(a) The directors of Happy Trails are keen to purchase a block of land next door to the company’s business premises for $150,000 which will allow the company to expand. They set up a subsidiary company to purchase the land with the intention that Happy Trails would become the sole shareholder of the subsidiary. Happy Trails contributed $10,000 capital as a deposit to the subsidiary with the intention that the balance of the purchase price would be raised by way of a bank loan. However, the bank wanted $40,000 deposit and Happy Trails did not have enough cash to make up the shortfall. The three directors of Happy Trails decide to contribute $10,000 each personally in order for the sale to proceed. By doing so, they became shareholders of the subsidiary. One year later Happy Trails decides that they don’t need to expand, and the land is sold for $300,000 and the directors who had put in their own money to finance the deal made a large profit. Miss Eli and Mr Wu argue that the whole of the profit belonged to Happy Trails and that the three directors should not be allowed to keep their profit. Advise Miss Eli and Mr Wu. (b) The three directors recently had a serious disagreement about the direction the company should take and as a result they are hardly speaking to each other. This means that it is very difficult for any business decisions to be made and the company is suffering financially. Mr Wu is very concerned as a shareholder. Advise Mr Wu.

A suburban hotel derives its revenue from its hotel and restaurant operations. The owners are interested in the relationship between the number of rooms occupied on a nightly basis and the revenue per day in the restaurant. Below is a sample of 25 days (Monday through Thursday) from last year showing the restaurant income and number of rooms occupied.

1. Choose the scatter diagram that best fits the data.

• Scatter diagram 1

• Scatter diagram 2

• Scatter diagram 3

2. Determine the coefficient of correlation between the two variables. (Round your answer to 3 decimal places.)

Pearson correlation _______

1. c-1. State the decision rule for 0.01 significance level: H₀: ρ ≤ 0; H₁: ρ > 0. (Round your answer to 3 decimal places.)

Reject H0 if t > ________

1. c-2. Compute the value of the test statistic. (Round your answer to 2 decimal places.)

Value of the test statistic ______

4. What percent of the variation in revenue in the restaurant is accounted for by the number of rooms occupied? (Round your answer to 1 decimal place.)

_____ % of the variation in revenue is explained by variation in occupied rooms.

Gallatin Carpet Cleaning is a small, family-owned business operating out of Bozeman, Montana. For its services, the company has always charged a flat fee per hundred square feet of carpet cleaned. The current fee is $22.75 per hundred square feet. However, there is some question about whether the company is actually making any money on jobs for some customers—particularly those located on remote ranches that require considerable travel time. The owner’s daughter, home for the summer from college, has suggested investigating this question using activity-based costing. After some discussion, she designed a simple system consisting of four activity cost pools. The activity cost pools and their activity measures appear below:

The total cost of operating the company for the year is $361,000 which includes the following costs:

Resource consumption is distributed across the activities as follows:

Job support consists of receiving calls from potential customers at the home office, scheduling jobs, billing, resolving issues, and so on.

Required:

1. Prepare the first-stage allocation of costs to the activity cost pools.

2. Compute the activity rates for the activity cost pools. (Round your answers to 2 decimal places.)

3. The company recently completed a 600 square foot carpet-cleaning job at the Flying N Ranch—a 60-mile round-trip journey from the company’s offices in Bozeman. Compute the cost of this job using the activity-based costing system. (Round your intermediate calculations and final answer to 2 decimal places.)

4. The revenue from the Flying N Ranch was $136.50 (600 square feet @ $22.75 per hundred square feet). Calculate the customer margin earned on this job. (Round your intermediate calculations and final answers to 2 decimal places.)

In a world of get-rich-quick schemes, few are mentioned more frequently than lawsuits. One of the reasons is the infamous McDonald’s coffee case (Liebeck v. McDonald’s Restaurants). This is what happened in 1992 in Albuquerque, New Mexico. Stella Liebeck, seventy-nine, was riding in a car driven by her grandson. They stopped at a McDonald’s drive-through, where she purchased a Styrofoam cup of coffee. Wanting to add cream and sugar, she squeezed the cup between her knees and pulled off the plastic lid. The entire thing spilled back into her lap. The searing liquid left her with extensive third-degree burns. Eight days of hospitalization—which included skin grafts—were required. Initially, she sought $20,000 from McDonald’s, which was more or less the cost of her medical bills. McDonald’s refused. They went to court. There it came to light that about seven hundred claims had been made by consumers between 1982 and 1992 for similar incidents. This seems to indicate that McDonald’s knew—or at least should have known—that the hot coffee was a problem. Most of the rest of the case turned around temperature questions. McDonald’s admitted that they served their coffee at 185 degrees, which will burn the mouth and throat and is about 50 degrees higher than typical homemade coffee. More importantly, coffee served at temperatures up to 155 degrees won’t cause burns, but the danger rises abruptly with each degree above that limit. So why did McDonald’s serve it so hot? Most customers, the company claimed, bought on the way to work or home and would drink it on arrival. The high temperature would keep it fresh until then. Unfortunately, internal documents showed that McDonald’s knew their customers intended to drink the coffee in the car immediately after purchase. Next, McDonald’s asserted that their customers wanted their coffee hot. The restaurant conceded, however, that customers were unaware of the serious burn danger and that no adequate warning of the threat’s severity was provided. Finally, the jury awarded Liebeck $160,000 in compensatory damages and $2.7 million in punitive damages (about two days’ worth of McDonalds’ coffee sales). The judge, however, reduced the $2.7 million to $480,000. McDonald’s threatened to appeal, and the two sides eventually came to a private settlement agreement.

