If a particular batch of data is approximately normally distributed, we would find that approximately

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Question 19

1. You are offered a choice where you can take a certain amount of $ 20 or partake in a gamble, where you put up $30 and have a 50% chance of winning $50 and 50% chance of winning $10. The decision tree is drawn for you. What should you do--take the $20 or gamble?


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Question 20

1. You are offered a choice where you can take a certain amount of $ 20 or partake in a gamble, where you put up $30 and have a 50% chance of winning $50 and 50% chance of winning $10. The decision tree is drawn for you. How much should you expect to make?


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Question 21

1. A researcher wants to estimate the average price of television sets sold at Wal-Mart. It is assumed from previous studies that ? = 45. To estimate the average price within $10 with 95% confidence, what sample size should be used?

All that is needed is the answer. However, if you would like the possibility of partial credit, show me your work. (Don't worry about formatting or symbols.)

Question 22

1. Customers at Ingles have a choice when paying for their groceries. They may check out and pay using the standard cashier assisted checkout, or they may use a new U-Scan procedure. In the standard procedure an Ingles employee scans each item and another employee puts it in a bag and then into the grocery cart. In the U-Scan procedure the customer scans each item, bags it, and places the bags in the cart themselves. The U-Scan procedure is designed to reduce the time a customer spends in the check out line. The store manger wants to know if the mean checkout time using the standard checkout method is longer than using the U-Scan. She gathered the following sample information. The time is measured from when the customer enters the line until their bags are in the cart. Hence the time includes both waiting in line and checking out.

Mean (minutes) Standard deviation (minutes) Sample Size

Standard 5.5 0.4 50

U-Scan 5.3 0.3 100

What are the null and alternative hypotheses that management would be testing? (Don't worry about formatting your answer.)

 

Question 23

1. Customers at Ingles have a choice when paying for their groceries. They may check out and pay using the standard cashier assisted checkout, or they may use a new U-Scan procedure. In the standard procedure an Ingles employee scans each item and another employee puts it in a bag and then into the grocery cart. In the U-Scan procedure the customer scans each item, bags it, and places the bags in the cart themselves. The U-Scan procedure is designed to reduce the time a customer spends in the check out line. The store manger wants to know if the mean checkout time using the standard checkout method is longer than using the U-Scan. She gathered the following sample information. The time is measured from when the customer enters the line until their bags are in the cart. Hence the time includes both waiting in line and checking out.

Mean (minutes) Standard deviation (minutes) Sample Size

Standard 5.5 0.4 50

U-Scan 5.3 0.3 100

If ? = .05 and if the Excel output shows that p-value = 0.0013, would you reject Ho? Why or why not?

Question 24

1. Customers at Ingles have a choice when paying for their groceries. They may check out and pay using the standard cashier assisted checkout, or they may use a new U-Scan procedure. In the standard procedure an Ingles employee scans each item and another employee puts it in a bag and then into the grocery cart. In the U-Scan procedure the customer scans each item, bags it, and places the bags in the cart themselves. The U-Scan procedure is designed to reduce the time a customer spends in the check out line. The store manger wants to know if the mean checkout time using the standard checkout method is longer than using the U-Scan. She gathered the following sample information. The time is measured from when the customer enters the line until their bags are in the cart. Hence the time includes both waiting in line and checking out.

Mean (minutes) Standard deviation (minutes) Sample Size

Standard 5.5 0.4 50

U-Scan 5.3 0.3 100

Can we conclude that the U-Scan system is better than the standard checkout?

Question 25

1. The following data represent the age of batteries (in hours) for a random sample of 10 full charges on a fifth generation iPod music player:

7.3

10.2

12.9

10.8

12.1

6.6

10.2

9

8.5

7.1

2. 
The following Excel output was obtained. What is the point estimate of the population mean?

3. 

hours

  

Mean

9.47

Standard Error

0.676929

Median

9.6

Mode

10.2

Standard Deviation

2.140639

Sample Variance

4.582333

Kurtosis

-1.09156

Skewness

0.190992

Range

6.3

Minimum

6.6

Maximum

12.9

Sum

94.7

Count

10

Confidence Level(95.0%)

 

 

 

Question 26

1. The following data represent the age of batteries (in hours) for a random sample of 10 full charges on a fifth generation iPod music player:

7.3

10.2

12.9

10.8

12.1

6.6

10.2

9

8.5

7.1

2. 
The following Excel output was obtained. Construct a 95% confidence interval for the mean number of hours the batteries will last in the player.

3. 

hours

Mean

9.47

Standard Error

0.676929

Median

9.6

Mode

10.2

Standard Deviation

2.140639

Sample Variance

4.582333

Kurtosis

-1.09156

Skewness

0.190992

Range

6.3

Minimum

6.6

Maximum

12.9

Sum

94.7

Count

10

Confidence Level(95.0%)

1.531321

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Question 27

1. A sample of scores on an examination given in two different classes is examined. The following Excel output was obtained. Use ? = .05. In order to determine if Class 2 has a different grade from Class 1, what are the null and alternative hypotheses?

t-Test: Two-Sample Assuming Unequal Variances

   
 

class 1

class 2

Mean

78

79

Variance

90

47.333

Observations

9

7

Hypothesized Mean Difference

0

 

df

14

 

t Stat

-0.244

 

P(T<=t) one-tail

0.405

 

t Critical one-tail

1.761

 

P(T<=t) two-tail

0.811

 

t Critical two-tail

2.145

 

Question 28

1. A sample of scores on an examination given in two different classes is examined. The following Excel output was obtained. Use ? = .05. Exactly what p-value did you use to answer to determine if Class 2 has a different average?

