Wal*Mart Dry Goods Sales 2003-2004 The following items are a guide for responses to be addressed in project two. Note that WalMart's fiscal year starts the first week of February. This means that when analyzing the data, week 41 is actually week 45 (41+4 weeks for January) in 2003 or the beginning of November 2003. Also, week 52 is actually week 4 (52+4 weeks for January 2003 minus 52 weeks for 2003) in 2004 or the end of January 2004. As an example, the spikes in sales (revenue) during weeks 70-74 start in week 22 (70+4 weeks for January 2003 minus 52 weeks for 2003) in 2004 or the first week in June 2004, and extend through week 26 in 2004 or the end of June 2004. This corresponds perhaps to sales for graduation celebrations during the beginning of June and preparation for the July 4th holiday when people are buying barbecue related items. When doing your least squares modeling of the data, don't forget to generate the required models and then remove outliers (extreme values causing spikes in the data) and rerun the model. The results should improve with better R2 values. Discuss what outliers were selected, their calendar dates, and why the values were removed. Note that some items were removed and some added to the Dry Goods department in the Walmart Methuen store during the transition from 2002-2003 to 2003-2004. This accounts for the apparent variation in the graphs when comparing the data for project one and project two. Generate supporting Excel graphs (use scatter plots) to answer the following questions for the Dry Goods 2003-2004 data: Methuen WalMart 2003-2004 Dry Goods Week 41 42 43 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 Sales in s 18000 16800 15200 15000 13600 16000 12600 14800 16800 14800 15200 16000 15600 15600 15000 15700 15800 13800 12800 14400 15800 16000 12400 16200 17000 18600 16000 18000 19600 18600 18450 18000 18200 18600 16000 15200 16800 15800 17600 15800 15600 14200 16600 16100 14100 14400 14500 16900 17000 16000 17800 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? 4. For the model selected as your preferred predictor, compute the percent rate of increase Yz - Y for the next four weeks and provide appropriate backup computation. Note that week yi 91 sales is the last sales data available, so use your model to predict sales for week 92 and then compute the percent rate of increase. Repeat this process for Y; Y2, etc. Y2 Wal*Mart Dry Goods Sales 2003-2004 The following items are a guide for responses to be addressed in project two. Note that WalMart's fiscal year starts the first week of February. This means that when analyzing the data, week 41 is actually week 45 (41+4 weeks for January) in 2003 or the beginning of November 2003. Also, week 52 is actually week 4 (52+4 weeks for January 2003 minus 52 weeks for 2003) in 2004 or the end of January 2004. As an example, the spikes in sales (revenue) during weeks 70-74 start in week 22 (70+4 weeks for January 2003 minus 52 weeks for 2003) in 2004 or the first week in June 2004, and extend through week 26 in 2004 or the end of June 2004. This corresponds perhaps to sales for graduation celebrations during the beginning of June and preparation for the July 4th holiday when people are buying barbecue related items. When doing your least squares modeling of the data, don't forget to generate the required models and then remove outliers (extreme values causing spikes in the data) and rerun the model. The results should improve with better R2 values. Discuss what outliers were selected, their calendar dates, and why the values were removed. Note that some items were removed and some added to the Dry Goods department in the Walmart Methuen store during the transition from 2002-2003 to 2003-2004. This accounts for the apparent variation in the graphs when comparing the data for project one and project two. Generate supporting Excel graphs (use scatter plots) to answer the following questions for the Dry Goods 2003-2004 data: Methuen WalMart 2003-2004 Dry Goods Week 41 42 43 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 Sales in s 18000 16800 15200 15000 13600 16000 12600 14800 16800 14800 15200 16000 15600 15600 15000 15700 15800 13800 12800 14400 15800 16000 12400 16200 17000 18600 16000 18000 19600 18600 18450 18000 18200 18600 16000 15200 16800 15800 17600 15800 15600 14200 16600 16100 14100 14400 14500 16900 17000 16000 17800 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? 4. For the model selected as your preferred predictor, compute the percent rate of increase Yz - Y for the next four weeks and provide appropriate backup computation. Note that week yi 91 sales is the last sales data available, so use your model to predict sales for week 92 and then compute the percent rate of increase. Repeat this process for Y; Y2, etc. Y2