Question

1. Given the data below, compute for the following: a) Forecasts for P5 to P12 using a 4-period simple moving average b) Forecasts for P5 to P12 using a 4-month weighted moving average with the following weights: Most recent period = 0.40 2nd most recent period = 0.30 3rd most recent period = 0.20 4th most recent period = 0.10 Assuming a forecast of 5,000 units for Period 4 and a = 0.30, compute for the forecasts for P5 to P12. d) Using linear trend forecasting, determine the: - equation of the linear trend line - compute for the forecast for P5 to P12. PERIOD 1 5 2 DEMAND ,000 4,500 4,800 5,100 5,500 5,200 4,200 4,000 5,800 5,400 ,000 6,000 7 10 115 12
please i need asap answer

Answer 1

	a	4 period simple moving average
		Period	Demand	Forecast
		1	5,000
		2	4,500
		3	4,800
		4	5,100
		5	5,500	4,850	(5000+4500+4800+5100)/4
		6	5,200	4,975	(4500+4800+5100+5500)/4
		7	4,200	5,150
		8	4,000	5,000
		9	5,800	4,725
		10	5,400	4,800
		11	5,000	4,850
		12	6,000	5,050
	b	4 month weighted moving average
		Period	Demand
		1	5,000
		2	4,500
		3	4,800
		4	5,100
		5	5,500	4,880	(0.1*5000+0.2*4500+0.3*4800+0.4*5100)/4
		6	5,200	5,140	(0.1*4500+0.2*4800+0.3*5100+0.4*5500)/5
		7	4,200	5,230
		8	4,000	4,850
		9	5,800	4,450
		10	5,400	4,880
		11	5,000	5,120
		12	6,000	5,180
	c	EXPONENTIAL SMOOTHING			ALPHA=	0.3
		Period	Demand	Forecast
		4	5,100	5,000
		5	5,500	5,030	(5100*0.3+5000*(1-0.3)
		6	5,200	5,171	(5500*0.3+5030*(1-0.3)
		7	4,200	5,180
		8	4,000	4,886
		9	5,800	4,620
		10	5,400	4,974
		11	5,000	5,102
		12	6,000	5,071
							LINEAR FORCASTING Yf=4639+62x Period Demand Forecast 1 5,000 2 4,500 3 4,800 5,100 5 5,500 4,949 6 5,200 5,011 7 4,200 5,073 4,000 5,135 9 5,800 5,197 10 5,400 5,259 11 5,000 5,321 12 6,000 5,383 Click "Data" Click "Data Analysis" Select "Regression" tool Click"OK" Input y_Range (Demand) Input x_Range (Period) Click "OK" Demand (Y)=Intercept(a)+X Variable(b)*Period(x) Intercept 4639 X variable 62 Y=4639+62X SUMMARY OUTPUT Regression Statistics Multiple R 0.36961 R Square 0.13661 Adjusted R Square 0.05028 Standard Error 588.341 Observations ANOVA F Significance F 1.58231 0.23700734 Regression Residual Total of SS 1 547710 10 3461457 11 4009167 MS 547710 346146 Intercept X Variable 1 Coefficientandard Err Stat P-value Lower 95% Upper 95% Lower 95.0%pper 95.0% 4639.39 362.099 12.8125 1.6E-07 3832.58677 5446.2 3832.5868 5446.2 61.8881 49.1996 1.2579 0.23701 -47.735396 171.512 -47.7354 171.512 Lupuvalu 9 Aliment Number 19 G10 X fax A B C D H I J = (F6+F7+F8+F9)/4 E F G Period Demand Forecast 1 5,000 2 4,500 3 4,800 4 5,100 5 5,500 1 4,850 (5000+4500+4800+5100)/4 Clipboard Font Alignment Numbers Styles Cells x fc A B C D K L H I J (5000+4500+4800+5100)/4 (4500+4800+5100+5500)/4 =0.1*F22+0.2*F23+0.3*F24+0.4*F25 E F G 5 5,500 4,850 6 5,200 4,975 7 4,200 5,150 8 4,000 5,000 9 5,800 4,725 10 5,400 4,800 11 5,000 4,850 12 6,000 5,050 4 month weighted moving average Period Demand 1 5,000 2 4,500 3 4,800 4 5,100 5 5,500 6 5,200 7 4,200 8 4,000 9 5,800 10 5,400 11 5,000 12 6,000 4,880 I (0.1*5000+0.2*4500+0.3*4800+0.4*5100)/4 5,140 (0.1*4500+0.2*4800+0.3*5100+0.4*5500)/5 5,230 4,850 4,450 4,880 5,120 5,180 Clipboard Font Alignment Number G7 X for =F6*0.3+G6*(1-0.3) E A B C D F G H EXPONENTIAL SMOOTHING ALPHA= Period Demand Forecast 4 5,100 5,000 5 5,500 5,030 6 5,200 5,171 4,200 5,180 4,000 4,886 5,800 4,620 10 5,400 4,974 11 5,000 5,102 12 6,000 5,071 95,