Forecasting Data Order Description Scenario: You are consultant for the Excellent Consulting Group. Your client wants to be able to forecast sales on a monthly basis and believes that there is a valid relationship between sales and the number of hits on their website during the previous month. To test this theory, the client has collected data on sales of one of its products, a lottery app for smart phones and hits on its website. Case Assignment Using Excel and linear regression analyze the data and determine how to do forecasting using website hits. Then forecast the next three months using the monthly hits data. Compare the forecast to the actual sales and determine the forecasting error. Actual sales data (Jan 402, Feb 380, Mar 379 and Apr 405) Then write a report to your boss and the client that briefly describes the results that you obtained. Make a recommendation on how this might be used for forecasting purposes. Paper should include two files: (1) An Excel file; and (2) A Word document. Data: Attached the Word file Case 3 Data.docx with the data. Use this data in Excel for your analysis. Assignment Expectations Analysis • Accurate and complete Linear Regression analysis in Excel. Written Report : (Use Heading for paragraphs) • Length requirements = 4 pages minimum (not including Cover and Reference pages) • Provide a brief introduction/ background of the problem. • Complete and accurate Excel analysis. • Written analysis that supports Excel analysis, and provides thorough discussion of assumptions, rationale, and logic used. • Complete, meaningful, and accurate recommendation(s). * Note: See attachments with Case 3- Data for assignment and complete instructions. Paper should include two files: (1) An Excel file; and (2) A Word document. Case 3 Data Following are the data for website hits and app sales (number of the Lottery apps.) Month Hits Sales Jan 1200 420 Feb 820 545 Mar 1151 301 Apr 1050 510 May 1180 485 Jun 1047 525 Jul 1102 460 Aug 1054 500 Sep 1254 402 Oct 1071 584 Nov 1120 422 Dec 1287 514 Jan 1164 441 Feb 1159 —- Mar 1298 —- April —- —- IMPORTANT: Be sure to shift the monthly sales up by one month because the theory is that the hits predict the next month sales (e.g., the 1,200 hits in January are paired with February’s sales of 545). Therefore, your data will look like this: Month Hits Sales Jan 1200 545 Feb 820 301 Mar 1151 510 Apr 1050 485 May 1180 525 Jun 1047 460 Jul 1102 500 Aug 1054 402 Sep 1254 584 Oct 1071 422 Nov 1120 514 Dec 1287 441 Use the monthly hits for Jan through Mar to predict the sales for Feb through Apr. When you have done so, ask your Instructor to provide the data for the actual sales for Jan through Apr. Module 3 – Case Linear Regression Forecasting and Decision Trees Assignment Overview Scenario: You are consultant for the Excellent Consulting Group. Your client wants to be able to forecast sales on a monthly basis and believes that there is a valid relationship between sales and the number of hits on their website during the previous month. To test this theory, the client has collected data on sales of one of its products, a lottery app for smart phones and hits on its website. Case Assignment Using Excel and linear regression analyze the data and determine how to do forecasting using website hits. Then forecast the next three months using the monthly hits data. Compare the forecast to the actual sales and determine the forecasting error. Actual sales data (Jan 402, Feb 380, Mar 379 and Apr 405) Then write a report to your boss and the client that briefly describes the results that you obtained. Make a recommendation on how this might be used for forecasting purposes. Data: Download the Word file Case 3 Data.docx with the data. Use this data in Excel for your analysis. Assignment Expectations Analysis • Accurate and complete Linear Regression analysis in Excel. Written Report : (Use Heading for paragraphs) • Length requirements = 4 pages minimum (not including Cover and Reference pages) • Provide a brief introduction/ background of the problem. • Complete and accurate Excel analysis. • Written analysis that supports Excel analysis, and provides thorough discussion of assumptions, rationale, and logic used. • Complete, meaningful, and accurate recommendation(s).
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