The concept of manufacturer liability gives consumers the right to sue manufacturers for defective goods. There are three kinds of product defect: Design defects (errors in the product’s design) Manufacturing defects (errors in the production of one specific case of a generally safe product) Instructional defects (poor or incomplete instructions for a product’s safe use) Which (if any) of these defects are applicable in the McDonald’s coffee case? Explain.

What is the concept of strict product liability, and how could it be applicable in this case?

In 2000 Firestone tires had a major problem on their hands, several of their tires were exploding while being driven, causing serious injuries and death to its customers. In fact on May 2, 2000 the National Highway Traffic Safety Administration opeed an investigation into Firestone tires based on reports of tread separation on some of its tires. At that point, the agency had received 90 complaints, including reports of 33 crashes resulting in 27 injuries and four deaths. Answer the following based on your independent research on the Firestone case:

a. What type of tort claim would an injured or deceased party pursue against Firestone?

b. What is the goal of tort law and how does it apply to the Firestone case?

c. Think back to the Ford Pinto case, how is the Firestone incident similar; and how did Firestone confront the issue once it was discovered?

d. If you worked for Firestone what defenses would you claim?

1. Kaiser's Kraft Korner sells a single product. 7,000 units were sold resulting in $70,000 of sales revenue, $28,000 of variable costs, and $12,000 of fixed costs.

solve:
A. Contribution margin per unit is:
B. Breakeven point in unit is:
C. The number of units that must be sold to achieve 60,000 of operating income
D. If sales increase by 25,000, operating income will increase by:

2. Schuppener Company sells its only product for $18 per unit, variable production costs are $6 per unit, and selling and administrative costs are $3 per unit. Fixed costs for 10,000 units are $10,000.

3. Fixed costs equal $12,000, unit contribution margin equals $20, and the number of units sold equal 1,600. Operating income is:

4. If selling price per unit is $30, variable costs per unit are $20, total fixed costs are $10,000, the tax rate is 30%, and the company sells 5,000 units, net income is:

5. At the breakeven point of 200 units, variable costs total $400 and fixed costs total $600. The 201st unit sold will contribute ______ to profits

WalMart’s fiscal year starts the first week of February. This means that when analyzing the data, week 26 is actually week 30 (26+4 weeks for January) in 2002 or the end of July 2002. Also, week 52 is actually week 4 (52+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the end of January 2003. As an example, the spike in sales (revenue) at week 75 occurs in week 27 (75+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the first week in July 2003. This corresponds to sales for the July 4th holiday when people are buying barbecue related items.

1. Identify spikes (outliers) in the data where extreme sales values occur and correlate these spikes with actual calendar dates in 2002 or 2003 and with holidays or special events that may occur during these periods.

Modeling the data linearly - a. Generate a linear model for this data by choosing two points.

b. Generate a least squares linear regression model for this data.

c. How good is this regression model? Output and discuss the R² value.

d. What are the marginal sales (derivative, i.e. rate of change) for this department using the linear model with two data points and the regression model?

e. Compare the two models. Which do you feel is better?

f. Remove appropriate outliers as you deem necessary and rerun the linear regression model. What is the marginal sales and discuss improvements.

2. Modeling the data quadratically - a. Generate a quadratic model for this data. Also output and discuss the R² value.

b. What are the marginal sales for this department using this model?

c. Calculate the model generated relative max/min value. Show backup analytical work.

d. Compare actual and model generated relative max/min value.

e. Remove outliers and rerun the quadratic least squares model. What is the marginal sales and discuss improvements.

3. Comparing models - a. Based on all models run, which model do you feel best predicts future trends? Explain your rationale.

b. Based on the model selected, what type of seasonal adjustments, if any, would be required to meet customer needs?

