BUSI508 Final Exam SCORE 100 PERCENT

Quiz

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Question 1 (1 point)

 

In regression terms what does "best fit" mean?

Question 1 options:

a) 

The estimated parameters, b0 and b1, are minimized.

b) 

The estimated parameters, b0 and b1, are linear.

c) 

The error terms are as small as possible.

d) 

The largest error term is as small as possible.

Question 2 (1 point)

 

Why do we create a scatter plot of the data in regression analysis?

Question 2 options:                

a) 

To compute the error terms.

b) 

Because Excel calculates the function from the scatter plot.

c) 

To visually check for a relationship between X and Y.

d) 

To estimate predicted values.

Question 3 (1 point)

 

Based on the following regression output, what proportion of the total variation in Y is explained by X?

 

Regression Statistics

 

 

 

 

Multiple R

0.917214

 

 

 

 

R Square

0.841282

 

 

 

 

Adjusted R Square

0.821442

 

 

 

 

Standard Error

9.385572

 

 

 

 

Observations

10

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

df

SS

MS

F

Significance F

Regression

1

3735.3060

3735.30600

42.40379

0.000186

Residual

8

704.7117

88.08896

 

 

Total

9

4440.0170

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Intercept

31.623780

10.442970

3.028236

0.016353

7.542233

X Variable 1

1.131661

0.173786

6.511819

0.000186

0.730910

Question 3 options:

a) 

0.917214

b) 

0.841282

c) 

0.821442

d) 

9.385572

Question 4 (1 point)

 

The regression function indicates the

Question 4 options:

a) 

average value the dependent variable assumes for a given value of the independent variable.

b) 

actual value the independent variable assumes for a given value of the dependent variable

c) 

average value the dependent variable assumes for a given value of the dependent variable

d) 

actual value the dependent variable assumes for a given value of the independent variable

Question 5 (1 point)

 

The reason an analyst creates a regression model is

Question 5 options:

a) 

to determine the errors in the data collected.

b) 

to predict a dependent variable value given specific independent variable values.

c) 

to predict an independent variable value given specific dependent variable values.

d) 

to verify the errors are normally distributed.

Question 6 (1 point)

 

Based on the following regression output, what is the equation of the regression line?

 

Regression Statistics

 

 

 

 

Multiple R

0.917214

 

 

 

 

R Square

0.841282

 

 

 

 

Adjusted R Square

0.821442

 

 

 

 

Standard Error

9.385572

 

 

 

 

Observations

10

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

df

SS

MS

F

Significance F

Regression

1

3735.3060

3735.30600

42.40379

0.000186

Residual

8

704.7117

88.08896

 

 

Total

9

4440.0170

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Intercept

31.623780

10.442970

3.028236

0.016353

7.542233

X Variable 1

1.131661

0.173786

6.511819

0.000186

0.730910

Question 6 options:

a) 

b) 

c) 

d) 

Question 7 (1 point)

 

R2 is also referred to as

Question 7 options:

a) 

coefficient of determination.

b) 

correlation coefficient.

c) 

total sum of squares.

d) 

regression sum of squares.

Question 8 (1 point)

 

Estimation errors are often referred to as

Question 8 options:

a) 

mistakes.

b) 

constant errors.

c) 

residuals.

d) 

squared errors.

Question 9 (1 point)

 

Regression analysis is a modeling technique

Question 9 options:

a) 

that assumes all data is normally distributed.

b) 

for analyzing the relationship between dependent and independent variables.

c) 

for examining linear trend data only.

d) 

for capturing uncertainty in predicted values of Y.

Question 10 (1 point)

 

What is the correct range for R2 values?

Question 10 options:

a) 

(−1 ≤ R2 ≤ 0)

b) 

(−1 ≤ R2 ≤ 1)

c) 

(0 ≤ R2 ≤ 1)

d) 

(0 ≤ R2 ≤ .5)

Question 11 (1 point)

 

Residuals are assumed to be

Question 11 options:

a) 

dependent, uniformly distributed random variables.

b) 

independent, uniformly distributed random variables.

c) 

dependent, normally distributed random variables.

d) 

independent, normally distributed random variables.

