Description
Managerial Economics Christopher Thomas 13th Edition – Test Bank
Chapter 4: BASIC ESTIMATION TECHNIQUES
Multiple Choice
41 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: 0401
42 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: 0401
43 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: 0401
44 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
 Rsquare
 random error term
Answer: d
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 0401
45 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: 0401
46 Which of the following is an example of a timeseries 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 timeseries data sets.
Answer: b
Difficulty: 01 Easy
Topic: The Simple Linear Regression Model
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 0401
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: 0402
48 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: 0401
49 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 tratio 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: 0403
410 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: 0402
411 The critical value of t is the value that a tstatistic 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: 0403
412 To test whether the overall regression equation is statistically significant one uses
a. the tstatistic.
 the R^{2}statistic.
 the Fstatistic.
d. the standard error statistic.
Answer: c
Difficulty: 01 Easy
Topic: Evaluation of the Regression Equation
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 0404
413 In the regression model , a test of the hypothesis that parameter c equals zero is
a. an Ftest.
 an R^{2}test.
c. a zerostatistic.
 a ttest.
 a Ztest.
Answer: d
Difficulty: 01 Easy
Topic: Testing for Statistical Significance
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 0403
414 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 loglinear model
Answer: b
Difficulty: 01 Easy
Topic: Multiple Regression
AACSB: Reflective Thinking
Blooms: Remember
Learning Objective: 0405
415 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: 0401
416 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 10unit 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: 0401
417 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: 0403
418 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: 0403
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