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?

I want to know how to solve the following in excel. What is the x value and what is the Y value using the data table below?

Wal-Mart is the second largest retailer in the world. The data file (Wal-Mart Revenue 2004-2009.xlsx) is posted below the case study one file, and it holds monthly data on Wal-Mart’s revenue, along with several possibly related economic variables.

A. Develop a linear regression model to predict Wal-Mart revenue, using CPI as the only independent variable.

B. Develop a linear regression model to predict Wal-Mart revenue, using Personal Consumption as the only independent variable.

C. Develop a linear regression model to predict Wal-Mart revenue, using Retail Sales Index as the only independent variable.

D. Which of these three models is the best? Use R-square values, Significance F values, p-values and other appropriate criteria to explain your answer.

E. Generate a scatter plot, residual plot and normal probability plot for the best model in part (d) and comment on what you see.

BREXIT QUESTION

From a macroeconomic standpoint, what are the implications of the UK leaving the European Union? What does this mean for Britain's economy? What does it mean for the pound? Furthermore, what does Brexit mean for the nations still in the EU?

A currency speculator expects the spot rate of British Pounds (GBP) to change from $2.00 to $2.20 in 6-months. Assume the speculator has access to credit lines of USD 20,000,000 in the US and GBP 10,000,000 in UK. The annual borrowing and lending rates are 6 percent in US and 4 percent in UK. If his forecast turns out be to true, at the end of the 6-month period, the speculator’s expected profit will be:

A currency speculator expects the spot rate of Euros to change from $1.20 to $0.80 in one year. Assume the speculator has access to credit lines of USD 12,000,000 in the US and EUR 10,000,000 in Europe. The annual borrowing and lending rates are 6 percent in US and 8 percent in Europe. If his forecast turns out be to true, at the end of the one-year period, the speculator’s expected profit will be:

In respect of the use of electronic payment systems, consumers have a much higher security risk
than banks. Critically examine the above statement with reference to the issues raised by Schulze
(2004).

Office Problem (Use the attached spreadsheets as a guide)

Property:             Office One, Anytown, U.S.A.

Acquisition date:         December 31, 1999

Purchase Price:           2000 NOI @ 10% CAP RATE

Deal Terms:                65% financed with debt, 9% interest-only, 10-year term

35% equity ownership

Base Year 1999:         Rental Income                   $1,600,000

Escalation Income             $              0

Less:         Janitorial & Cleaning       $   330,000

Labor                    $   215,250

Utilities                         $     60,000

Management Fee         $     80,000

Real Estate Taxes         $     80,000

Assumptions:              Vacancy Rate :        9%

                                   Growth Rates:          Rental      Income            5% Annually

                                                             Janitorial & Cleaning       3% Annually

Utilities                     3% Annually

                                                             Management Fee              3% Annually

In 2001, Labor and Real Estate Taxes escalate by 13.07 and 10%, respectively, and remain at those levels for the remainder of the holding period. Tenant pays the increase over the stated Base Year.

Sell on December 31, 2004

Selling Expenses- 5% of sale price (2005 NOI @ 10% Cap Rate)

Depreciable Basis = 80% of cost (calculate depreciation using straight-line method)

Owner’s Ordinary Tax Rate: 39.6%

Use Post-1997 capital gains & recapture tax rates (20% & 25% respectively)

REQUIRED:

9A) Pro-forma Analysis for both Pre-Tax and After- Tax scenarios

9B) Calculations for:

Adjusted Basis

Capital Gains and Recapture Taxes

Net Sales Proceeds

Break Even Occupancy (2000 & 2004)

Cash-on-Cash Returns (annually)

Gross Rent Multiplier ((2000 & 2004)

Debt Service Coverage (2000 & 2004)

Before and After Tax IRR

Before and After Tax NPV @12%