Question 12 (1 point)

 

What goodness-of-fit measure is commonly used to evaluate a multiple regression function?

Question 12 options:

a) 

R2

b) 

adjusted R2

c) 

partial R2

d) 

total R2

Question 13 (1 point)

 

Exhibit 9.2

The following questions are based on the problem description and spreadsheet below.

A paint manufacturer is interested in knowing how much pressure (in pounds per square inch, PSI) builds up inside aerosol cans at various temperatures (degrees Fahrenheit). It has developed the following Excel spreadsheet of the results.

 

 

A

B

C

D

E

F

G

1

Regression Statistics

 

 

 

 

 

2

Multiple R

0.9999

 

 

 

 

 

3

R Square

0.9998

 

 

 

 

 

4

Adjusted R Square

0.9997

 

 

 

 

 

5

Standard Error

0.2853

 

 

 

 

 

6

Observations

10

 

 

 

 

 

7

 

 

 

 

 

 

 

8

ANOVA

 

 

 

 

 

 

9

 

df

SS

MS

F

Significance F

10

Regression

1

2843.4178

2843.4100

34930.220

0.0000

 

11

Residual

8

0.6512

0.0814

 

 

 

12

Total

9

2844.0690

 

 

 

 

13

 

 

 

 

 

 

 

14

 


Coefficients

Standard
Error


t Stat


P-value

Lower
95%

Upper
95%

15

Intercept

38.1923

0.8131

46.9741

0.0000

36.3174

40.0672

16

X Variable 1

1.2447

0.0067

186.8963

0.0000

1.2293

1.2600

 

Refer to Exhibit 9.2. Predict the mean pressure for a temperature of 120 degrees.

Question 13 options:

Question 14 (1 point)

 

Exhibit 9.2

The following questions are based on the problem description and spreadsheet below.

A paint manufacturer is interested in knowing how much pressure (in pounds per square inch, PSI) builds up inside aerosol cans at various temperatures (degrees Fahrenheit). It has developed the following Excel spreadsheet of the results.

 

 

A

B

C

D

E

F

G

1

Regression Statistics

 

 

 

 

 

2

Multiple R

0.9999

 

 

 

 

 

3

R Square

0.9998

 

 

 

 

 

4

Adjusted R Square

0.9997

 

 

 

 

 

5

Standard Error

0.2853

 

 

 

 

 

6

Observations

10

 

 

 

 

 

7

 

 

 

 

 

 

 

8

ANOVA

 

 

 

 

 

 

9

 

df

SS

MS

F

Significance F

10

Regression

1

2843.4178

2843.4100

34930.220

0.0000

 

11

Residual

8

0.6512

0.0814

 

 

 

12

Total

9

2844.0690

 

 

 

 

13

 

 

 

 

 

 

 

14

 


Coefficients

Standard
Error


t Stat


P-value

Lower
95%

Upper
95%

15

Intercept

38.1923

0.8131

46.9741

0.0000

36.3174

40.0672

16

X Variable 1

1.2447

0.0067

186.8963

0.0000

1.2293

1.2600

 

Refer to Exhibit 9.2. Test the significance of the model and explain which values you used to reach your conclusions.

Question 14 options:

Question 15 (1 point)

 

Exhibit 9.2

The following questions are based on the problem description and spreadsheet below.

A paint manufacturer is interested in knowing how much pressure (in pounds per square inch, PSI) builds up inside aerosol cans at various temperatures (degrees Fahrenheit). It has developed the following Excel spreadsheet of the results.

 

 

A

B

C

D

E

F

G

1

Regression Statistics

 

 

 

 

 

2

Multiple R

0.9999

 

 

 

 

 

3

R Square

0.9998

 

 

 

 

 

4

Adjusted R Square

0.9997

 

 

 

 

 

5

Standard Error

0.2853

 

 

 

 

 

6

Observations

10

 

 

 

 

 

7

 

 

 

 

 

 

 

8

ANOVA

 

 

 

 

 

 

9

 

df

SS

MS

F

Significance F

10

Regression

1

2843.4178

2843.4100

34930.220

0.0000

 

11

Residual

8

0.6512

0.0814

 

 

 

12

Total

9

2844.0690

 

 

 

 

13

 

 

 

 

 

 

 

14

 


Coefficients

Standard
Error


t Stat


P-value

Lower
95%

Upper
95%

15

Intercept

38.1923

0.8131

46.9741

0.0000

36.3174

40.0672

16

X Variable 1

1.2447

0.0067

186.8963

0.0000

1.2293

1.2600

 

Refer to Exhibit 9.2. Interpret the meaning of the "Lower 95%" and "Upper 95%" terms in cells F16:G16 of the spreadsheet.