Note that Walmart's fiscal year starts the first week of February. This means that when analyzing the data, week 26 is actually week 30 (26+4 weeks for January) in 2002 or the end of July 2002. Also, week 52 is actually week 4 (52+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the end of January 2003. As an example, the spike in sales(revenue) at week 75 occurs in week 27 (75+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the first week in July 2003. This corresponds to sales for the July 4th holiday when people are buying barbecue related items.

1. identify spikes (outliers) in the data where extreme sales values occur and correlate these spikes with actual calendar dates 2002 or 2003 and with holidays or special events that may occur during these periods.

2. Modeling the data linearly -

a. Generate a linear model for this data by choosing two points.

b. Generate a least squares linear regression model for this data.

c. How good is this regression model? Output and discuss the R2 value.

d. What are the marginal sales (derivative, i.e. rate of change) for this department using the linear model with two data points and the regression model?

e. Compare the two models. Which do you feel is better?

f. Remove appropriate outliers as you deem necessary and rerun the linear regression model. What is the marginal sales and discuss improvements.

3. Modeling the data quadratically -

a. Generate a quadratic model for this data. Also output and discuss the R2 value.

b. What are the marginal sales for this department using this model?

c. Calculate the model generated relative max/min value. Show backup analytical work.

d. Compare actual and model generated relative max/min value.

e. Remove outliers and rerun the quadratic least squares model. What is the marginal sales and discuss improvements.

4. Comparing models

a. Based on all models run, which model do you feel best predicts future trends? Explain your rationale.

b. Based on the model selected, what type of seasonal adjustments, if any, would be required to meet customer needs?

The WalMart’s fiscal year starts the first week of February. This means that when analyzing the data, week 26 is actually week 30 (26+4 weeks for January) in 2002 or the end of July 2002. Also, week 52 is actually week 4 (52+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the end of January 2003. As an example, the spike in sales (revenue) at week 75 occurs in week 27 (75+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the first week in July 2003. This corresponds to sales for the July 4th holiday when people are buying barbecue related items. Please use excel.

Identify spikes (outliers) in the data where extreme sales values occur and correlate these spikes with actual calendar dates in 2002 or 2003 and with holidays or special events that may occur during these periods.

1. Modeling the data linearly - a. Generate a linear model for this data by choosing two points.

b. Generate a least squares linear regression model for this data.

c. How good is this regression model? Output and discuss the R² value.

d. What are the marginal sales (derivative, i.e. rate of change) for this department using the linear model with two data points and the regression model?

e. Compare the two models. Which do you feel is better?

f. Remove appropriate outliers as you deem necessary and rerun the linear regression model. What is the marginal sales and discuss improvements.

2. Modeling the data quadratically - a. Generate a quadratic model for this data. Also output and discuss the R² value.

b. What are the marginal sales for this department using this model?

c. Calculate the model generated relative max/min value. Show backup analytical work.

d. Compare actual and model generated relative max/min value.

e. Remove outliers and rerun the quadratic least squares model. What is the marginal sales and discuss improvements.

3. Comparing models - a. Based on all models run, which model do you feel best predicts future trends? Explain your rationale.

b. Based on the model selected, what type of seasonal adjustments, if any, would be required to meet customer needs?

The WalMart’s fiscal year starts the first week of February. This means that when analyzing the data, week 26 is actually week 30 (26+4 weeks for January) in 2002 or the end of July 2002. Also, week 52 is actually week 4 (52+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the end of January 2003. As an example, the spike in sales (revenue) at week 75 occurs in week 27 (75+4 weeks for January 2002 minus 52 weeks for 2002) in 2003 or the first week in July 2003. This corresponds to sales for the July 4th holiday when people are buying barbecue related items. Please use excel.

Identify spikes (outliers) in the data where extreme sales values occur and correlate these spikes with actual calendar dates in 2002 or 2003 and with holidays or special events that may occur during these periods.

1. Modeling the data linearly - a. Generate a linear model for this data by choosing two points.

b. Generate a least squares linear regression model for this data.

c. How good is this regression model? Output and discuss the R² value.

d. What are the marginal sales (derivative, i.e. rate of change) for this department using the linear model with two data points and the regression model?

e. Compare the two models. Which do you feel is better?

f. Remove appropriate outliers as you deem necessary and rerun the linear regression model. What is the marginal sales and discuss improvements.

2. Modeling the data quadratically - a. Generate a quadratic model for this data. Also output and discuss the R² value.

b. What are the marginal sales for this department using this model?

c. Calculate the model generated relative max/min value. Show backup analytical work.

d. Compare actual and model generated relative max/min value.

e. Remove outliers and rerun the quadratic least squares model. What is the marginal sales and discuss improvements.

3. Comparing models - a. Based on all models run, which model do you feel best predicts future trends? Explain your rationale.

b. Based on the model selected, what type of seasonal adjustments, if any, would be required to meet customer needs?