Question

the first two are the instructions to the assignment and the last two are the data

MATH.1220 Management Calculus Project #1 Wal Mart Dry Goods Sales 2002-2003 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 s 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 4 holiday when people are buying barbecue related items. All projects must be printed in portrait form on 8.5x11 paper in a word document with imbedded Excel graphs. Students may work either separately or in teams of two. Team projects will result in more intense grading. 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? Compare the two models. Which do you feel is better? Remove appropriate outliers as you deem necessary and rerun the linear regression model. What is the marginal sales and discuss improvements e. f. 3. Modeling the data quadratically- a. Generate a quadratic model for this data. Also output and discuss the R'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 Based on all models run, which model do you feel best predicts future trends? Explain your rationale. Based on the model selected, what type of seasonal adjustments,if any, would be required to meet customer needs? a. b. 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 5 6 7 8 49 50 5 5 5 54 55 56 57 58 59 6 6 6 6 6 65 66 67 68 69 2 2 0123456

Answer 1

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