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Managerial Economics Christopher Thomas 13th Edition - Test Bank

Managerial Economics Christopher Thomas 13th Edition – Test Bank

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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

  1. estimate the parameters a and b.
  1. estimate the variables Y and X.
  1. fit a straight line through the data scatter in such a way that the sum of the squared errors is minimized.
  2. 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

  1. ΔX / ΔY.
  2. ΔY / ΔX.
  3. ΔY / Δb.
  4. ΔX / Δb.
  5. 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

    1. the value of X when Y is zero.
    2. the value of Y when X is zero.
    3. the amount that Y changes when X changes by one unit.
    4. 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.

  1. intercept
  2. slope parameter
  3. R-square
  4. 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

    1. shows the actual (or true) relation between the dependent and independent variables.
    2. is used to estimate the population regression line.
    3. connects the data points in a sample.
    4. is estimated by the population regression line.
    5. 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

47 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.

  1. minimizes the distance between the population regression line and the sample regression line.
  2. 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

    1. the value of Y obtained by substituting specific values of X into the sample regression equation.
    2. the value of X associated with a particular value of Y.
    3. the value of X that the regression equation predicts.
    4. the values of the parameters predicted by the estimators.
    5. 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

    1. sample regression equals the population regression.
    2. parameter estimated from the sample equals the true value of the parameter.
    3. value of the t-ratio equals the critical value.
    4. 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.

  1. the R2-statistic.
  2. 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.

  1. an R2-test.

c. a zero-statistic.

  1. a t-test.
  2. 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

RSQUARE

FRATIO

PVALUE ON F

OBSERVATIONS:

18

0.3066

7.076

0.0171

                   VARIABLE

PARAMETER

ESTIMATE

STANDARD

ERROR

TRATIO

PVALUE

                    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

RSQUARE

FRATIO

PVALUE ON F

OBSERVATIONS:

18

0.3066

7.076

0.0171

                   VARIABLE

PARAMETER

ESTIMATE

STANDARD

ERROR

TRATIO

PVALUE

                    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

RSQUARE

FRATIO

PVALUE ON F

OBSERVATIONS:

18

0.3066

7.076

0.0171

                   VARIABLE

PARAMETER

ESTIMATE

STANDARD

ERROR

TRATIO

PVALUE

                    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

RSQUARE

FRATIO

PVALUE ON F

OBSERVATIONS:

18

0.3066

7.076

0.0171

                   VARIABLE

PARAMETER

ESTIMATE

STANDARD

ERROR

TRATIO

PVALUE

                    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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