Question 15 options:

Question 16 (1 point)

 

Exhibit 9.2

The following questions are based on the problem description and spreadsheet below.

A paint manufacturer is interested in knowing how much pressure (in pounds per square inch, PSI) builds up inside aerosol cans at various temperatures (degrees Fahrenheit). It has developed the following Excel spreadsheet of the results.

 

 

A

B

C

D

E

F

G

1

Regression Statistics

 

 

 

 

 

2

Multiple R

0.9999

 

 

 

 

 

3

R Square

0.9998

 

 

 

 

 

4

Adjusted R Square

0.9997

 

 

 

 

 

5

Standard Error

0.2853

 

 

 

 

 

6

Observations

10

 

 

 

 

 

7

 

 

 

 

 

 

 

8

ANOVA

 

 

 

 

 

 

9

 

df

SS

MS

F

Significance F

10

Regression

1

2843.4178

2843.4100

34930.220

0.0000

 

11

Residual

8

0.6512

0.0814

 

 

 

12

Total

9

2844.0690

 

 

 

 

13

 

 

 

 

 

 

 

14

 


Coefficients

Standard
Error


t Stat


P-value

Lower
95%

Upper
95%

15

Intercept

38.1923

0.8131

46.9741

0.0000

36.3174

40.0672

16

X Variable 1

1.2447

0.0067

186.8963

0.0000

1.2293

1.2600

 

Refer to Exhibit 9.2. Interpret the meaning of R Square in cell B3 of the spreadsheet.

Question 16 options:

Question 17 (1 point)

 

Why would a manager be interested in analyzing risk?

Question 17 options:

a) 

To determine a most likely outcome.

b) 

To determine a range of outcomes.

c) 

To determine a distribution of outcomes.

d) 

To determine a confidence interval on most likely outcomes.

Question 18 (1 point)

 

If chance or uncertainty is present in a system then there is an element of ____ in the decision-making problem.

Question 18 options:

a) 

danger

b) 

security

c) 

risk

d) 

difficulty

Question 19 (1 point)

 

Simulation is used to

Question 19 options:

a) 

find possible worst case values for the dependent variable(s).

b) 

find worst case and best case values for the dependent variable(s).

c) 

find distribution information for the dependent variable(s).

d) 

find median values for the dependent variable(s).

Question 20 (1 point)

 

A store is considering adding a second clerk. The customer arrival rate at this new server will be

Question 20 options:

a) 

twice the old rate.

b) 

half the old rate.

c) 

the same as the old rate.

d) 

unpredictable.

Question 21 (1 point)

 

One reason to use queuing models in business is

Question 21 options:

a) 

to trade-off the cost of providing service and the cost of customer dissatisfaction

b) 

to maximize the number of service providers

c) 

to minimize the cost of providing service

d) 

all of the above

Question 22 (1 point)

 

Which of the following best describes queuing theory?

Question 22 options:

a) 

The study of arrival rates.

b) 

The study of service times.

c) 

The study of waiting lines.

d) 

The evaluation of service time costs.

Question 23 (1 point)

 

An arrival process is memoryless if

Question 23 options:

a) 

the time until the next arrival depends on the time elapsed since the last arrival.

b) 

the time until the next arrival is based on the time elapsed since the last arrival.

c) 

the time until the next arrival does not depend on the time elapsed since the last arrival.

d) 

the time until the next arrival is based on the arrival rate.

Question 24 (1 point)

 

The memoryless property is also referred to as the ____ property.

