1) High blood pressure has been identified as a risk factor for heart attacks and strokes. The proportion of U.S. adults with high blood pressure is 0.3. A sample of 81 U.S. adults is chosen.

a) What is the sampling distribution of?

pˆ

b) Find the probability that the proportion of individuals in the sample who have high blood pressure is between 0.24 and 0.35.

c) Find the probability that less than 26% of the people in the sample have high blood pressure.

d) Find the probability that more than 28% of the people in the sample have high blood pressure.

e) Would it be unusual if more than 31% of the individuals in the sample of 81 had high blood pressure? Explain.

2) Researchers obtained the body temperature of 93 healthy humans and found the average body temperature of these 93 people to be 97.3°F. Construct a 99% confidence interval for the population mean body temperature of healthy humans. Assume σ = 0.63°F. Interpret your result.

3) A firm would like to estimate the mean age of all people in the civilian labor force. What size sample would be required to estimate the mean age of all people in the civilian labor force accurate to within 0.75 year of the estimate at the 95% confidence level, given that σ = 15.26 years?

4) The average monthly electric bill of a random sample of 35 residents of a city is $90 with a standard deviation of $24. Construct a 98% confidence interval for the population monthly electric bills of all residents. Interpret your result.

EXPECTED RETURNS

Stocks A and B have the following probability distributions of expected future returns:

1. Calculate the expected rate of return, r_B, for Stock B (r_A = 8.20%.) Do not round intermediate calculations. Round your answer to two decimal places.
   %

2. Calculate the standard deviation of expected returns, σ_A, for Stock A (σ_B = 25.07%.) Do not round intermediate calculations. Round your answer to two decimal places.
   %

3. Now calculate the coefficient of variation for Stock B. Round your answer to two decimal places.

4. Is it possible that most investors might regard Stock B as being less risky than Stock A?  

   1. If Stock B is more highly correlated with the market than A, then it might have the same beta as Stock A, and hence be just as risky in a portfolio sense.
   2. If Stock B is less highly correlated with the market than A, then it might have a lower beta than Stock A, and hence be less risky in a portfolio sense.
   3. If Stock B is less highly correlated with the market than A, then it might have a higher beta than Stock A, and hence be more risky in a portfolio sense.
   4. If Stock B is more highly correlated with the market than A, then it might have a higher beta than Stock A, and hence be less risky in a portfolio sense.
   5. If Stock B is more highly correlated with the market than A, then it might have a lower beta than Stock A, and hence be less risky in a portfolio sense.
   
   
   

Nast stores has derived the following consumer credit-scoring model after years of data collect Y=(0.20 x Employment) + (0.4 x Homeowner) + (0.3 x Cards)

Employment = 1 if employed part-time, and 0 if unemployed

Cards= 1 if presently has 1-5 credit cards, 0 otherwise

Nast determines that a score of at least 0.70 indicates a very good credit risk, and it extends credit to these individuals. (each letter below is a separate question, answer a-d)

PLEASE SHOW ALL WORK

A. If Janice is employed part-time, is a homeowner, and has six credit cards at present, does the model indicate she should receive credit?

B. Janice just got a full-time job and closed two of her credit card accounts. Should she receive credit? Has her credit worthiness increased or decreased, according to model?

C. Your boss mentions that he just returned from a trade-association conference, at which one of the speakers recommended that length of time at present residence (regardless of homeownership status) be include in credit-scoring models. If the weight turns out to be 0.25, how do you think the variable would be coded (i.e., 0 stands for what, 1 stands for what, etc)?

D. Suggest other variables that associated might have left out of the model, and tell how you would code them (i.e., 0,1,2 are assigned to what conditions or variables?).

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Simplifying the ABC System: Equally Accurate Reduced ABC Systems

Selected activities and other information are provided for Patterson Company for its most recent year of operations.

Required:

1. Form reduced system cost pools for activities 7 and 8. Do not round interim calculations. Round your final answers to the nearest dollar.

2. Assign the costs of the reduced system cost pools to Wafer A and Wafer B. Do not round interim calculations. Round your final answers to the nearest dollar.

3. What if the two activities were 1 and 3? Repeat Requirements 1 and 2.

Form reduced system cost pools for activities 1 and 3.

Do not round interim calculations. Round your final answers to the nearest dollar.

Assign the costs of the reduced system cost pools to Wafer A and Wafer B.

Concord Air Express decided to offer direct service from Cleveland to Myrtle Beach. Management must decide between a full-price service using the company’s new fleet of jet aircraft and a discount service using smaller capacity commuter planes. It is clear that the best choice depends on the market reaction to the service Concord Air offers. Management developed estimates of the contribution to profit for each type of service based upon two possible levels of demand for service to Myrtle Beach: strong and weak. The following table shows the estimated quarterly profits (in thousands of dollars):

1. What is the decision to be made, what is the chance event, and what is the consequence for this problem? How many decision alternatives are there? How many outcomes are there for the chance event?
2. If nothing is known about the probabilities of the chance outcomes, what is the recommended decision using the optimistic, conservative, and minimax regret approaches?
3. Suppose that management of Myrtle Air Express believes that the probability of strong demand is 0.7 and the probability of weak demand is 0.3. Use the expected value approach to determine an optimal decision.
4. Suppose that the probability of strong demand is 0.8 and the probability of weak demand is 0.2. What is the optimal decision using the expected value approach?
5. Use graphical sensitivity analysis to determine the range of demand probabilities for which each of the decision alternatives has the largest expected value.
6. PLz show details for each step and give explanations. Thank you^^

1. How will the equivalence point volume change if you titrate the two solutions in question 2? What is the pH of the equivalence point of the two solutions if you titrate with 0.3 M NaOH?

Note: You do not need to answer question 2 in order to do question 3.
The two solutions that question #3 refers to are:
(1) a 10-mL vinegar solution that has a concentration of 5% (w/v%)
(2) a 10-mL vinegar solution that has a concentration of 5% (w/v%) together with 30mL of water

Each one of these solutions is then titrated separately with the NaOH solution. Ka for acetic acid is listed in question #1 ii. Calculate the amount (in mL) of a 1.520M NaOH that is required to add the following acetic acid solutions to prepare a buffer with the corresponding pH: pKa of acetic acid = 4.74

1. 30.00mL of a 5.00% (w/v%) acetic acid; the resulting acetate buffer has a pH of 5.75
2. 50.00mL of a 5.00% (w/v%) acetic acid; the resulting acetate buffer has a pH of 4.98
3. 40.00mL of a 5.00% (w/v%) acetic acid; the resulting acetate buffer has a pH of 4.33

Hint: Set up an equilibrium table for a reaction between acetic acid and NaOH (similar to the examples that we did in lectures). However, in this case, the mole of NaOH in the equilibrium table is a function of volume (i.e. MNaOH.V) and it will be the limiting reagent in the equilibrium table. You will then use the Henderson-Hasselbalch equation to determine the volume of NaOH (i.e. V) that is required to prepare the specific acetate buffer solution at the corresponding pH.

Levi-Strauss Co manufactures clothing. The quality control department measures weekly values of different suppliers for the percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up). The data is in table #11.3.3, and there are some negative values because sometimes the supplier is able to layout the pattern better than the computer ("Waste run up," 2013). Do the data show that there is a difference between some of the suppliers? Test at the 1% level.

Table #11.3.3: Run-ups for Different Plants Making Levi Strauss Clothing