Question 3Metropolitan Hospital has estimated its average monthly bed needs asN = 1,000 + 9XX = time period (months); January 2002 = 0N = monthly bed needsAssume that no new hospital additions are expected in the area in theforeseeable future. The following monthly monthly seasonal adjustmentfactors have been estimated, using data from the past 5 years:

MONTH X = time period (months)Jan + 5% 5 years X 5 = 60Apr - 15% 60 + 3 = 63Jul + 4% 63 + 3 = 66

Nov - 5% 66 + 4 = 70Dec - 25% 70 + 1 = 71

a) Forecast Metropolitan's bed demand for Jan, Apr, Jul, Nov and Dec 2007.Jan 2002 - Jan 2007; x = (2007 - 2002) x 12 = 60Jan 2007 - Apr 2007; x = 60 + 3 = 63Apr 2007 - Jul 2007; x = 63 + 3 = 66Jul 2007 - Nov 2007; x = 66 + 4 = 70

Nov 2007 - Dec 2007; x = 70 + 1 = 71

Jan-07N = 1000 + 9(60)N = 1540

Forecast bed demand = N x 1.05Forecast bed demand = 1540 x 1.05Forecast bed demand = 1617

Apr-07N = 1000 + 9(63)N = 1567

Forecast bed demand = N x 0.85Forecast bed demand = 1567 x 0.85Forecast bed demand = 1331.95

Jul-07N = 1000 + 9(66)N = 1594

Forecast bed demand = N x 1.04Forecast bed demand = 1594 x 1.04Forecast bed demand = 1657.76

Nov-07

ADJUSTMENT FACTOR

N = 1000 + 9(70)N = 1630

Forecast bed demand = N x 0.95Forecast bed demand = 1630 x 0.95Forecast bed demand = 1548.5

Dec-07N = 1000 + 9(71)N = 1639

Forecast bed demand = N x 0.75Forecast bed demand = 1639 x 0.75Forecast bed demand = 1229.25

b) If the following actual and forecast values for June bed demands have been recorded,what seasonal adjustment factor would u recommend be used in making futureJune forecast?

Year Forecast Actual Actual / Forecast2007 1045 1096 1.04880382775122006 937 993 1.0597652081112005 829 897 1.08202653799762004 721 751 1.04160887656032003 613 628 1.02446982055462002 505 560 1.1089108910891

Sum 6.3655851620639Min 1.060930860344

Seasonal adjustment factor = 6%

Question 4Corporate profits (Pt - 1) for all firms in Indula were about $100 billion. GDP for the nationis composed of consumption C, investment I, and government spending G. It is anticipated that Indula's federal, state and local governments will spend in the range of $200 billionnext year. On the basis of an analysis of recent economic activity in Indula, consumption expenditures are assumes to be $100 billion plus 80% of national income. National income is equal to GDP minus taxes T. Taxes are estimated to be at a rate of about 30% ofGDP. Finally corporate investment have historically equalled $30 billion plus 90% of lastyear's corporate profits (Pt - 1)

a) Construct 5 equation econometric model of the state of Indula. There will be a consumption equation, an investment equation, a tax receipt equation, an equationrepresenting the GDP identity, and a national income equation.(1) GDP = C + I + G(2) C = 100 + 0.8Y(3) Y = GDP - T Y = Income(4) T = 0.3GDP(5) I = 30 + 0.9(Pt - 1)

b) Assuming all random disturbance average to zero, solve the system of equations to arrive at next year's forecast values for C, I, T GDP and Y.(PT - 1) = $100 billion G = $200 billion

I = 30 + 0.9(Pt - 1)I = 30 + 0.9(100)

I = $ 120 billion

C = 100 + 0.8Y100 + 0.8(GDP - 0.3GDP)100 + 0.56GDP

GDP = 100 + 0.56GDP + 120 + 2000.44 GDP = 420

GDP = 420 / 0.44

GDP = $ 955 billion

C = 100 + 0.56(955)

C = $ 635 billion

T = 0.3(GDP)T = 0.3(955)

T = $ 287 billion

Y = GDP - T

Y = 955 - 287

Y = $ 668 billion

Question 7Year Sales ($000) t1997 121 01998 130 11999 145 22000 160 32001 155 42002 179 52003 215 62004 208 72005 235 82006 262 92007 10

a) Compute the quation of a trend line for these sales data to forecast sales for the next year.What does this equation forecast for sales in the year 2007?SUMMARY OUTPUT

Regression StatisticsMultiple R 0.97672616R Square 0.95399399Adjusted R Square 0.94824324Standard Error 10.7191418Observations 10

ANOVAdf SS MS F Significance F

Regression 1 19060.8 19060.8 165.890339 1.2481561E-06Residual 8 919.2 114.9Total 9 19980

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 112.6 6.3002164465 17.8724 9.8417E-08 98.071674834 127.12833 98.07167483 127.1283252t 15.2 1.18013866674 12.87984 1.2482E-06 12.478595357 17.921405 12.47859536 17.92140464

Sales = 112.6 + 15.2 (t)2007, t = 10

Sales = 112.6 + 15.2(10)Sales = 264.6 ($000)

Question 9Saving-Mart developed the following quaterly sales forecasting model:

Yt =Yt = predicted sales ($million) in quarter t

8.25 = quaterly sales ($million) when t = 0t = time period (quarter) where the 4th quarter of 2002 = 0,

1st quarter of 2003 = 1, 2nd quarter of 2003 = 2.

1 for 1st quarter observations 1 for 2nd quarter observations0 otherwise 0 otherwise

1 for 3rd quarter observations0 otherwise

Forecast Saving-Mart's sales of patio and lawn furniture for each quarter of 2010.

t2002.4 0 0 0 02003.1 1 0 0 12003.2 0 1 0 22003.3 0 0 1 32003.4 0 0 0 4

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .2010.1 1 0 0 292010.2 0 1 0 302010.3 0 0 1 312010.4 0 0 0 32

1st Quarter 2010 (2010.1)

Yt =Yt = 8.25 + 0.125(29) - 2.75(1) + 0 + 0Yt = 9.125

2nd Quarter 2010 (2010.2)

Yt =Yt = 8.25 + 0.125(30) - 0 + 0.25(1) + 0Yt = 12.25

3rd Quarter 2010 (2010.1)

8.25 + 0.125t - 2.75D1t + 0.25D2t + 3.5D3t

D1t = D2t =

D3t =

D 1t D 2t D 3t

8.25 + 0.125t - 2.75D1t + 0.25D2t + 3.5D3t

8.25 + 0.125t - 2.75D1t + 0.25D2t + 3.5D3t

Yt =Yt = 8.25 + 0.125(31) - 0 + 0 + 3.5(1)Yt = 15.625

1st Quarter 2010 (2010.1)

Yt =Yt = 8.25 + 0.125(32) - 0 + 0 + 0Yt = 12.25

8.25 + 0.125t - 2.75D1t + 0.25D2t + 3.5D3t

8.25 + 0.125t - 2.75D1t + 0.25D2t + 3.5D3t