Question 24 options:

a) 

Markov

b) 

Erlang

c) 

Poisson

d) 

Normal

Question 25 (1 point)

 

Which of the following is a reason to employ queuing theory?

Question 25 options:

a) 

To reduce customer wait time in line.

b) 

To reduce service times.

c) 

To generate more arrivals to the system.

d) 

To reduce worker idle time in line.

Question 26 (1 point)

 

If a company adds an additional identical server to its M/M/1 system, making an M/M/2 system, what happens to a customer's average service time?

Question 26 options:

a) 

increases

b) 

decreases

c) 

it is unchanged

d) 

depends on the arrival rate

Question 27 (1 point)

 

The number of arrivals to a store follows a Poisson distribution with mean λ = 10/hour. What is the mean inter-arrival time?

Question 27 options:

a) 

6 seconds

b) 

6 minutes

c) 

10 minutes

d) 

10 hours

Question 28 (1 point)

 

If the number of arrivals in a time period follow a Poisson distribution with mean λ then the inter-arrival times follow a(n) ____ distribution with mean ____.

Question 28 options:

a) 

normal; μ

b) 

constant; λ

c) 

exponential; λ

d) 

exponential; 1/λ

Question 29 (1 point)

 

What is the service policy in the queuing systems presented in this chapter that is considered "fair" by the customers?

Question 29 options:

a) 

FIFO

b) 

LIFO

c) 

FILO

d) 

Priority

Question 30 (1 point)

 

The M in M/G/1 stands for

Question 30 options:

a) 

Markovian inter-arrival times.

b) 

Mendelian inter-arrival times.

c) 

Mean inter-arrival times.

d) 

Mathematical inter-arrival times.

Question 31 (1 point)

 

Which type of queuing system are you likely to encounter at an ATM?

Question 31 options:

a) 

Single waiting line, single service station.

b) 

Multiple waiting lines, single service station.

c) 

Single waiting line, multiple service stations.

d) 

Multiple waiting lines, multiple service stations.

Question 32 (1 point)

 

Exhibit 13.1

The following questions are based on the output below.

A store currently operates its service system with 1 operator. Arrivals follow a Poisson distribution and service times are exponentially distributed. The following spreadsheet has been developed for the system.

M/M/s queuing computations

 

Arrival rate

6

 

 

Service rate

8

 

 

Number of servers

1

(max of 40)

 

 

Utilization

75.00%

P(0), probability that the system is empty

0.2500

Lq, expected queue length

2.2500

L, expected number in system

3.0000

Wq, expected time in queue

0.3750

W, expected total time in system

0.5000

Probability that a customer waits

0.7500

 

Refer to Exhibit 13.1. What is the probability that a customer must wait in queue before being served?

Question 32 options:

a) 

0.00

b) 

0.25

c) 

0.75

d) 

1.00

Question 33 (1 point)

 

Exhibit 13.1

The following questions are based on the output below.

A store currently operates its service system with 1 operator. Arrivals follow a Poisson distribution and service times are exponentially distributed. The following spreadsheet has been developed for the system.

M/M/s queuing computations

 

Arrival rate

6

 

 

Service rate

8

 

 

Number of servers

1

(max of 40)

 

 

Utilization

75.00%

P(0), probability that the system is empty

0.2500

Lq, expected queue length

2.2500

L, expected number in system

3.0000

Wq, expected time in queue

0.3750

W, expected total time in system

0.5000

Probability that a customer waits

0.7500

 

Refer to Exhibit 13.1. What is average amount of time spent waiting in line?

Question 33 options:

a) 

0.375

b) 

0.50

c) 

2.25

d) 

3.00

Question 34 (1 point)

 

Exhibit 13.1

The following questions are based on the output below.

A store currently operates its service system with 1 operator. Arrivals follow a Poisson distribution and service times are exponentially distributed. The following spreadsheet has been developed for the system.

M/M/s queuing computations

 

Arrival rate

6

 

 

Service rate

8

 

 

Number of servers

1

(max of 40)

 

 

Utilization

75.00%

P(0), probability that the system is empty

0.2500

Lq, expected queue length

2.2500

L, expected number in system

3.0000

Wq, expected time in queue

0.3750

W, expected total time in system

0.5000

Probability that a customer waits

0.7500

 

Refer to Exhibit 13.1. What is the probability that a customer can go directly into service without waiting in line?

