Description
Managerial Economics Christopher Thomas 13th Edition – Test Bank
Chapter 4: BASIC ESTIMATION TECHNIQUES
Multiple Choice
4-1 For the equation Y = a + bX, the objective of regression analysis is to
- estimate the parameters a and b.
- estimate the variables Y and X.
- fit a straight line through the data scatter in such a way that the sum of the squared errors is minimized.
- both a and c
Answer: d
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4-2 In a linear regression equation of the form Y = a + bX, the slope parameter b shows
- ΔX / ΔY.
- ΔY / ΔX.
- ΔY / Δb.
- ΔX / Δb.
- none of the above
Answer: b
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4-3 In a linear regression equation of the form Y = a + bX, the intercept parameter a shows
-
- the value of X when Y is zero.
- the value of Y when X is zero.
- the amount that Y changes when X changes by one unit.
- the amount that X changes when Y changes by one unit.
Answer: b
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4-4 In a regression equation, the ______ captures the effects of factors that might influence the dependent variable but aren’t used as explanatory variables.
- intercept
- slope parameter
- R-square
- random error term
Answer: d
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4-5 The sample regression line
-
- shows the actual (or true) relation between the dependent and independent variables.
- is used to estimate the population regression line.
- connects the data points in a sample.
- is estimated by the population regression line.
- maximizes the sum of the squared differences between the data points in a sample and the sample regression line.
Answer: b
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4-6 Which of the following is an example of a time-series data set?
a. amount of labor employed in each factory in the U.S. in 2010
b. amount of labor employed yearly in a specific factory from 1990 through 2010
c. average amount of labor employed at specific times of the day at a specific factory in 2010
d. All of the above are time-series data sets.
Answer: b
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4–7 The method of least squares
a. can be used to estimate the explanatory variables in a linear regression equation.
b. can be used to estimate the slope parameters of a linear equation.
- minimizes the distance between the population regression line and the sample regression line.
- all of the above
Answer: b
Difficulty: 01 Easy
Topic: Fitting a Regression Line
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-02
4-8 In a linear regression equation Y = a + bX, the fitted or predicted value of Y is
-
- the value of Y obtained by substituting specific values of X into the sample regression equation.
- the value of X associated with a particular value of Y.
- the value of X that the regression equation predicts.
- the values of the parameters predicted by the estimators.
- the value of Y associated with a particular value of X in the sample.
Answer: a
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4-9 A parameter estimate is said to be statistically significant if there is sufficient evidence that the
-
- sample regression equals the population regression.
- parameter estimated from the sample equals the true value of the parameter.
- value of the t-ratio equals the critical value.
- true value of the parameter does not equal zero.
Answer: d
Difficulty: 02 Medium
Topic: Testing for Statistical Significance
AACSB: Reflective Thinking
Blooms: Understand
Learning Objective: 04-03
4-10 An estimator is unbiased if it produces
a. a parameter from the sample that equals the true parameter.
b. estimates of a parameter that are close to the true parameter.
c. estimates of a parameter that are statistically significant.
d. estimates of a parameter that are on average equal to the true parameter.
e. both b and c
Answer: d
Difficulty: 01 Easy
Topic: Fitting a Regression Line
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-02
4-11 The critical value of t is the value that a t-statistic must exceed in order to
a. reject the hypothesis that the true value of a parameter equals zero.
b. accept the hypothesis that the estimated value of parameter equals the true value.
c. reject the hypothesis that the estimated value of the parameter equals the true value.
d. reject the hypothesis that the estimated value of the parameter exceeds the true value.
Answer: a
Difficulty: 02 Medium
Topic: Testing for Statistical Significance
AACSB: Reflective Thinking
Blooms: Understand
Learning Objective: 04-03
4-12 To test whether the overall regression equation is statistically significant one uses
a. the t-statistic.
- the R2-statistic.
- the F-statistic.
d. the standard error statistic.
Answer: c
Difficulty: 01 Easy
Topic: Evaluation of the Regression Equation
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-04
4-13 In the regression model , a test of the hypothesis that parameter c equals zero is
a. an F-test.
- an R2-test.
c. a zero-statistic.
- a t-test.
- a Z-test.
