Format Outline:

Introduction : This section generally should include a short description of overview of the given sales data information, the objective and the methods used to model raw data into a realistic mathematical model.

Data Analysis: This section includes addressing and answering the specifications and requirements as stated in the requirements file. Each model is to be supported by appropriate data plots, graphs, mathematical computations, and explanations where needed showing all work. Spikes in the data are outliers and major ones should normally be considered and removed from the data to clean and ensure a better model as part of your option to differentiate results. Generate supporting Excel spreadsheet(s) and graphs for analyses and outcomes. Provide proper labeling of the axes in the graphs when used in context to your analysis. "Analyze the given Walmart Boxed Foods 2002-2003 data set that is part of the Dry Goods department. Correlate the major spikes of the data with that of the major holiday periods cited in the 2002-2003 calendar year. Identify holiday periods or special events that cause the spikes in the data. Present your analysis result in a table format showing: Wks.-No, Spike-Sales-Value, and Calendar period 2. Generate linear and quadratic models using regression for the data set. Present your plots with appropriate titles, the axes labeled and showing the model equation and R2 value. Refer to the project requirements file for detailed instructions to be followed."

Conclusion : Besides discussing relevant items, there should be a discussion of the results of a good model based on the R2 value for least squares models, marginal sales (rates of change of the sales models) and their meanings for the various models generated, and other discussions.

This the chart that I used to create the graph below:

Boxed Foods 2002-2003 Data
Week	Sales in $
26	2400
27	2000
28	1800
29	1750
30	1700
31	2500
32	3100
33	2400
34	2350
35	3100
36	3150
37	2300
38	2600
39	2025
40	2225
41	2200
42	1975
43	2025
44	2025
45	2400
46	2200
47	2600
48	1975
49	2700
50	2800
51	3600
52	3200
53	3025
54	3000
55	3400
56	3400
57	4050
58	4500
59	3850
60	3500
61	3475
62	4000
63	3900
64	3250
65	3600
66	4500
67	3600
68	4100
69	4300
70	4600
71	3950
72	4300
73	4300
74	4225
75	3975
76	4600
77	4300

This chart is part of my project below:

Marginals for the exponential function and log function from Excel models y = de" then y' = abe The marginal sales in week x is y'. Ex: Assume y =14000e then y' =14000(.002)e"?* = 28e" and in week 50, the rate of change in sales is y' = 28023 =28el =30.94 . Y=alnx+b then y'= a X The marginal sales in week x is y'. 1600 1600 Ex: y=1600Inx+9000 then y' = and in week 50, the rate of change in sales is y' = = 32 . X 50 Note: Also compute the marginal for models in other weeks to see the rates of change then.(Dry Goods) Boxed Foods 2002-2003 Data Sales in $ $5,000.00 $4,500.00 y =51 163x + 495.86 54,000.00 R3 = 0.7491 $3,500.00 $3,000.00 Sales 52,500.00 52,000.00 $1,500.00 $1,000.00 $500.00 50.00 10 20 30 40 50 60 70 80 90 WeeksWal*Mart Dry Goods Sales 2002-2003 Walmart's fiscal year starts the first week of February. This means that when analyz 25TH 5TH 26 is week 30 (26+4 weeks for January) in 2002 or the end of July 2002. Also, week 52 is 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. Even though the acquired data is from 2002-2003, the data analyses are relevant for data acquired at other time periods. When doing the least squares modeling of the data, please include model (linear, logarithmic, exponential) and then remove outliers (extreme values causing spikes in the data) and rerun the model. The results should improve with better R2 values. Please let me know why outliers. were removed. 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. __-_-. __._..__. _.____ ...____ _. ____ __._. __._.. .._.._._J._ _. _'___._. _._..._ ...__ ..._: __-_.. during these periods. 2. Modeling the data - a. Generate linear, logarithmig and exponential models. Output at most two models on any graph. lo. 1Il'ilhen generating the least squares models for this data. output the model and the FEE. 1.u'alue and discuss these results. c. What are the marginal sales [derivatives i.e., rate of change} for 1.u'arious weeks throughout the data set for this department using each model? I need detail for what. the marginal sales for each model indicate. d. ilinal'hl'ticalllir prepare predictions of sales for each model for the weeks 1'9, 81 and 83. which is six weeks after the data set ends. Also compute rates of change [marginals] for each model at weeks F9, 81 and 83. e. How would it seem if appropriate outliers as you deem necessary were removed and the least squares models were rerun. What are the marginal sales for each new model and discuss R2 changes. f. For the new models with outliers removed, analytically what are the predictions of sales. and marginals at weeks i9. 81 and 83. rrnrrihilihau (_nna-l inn. an