Question 34 options:

a) 

0.00

b) 

0.25

c) 

0.75

d) 

1.00

Question 35 (1 point)

 

Exhibit 13.1

The following questions are based on the output below.

A store currently operates its service system with 1 operator. Arrivals follow a Poisson distribution and service times are exponentially distributed. The following spreadsheet has been developed for the system.

M/M/s queuing computations

 

Arrival rate

6

 

 

Service rate

8

 

 

Number of servers

1

(max of 40)

 

 

Utilization

75.00%

P(0), probability that the system is empty

0.2500

Lq, expected queue length

2.2500

L, expected number in system

3.0000

Wq, expected time in queue

0.3750

W, expected total time in system

0.5000

Probability that a customer waits

0.7500

 

Refer to Exhibit 13.1. How many customers will be in the store on average at any one time?

Question 35 options:

a) 

0.375

b) 

0.50

c) 

2.25

d) 

3.00

Question 36 (1 point)

 

A grocery clerk can serve 20 customers per hour on average and the service time follows an exponential distribution. What is the expected service time per customer?

Question 36 options:

Question 37 (1 point)

 

A grocery clerk can serve 20 customers per hour on average and the service time follows an exponential distribution. What is the probability that a customer's service time is less than 2 minutes?

Question 37 options:

Question 38 (1 point)

 

A grocery clerk can serve 20 customers per hour on average and the service time follows an exponential distribution. What is the probability that a customer's service time is more than 4 minutes?

Question 38 options:

Question 39 (1 point)

 

If we are employing Activity-On-Arc (AOA) network design, the arcs in the network diagram represent

Question 39 options:

a) 

significance relationships.

b) 

precedence relationships.

c) 

tasks to complete.

d) 

task times.

Question 40 (1 point)

 

Which activities are critical in the following diagram?

Question 40 options:

a) 

A, C, D, F

b) 

B, D, E

c) 

A, C, D, E

d) 

A, B, D, E, F

Question 41 (1 point)

 

The purpose of the forward pass in the Critical Path Method (CPM) technique is to

Question 41 options:

a) 

review each of the precedence relationships in the activity network.

b) 

calculate the slack time within each node on the activity network.

c) 

determine the earliest time each activity can start and finish.

d) 

determine the latest time each activity can start and finish.

Question 42 (1 point)

 

What is the formula for the earliest finish time for activity i?

Question 42 options:

a) 

EFTi = ESTi + ti

b) 

EFTi = EFTj + tj

c) 

EFTi = EFTi−1 + ti

d) 

EFTi = ESTi − ti

Question 43 (1 point)

 

What is the latest finish time for activity D in the following diagram?

Question 43 options:

a) 

6

b) 

13

c) 

14

d) 

15

Question 44 (1 point)

 

What is the earliest start time for activity D in the following diagram?

Question 44 options:

a) 

3

b) 

4

c) 

6

d) 

7

Question 45 (1 point)

 

What are the immediate successors of activity D in this network?

Question 45 options:

a) 

A, B, C

b) 

B, C

c) 

E, F

d) 

E

Question 46 (1 point)

 

What are the immediate predecessors of activity D in this network?

Question 46 options:

a) 

A, B, C

b) 

B, C

c) 

E, F

d) 

E, G, H

Question 47 (1 point)

 

How many paths are there in the following project precedence network?

Question 47 options:

a) 

6

b) 

7

c) 

8

d) 

9

Question 48 (1 point)

 

Which activities have slack in the following diagram?

Question 48 options:

a) 

A, C, D, F

b) 

B, E

c) 

A, C, D, E

d) 

B, D, E

Question 49 (1 point)

 

The critical path is the ____ path throughout the network.

Question 49 options:

a) 

longest

b) 

most expensive

c) 

shortest

d) 

least expensive

Question 50 (1 point)

 

The longest path through a network is comprised of the ____ activities.

Question 50 options:

a) 

longest

b) 

essential

c) 

mandatory

d) 

critical

     


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