The following items are a guide for responses to be addressed in project one. 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. All projects must be printed on 8.5x11 paper in a word document with imbedded Excel graphs. No electronic copies or handwritten ones will be accepted. All content must be printed - no handwritten mathematics, graphs, labels, etc. All projects must be stapled. Students are to work separately. Prepare your document with graphs imbedded in the document as if you are a consultant to Walmart. The document show follow a readable format. When doing your least squares modeling of the data, don't forget to generate the model (linear or quadratic) and then remove outliers (extreme values causing spikes in the data) and rerun the model. The results should improve with better R? values. Discuss what outliers were removed and why. Generate supporting Excel graphs (use scatter plots) to answer the following questions for the Dry Goods 2002-2003 data: 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. 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 R 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. Methuen Wal Mart 2002-2003 Dry Goods Week Sales in $ 26 15200 27 15600 28 16400 29 15600 30 14200 31 14400 32 16400 33 15200 34 14400 35 13800 36 15000 37 14100 38 14400 39 14000 40 15600 41 15000 42 14400 43 17800 44 15000 45 15200 46 15800 47 18600 48 15400 49 15500 50 16800 51 18700 52 21400