|d. |[pic] |
15. Refer to Exhibit 15-5. The interpretation of the coefficient on X1 is that
|a. |a one unit change in X1 will lead to a 25.625 unit change in Y |
|b. |a one unit change in X1 will lead to a 25.625 unit increase in Y when all other variables are held constant |
|c. |a one unit change in X1 will lead to a 25.625 unit increase in X2 when all other variables are held constant |
|d. |It is impossible to interpret the coefficient. |
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