Answer: d
Difficulty: 01 Easy
Topic: Testing for Statistical Significance
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-03
4-14 If an analyst believes that more than one explanatory variable explains the variation in the dependent variable, what model should be used?
a. a simple linear regression model
b. a multiple regression model
c. a nonlinear regression model
d. a log-linear model
Answer: b
Difficulty: 01 Easy
Topic: Multiple Regression
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-05
4-15 The linear regression equation, Y = a + bX, was estimated. The following computer printout was
obtained:
|
DEPENDENT VARIABLE: |
Y |
R−SQUARE |
F−RATIO |
P−VALUE ON F |
|
|
OBSERVATIONS: |
18 |
0.3066 |
7.076 |
0.0171 |
|
|
VARIABLE |
PARAMETER ESTIMATE |
STANDARD ERROR |
T−RATIO |
P−VALUE |
|
|
INTERCEPT |
15.48 |
5.09 |
3.04 |
0.0008 |
|
|
X |
−21.36 |
8.03 |
−2.66 |
0.0171 |
|
Given the above information, the parameter estimate of a indicates
a. when X is zero, Y is 5.09.
b. when X is zero, Y is 15.48.
c. when Y is zero, X is –21.36.
d. when Y is zero, X is 8.03.
Answer: b
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 04-01
4-16 The linear regression equation, Y = a + bX, was estimated. The following computer printout was
obtained:
|
DEPENDENT VARIABLE: |
Y |
R−SQUARE |
F−RATIO |
P−VALUE ON F |
|
|
OBSERVATIONS: |
18 |
0.3066 |
7.076 |
0.0171 |
|
|
VARIABLE |
PARAMETER ESTIMATE |
STANDARD ERROR |
T−RATIO |
P−VALUE |
|
|
INTERCEPT |
15.48 |
5.09 |
3.04 |
0.0008 |
|
|
X |
−21.36 |
8.03 |
−2.66 |
0.0171 |
|
Given the above information, the parameter estimate of b indicates
a. X increases by 8.03 units when Y increases by one unit.
b. X decreases by 21.36 units when Y increases by one unit.
c. Y decreases by 2.66 units when X increases by one unit.
d. a 10-unit decrease in X results in a 213.6 unit increase in Y.
Answer: d
Difficulty: 02 Medium
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Understand
Learning Objective: 04-01
4-17 The linear regression equation, Y = a + bX, was estimated. The following computer printout was
obtained:
|
DEPENDENT VARIABLE: |
Y |
R−SQUARE |
F−RATIO |
P−VALUE ON F |
|
|
OBSERVATIONS: |
18 |
0.3066 |
7.076 |
0.0171 |
|
|
VARIABLE |
PARAMETER ESTIMATE |
STANDARD ERROR |
T−RATIO |
P−VALUE |
|
|
INTERCEPT |
15.48 |
5.09 |
3.04 |
0.0008 |
|
|
X |
−21.36 |
8.03 |
−2.66 |
0.0171 |
|
Given the above information, what is the critical value of t at the 1% level of significance?
a. 1.746
b. 2.120
c. 2.878
d. 2.921
Answer: d
Difficulty: 02 Medium
Topic: Testing for Statistical Significance
AACSB: Reflective Thinking
Blooms: Apply
Learning Objective: 04-03
4-18 The linear regression equation, Y = a + bX, was estimated. The following computer printout was
obtained:
|
DEPENDENT VARIABLE: |
Y |
R−SQUARE |
F−RATIO |
P−VALUE ON F |
|
|
OBSERVATIONS: |
18 |
0.3066 |
7.076 |
0.0171 |
|
|
VARIABLE |
PARAMETER ESTIMATE |
STANDARD ERROR |
T−RATIO |
P−VALUE |
|
|
INTERCEPT |
15.48 |
5.09 |
3.04 |
0.0008 |
|
|
X |
−21.36 |
8.03 |
−2.66 |
0.0171 |
|
Given the above information, which of the following statements is correct at the 1% level of significance?
a. Both and are statistically significant.
b. Neither nor is statistically significant.
c. is statistically significant, but is not.
d. is statistically significant, but is not.
Answer: c
Difficulty: 02 Medium
Topic: Testing for Statistical Significance
AACSB: Reflective Thinking
Blooms: Understand
Learning Objective: 04-03
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