Forecasting Sheet (4 Problems)

Prb 5

		Forecast a =
Week	Demand (Lab Requirements)	0.1	Error	Absolute Error
1	330
2	350
3	320
4	370
5	368
6	343
			MAD =
		Forecast a =
Week	Demand (Lab Requirements)	0.7	Error	Absolute Error
1	330
2	350
3	320
4	370
5	368
6	343
			MAD =
Given the historical date, which do you think would be the better to use?

Demand 1 2 3 4 5 6 330 350 320 370 368 343 Forecast a = 0.1 1 2 3 4 5 6 Forecast a = 0.7 1 2 3 4 5 6 Week Demand (Lab Requirements)

Prb 7

Week	Demand	Moving Avg.	Error	Absolute Error		Error ^2
1	20
2	31
3	36
4	38
5	42
6	40
			MAD =		MSE =
		Forecast a =
Week	Demand	0.2	Error	Absolute Error		Error ^2
1	20
2	31
3	36
4	38
5	42
6	40
			MAD =		MSE =
Week	Demand	Linear Reg.	Error	Absolute Error		Error ^2		Using MAD and MSE, which forcasting model is best?
1	20
2	31							Are your results the same using the two error measurements?
3	36
4	38
5	42
6	40
			MAD =		MSE =

Demand 1 2 3 4 5 6 20 31 36 38 42 40 Moving Avg. 1 2 3 4 5 6 Forecast a = 0.2 1 2 3 4 5 6 Linear Reg. 1 2 3 4 5 6 Week Demand

Prb 9

	Vistors	Vistors
Season	Year 1	Year 2
Fall	200	230
Winter	1400	1600
Spring	520	580
Summer	720	831
Total Demand
Average Vistors per Season
Calculate Seasonal Indices
Season	Year 1	Year 2	Average Seasonal Index
Fall
Winter
Spring
Summer
Calculate Forecast for Next Year
Estimated total vistors	4000
Average per season
Expected Seasonal Vistors, Based on Historical Seasonal Indices							Data for chart
Season	Forecast						Fall	200
Fall							Winter	1400
Winter							Spring	520
Spring							Summer	720
Summer							Fall	230
							Winter	1600
							Spring	580
							Summer	831
							Fall	0
							Winter	0
							Spring	0
							Summer	0

Fall Winter Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer 200 1400 520 720 230 1600 580 831 0 0 0 0 Season Vistors

Prb 25

Retail Sales ($)	Advertising ($)
29,789	16,893
35,434	18,398
38,732	20,376
43,585	22,982
46,821	25,732
49,283	27,281
52,271	32,182
55,289	35,298
57,298	36,281
58,293	38,178
Correlation coefficient:
Slope Coefficient (b)
Intercept Coefficient (a)
Amount spent on Ads	$ 40,000.00
Forecasted sales

Sales Forecast

y = 1.2181x + 13352 R² = 0.9549

16893 18398 20376 22982 25732 27281 32182 35298 36281 38178 29789 35434 38732 43585 46821 49283 52271 55289 57298 58293

Advertising ($)

Retail Sales ($)