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?

US Auto Company would like to offer rebates to its customers in order to increase sales. If it lowers prices sales will increase.    This will depend on the price elasticity of demand. Assume that the price elasticity of demand is 1.5. This firm is considering a $400 rebate on its cars. Also assume the following information on prices and costs before the rebates:

          Average price per car                                   $9,000 per car

          Expected sales volume at $9,000) per car     1,000,000 cars

          Average total costs per car                           $8,200 per car

          Total variable cost                                         $6,400,000,000

• Calculate the present total fixed costs, average variable costs and average fixed costs.
• What is the present breakeven point?
• What is the change in revenue resulting from the $400 price reduction?
• What is the effect on the cost per car after the change? In other words what is the average cost per car after the change?
• Should the change be made?

Please show the calculation. Thank you.

What revenue model does Uber Use? What about a company that makes printers?

Koshy Company is planning a cash budget for the next three months. Estimated sales revenue is:

Month Sales Revenue Month Sales Revenue

All sales are on credit; 60 percent is collected during the month of sale, and 40 percent is collected during the next month. Cost of goods sold is 80 percent of sales. Payments for merchandise sold are made in the month following the month of sale. Operating expenses total $26,000 per month and are paid during the month incurred. The cash balance on February 1 is estimated to be $35,000.

Prepare monthly cash budgets for February, March, and April.

Metro Bus Company had $400,000 of revenue and $401,000 of expense (including depreciation) for the current year resulting in a $1,000 net loss. All revenues were received in cash. All expenses were paid in cash, except for depreciation of $181,000. At the end of the year, the Balance Sheet shows $225,000 of Cash and $1,775,000 of other assets. The Company has no debt and all of the busses are modern - there is no plan to purchase more busses. Although there is sufficient Retained Earnings and they historically have paid dividends of $25,000, Management has decided against paying a dividend to stockholders in the current year. Instead, they issue a statement to their stockholders, explaining that "with a $1,000 net loss, Management feels there is insufficient cash for the dividend."

What is the Company's cash flow? What is the difference between cash flow and net income? Evaluate the accuracy of Management's statement: "with a $1,000 net loss, Management feels there is insufficient cash for the dividend." Evaluate the plan to skip the dividend. How would your response change if the stockholders were (a) common stockholders, (b) non-cumulative preferred, or (c) cumulative preferred?

A company has the following data:

• Revenue (Price x Quantity) = $750,000

• Variable cost per unit = $65

• Units sold (Quantity) = 5,000

• Total costs = $625,000

• The company has no semi-variable costs

The company will open a new branch that will increase its fixed costs by $125,000.

Determine the following:

a) Price of each unit.

b) Total variable cost.

c) Total fixed costs before the new branch.

d) Total fixed costs after the new branch.

e) Total costs after the new branch. f) Breakeven quantity after the new branch.