1. Canada Computer Company serves many businesses in the Ottawa region. The company sells supplies and provides customer service on all computers sold through their sales offices. Most of the items are stocked, and the management is concerned about stockout because of the growth in the business. Proper forecasting will help to estimate the requirements several months in advance so that adequate inventory can be stocked. The data on the demand (sales) for 16-GB DDR4 computer RAM over the past 50 months is provided in the attached Excel spreadsheet.
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3. a)Make a time series plot of sales data and comment on the patterns you observe. (2 marks)
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5. b)Forecast the demand for month 51 using the trend project method with linear regression. Provide the corresponding regression equation. Report the correlation coefficient for this model. Also calculate the Mean absolute deviation (MAD) error metric. Begin your error calculation from month 1. (11 marks)
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7. c)A management consultant team working with the company suggests that new office building leases can be a good predictor for company sales. They reference a study that new office building leases precede office equipment purchases and supply sales by three months. According to the study, leases in month 1 will affect equipment sales in month 4, and leases in month 2 will affect sales in month 5, and so on. Make a graph for sales data with leases as a predictor. Comment on the pattern you observe. Calculate the correction coefficient for their relationship. (4 marks)
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9. d)Adding leases as a leading predictor to your analysis, develop a forecasting model for sales, with leases as the independent variable. Provide the corresponding regression equation and forecast the sales for month 51. Also, calculate the Mean absolute deviation (MAD) error metric. Begin your error calculation from month 4. (9 marks)
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11. e)make a graph showing the forecasts from part (d) against actual values of sales. (2 marks)
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13. f)Which of the two models provides a better forecast? Explain. (2 marks)
     

Month	RAM Sales	Leases
1	80	32
2	132	29
3	143	32
4	180	54
5	200	53
6	168	89
7	212	74
8	254	93
9	397	120
10	385	113
11	472	147
12	397	126
13	476	138
14	699	145
15	545	160
16	837	196
17	743	180
18	722	197
19	735	203
20	838	223
21	1057	247
22	930	242
23	1085	234
24	1090	254
25	1218	271
26	1296	281
27	1199	298
28	1267	314
29	1300	323
30	1370	309
31	1489	343
32	1499	357
33	1669	353
34	1716	360
35	1603	370
36	1812	386
37	1817	389
38	1798	399
39	1873	409
40	1923	410
41	2028	413
42	2049	439
43	2084	454
44	2083	441
45	2121	470
46	2072	469
47	2262	490
48	2371	496
49	2309	509
50	2422	522