t-Test: Two-Sample Assuming Unequal Variances

 

class 1

class 2

Mean

78

79

Variance

90

47.333

Observations

9

7

Hypothesized Mean Difference

0

 

df

14

 

t Stat

-0.244

 

P(T<=t) one-tail

0.405

 

t Critical one-tail

1.761

 

P(T<=t) two-tail

0.811

 

t Critical two-tail

2.145

 

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Question 29

1. A sample of scores on an examination given in two different classes is examined. The following Excel output was obtained. Use ? = .05. Does Class 2 have a different grade?

t-Test: Two-Sample Assuming Unequal Variances

   
 

class 1

class 2

Mean

78

79

Variance

90

47.333

Observations

9

7

Hypothesized Mean Difference

0

 

df

14

 

t Stat

-0.244

 

P(T<=t) one-tail

0.405

 

t Critical one-tail

1.761

 

P(T<=t) two-tail

0.811

 

t Critical two-tail

2.145

 

Question 30

1. Auto, Inc., a fictional automobile manufacturer, is interested in estimating the value derived from a proposed Strategic Alternative (SA). The SA, posed by the CEO of the company, is to acquire an elite, high-end auto company where the average retail price of a car is $80,000. As part of this task, the acquisition team develops a three-year market analysis to determine if adequate market demand exists for luxury automobiles. The team chooses to use the predictive power of regression analysis to help make these projections.

The first step in this process is to understand what drives total market demand for elite, high-end luxury automobiles. After analyzing the industry, the group hypothesizes that gross domestic product, or GDP, is a key predictor of market demand for these automobiles.

To gather historical GDP data, the team turns to online databases located on the Federal Reserve Board website.To gather data on new vehicle sales for elite, high-end luxury cars, it uses the Bureau of Labor Statistics' online databases on new vehicle car sales. (The GDP data is in billions of dollars and the sales data is in thousands of units.)

Is GDP a valid predictor of sales? How do you know?

SUMMARY OUTPUT

     

Regression Statistics

    

Multiple R

0.831453

    

R Square

0.691314

    

Adjusted R Square

0.652728

    

Standard Error

251.102335

    

Observations

10

    
      

ANOVA

     
 

df

SS

MS

F

Significance F

Regression

1

1129665.838

1129665.84

17.9163069

0.0028655

Residual

8

504419.0623

63052.3828

  

Total

9

1634084.9

   
      
 

Coefficients

Standard Error

t Stat

P-value

 

Intercept

739.44976

519.58568

1.42315

0.19250

 

GDP (in $billions)

0.28527

0.06740

4.23277

0.00287

 

Question 31

1. Auto, Inc., a fictional automobile manufacturer, is interested in estimating the value derived from a proposed Strategic Alternative (SA). The SA, posed by the CEO of the company, is to acquire an elite, high-end auto company where the average retail price of a car is $80,000. As part of this task, the acquisition team develops a three-year market analysis to determine if adequate market demand exists for luxury automobiles. The team chooses to use the predictive power of regression analysis to help make these projections.

The first step in this process is to understand what drives total market demand for elite, high-end luxury automobiles. After analyzing the industry, the group hypothesizes that gross domestic product, or GDP, is a key predictor of market demand for these automobiles.

To gather historical GDP data, the team turns to online databases located on the Federal Reserve Board website.To gather data on new vehicle sales for elite, high-end luxury cars, it uses the Bureau of Labor Statistics' online databases on new vehicle car sales. (The GDP data is in billions of dollars and the sales data is in thousands of units.)

If the GDP is at 12,000 billion dollars, how many thousands of units of cars would you expect to be sold?

SUMMARY OUTPUT

     

Regression Statistics

    

Multiple R

0.831453

    

R Square

0.691314

    

Adjusted R Square

0.652728

    

Standard Error

251.102335

    

Observations

10

    
      

ANOVA

     
 

df

SS

MS

F

Significance F

Regression

1

1129665.838

1129665.84

17.9163069

0.0028655

Residual

8

504419.0623

63052.3828

  

Total

9

1634084.9

   
      
 

Coefficients

Standard Error

t Stat

P-value

 

Intercept

739.44976

519.58568

1.42315

0.19250

 

GDP (in $billions)

0.28527

0.06740

4.23277

0.00287

 

Question 32

1. Auto, Inc., a fictional automobile manufacturer, is interested in estimating the value derived from a proposed Strategic Alternative (SA). The SA, posed by the CEO of the company, is to acquire an elite, high-end auto company where the average retail price of a car is $80,000. As part of this task, the acquisition team develops a three-year market analysis to determine if adequate market demand exists for luxury automobiles. The team chooses to use the predictive power of regression analysis to help make these projections.

The first step in this process is to understand what drives total market demand for elite, high-end luxury automobiles. After analyzing the industry, the group hypothesizes that gross domestic product, or GDP, is a key predictor of market demand for these automobiles.

To gather historical GDP data, the team turns to online databases located on the Federal Reserve Board website.To gather data on new vehicle sales for elite, high-end luxury cars, it uses the Bureau of Labor Statistics' online databases on new vehicle car sales. (The GDP data is in billions of dollars and the sales data is in thousands of units.)

1. Interpret b1.

SUMMARY OUTPUT

     

Regression Statistics

    

Multiple R

0.831453

    

R Square

0.691314

    

Adjusted R Square

0.652728

    

Standard Error

251.102335

    

Observations

10

    
      

ANOVA

     
 

df

SS

MS

F

Significance F

Regression

1

1129665.838

1129665.84

17.9163069

0.0028655

Residual

8

504419.0623

63052.3828

  

Total

9

1634084.9

   
      
 

Coefficients

Standard Error

t Stat

P-value

 

Intercept

739.44976

519.58568

1.42315

0.19250

 

GDP (in $billions)

0.28527

0.06740

4.23277

0.00287

    • 11 years ago