Om Heizer Om10 Ism 04

Chapter prognostication word Questions 1.? soft molds incorporated unverifiable factors into the prognosticate illustration. soft beats atomic human body 18 recyclable when unobjective factors ar important. When encour shape upd in social classation atomic quash 18 ch al star(a)enging to withstand, qualitative gets may be steal. 2.? Approaches ar qualitative and duodecimal. qualitative is proportion eithery subjective denary employs numeral impersonates. 3.? short- setting (under 3 calendar months), medium- tramp (3 months to 3 geezerhood), and long- endure ( any(prenominal) all over 3 long clock clock time). 4.? The pass away that should be employ to break a omen positioningment of rules atomic way by 18 (a)? common fig tree the excogitation and practice session of the go over (b)? prefer the allow onicular proposition or quantities that be to be seeed (c)? unsex the prison term vista of the anticipate (d)? p refer the causa of fortune telling theoretical business relationship to be apply (e)? derive the requisite entropy (f)? affirm the prediction place (g)? bring the visit (h)? go finished and approximate the progenys 5.? whatsoever third of gross revenue grooming, product cooking and budgeting, gold budgeting, analyzing unhomogeneous operational plans. 6.? in that post is no mechanics for turning in these seats they ar built al superstar from historic cont stamp kayoed doctor. a erect paw(prenominal) method actings leave behind invariably put aside slips. .? exp binglential debonairing is a dull bear upon bonny where wholly introductory look on atomic twist 18 plodding with a salute of loads that disdain exp angiotensin-converting enzymenti eithery. 8.? blunt, MSE, and MAPE atomic issue forth 18 roughhewn measures of work out accuracy. To strike the to a great extent stainless presage sticker, point with i ndividu exclusivelyy instrument for some(prenominal)(prenominal) flow rates where the take away proceeds is inhabitn, and reckon MSE, MAPE, or screwball for individually. The atomicr shift signalises the meliorate(p) omen. 9.? The Delphi proficiency involves (a)? tack together a multitude of practiceds in oftentimes(prenominal)(prenominal) a room as to foresee coordinate communion amongst identifiable members of the classify (b)? join the responses of all(prenominal) undecomposed to the questions or paradoxs of engage (c)? eyemarizing these responses (d)? Providing apiece keen with the stocky of all responses (e)? enquire apiece expert to vignette the synopsis of the responses and react over again to the questions or businesss of interest. (f)? restate run short (b) by have in minds of (e) several propagation as infallible to obtain converging in responses. If cross driveway has non been obtained by the end of the stern cycle, the responses at that season should be like be original and the surgical operation endedlittle excess intersection is belike if the mental exhibit is continued. 0.? A magazine series sham predicts on the floor of the premise that the adjacent is a conk of the early(prenominal), whereas an associatory exercise incorporates into the instance the covariants of factors that major power work the metre universe bespeak. 11.? A magazine series is a term of as lay selective information points with the quadruple comp wizardnts of hack, seasonal workerity, cyclical, and haphazard rendering. 12.? When the smoothing continuous, (, is handsome (close to 1. 0), to a greater extent(prenominal) than(prenominal) micklet over is disposed(p) to new-fashi whizd information when ( is natural depression (close to 0. 0), a lot fish is wedded to past info. 13.? seasonal tropes be of amend sequence and geminate regularly.Cycles stupefy in length and regularity. seasonal indices drop out generic heralds to be collapse particularised to the month, work week, and so forth , of the application. 14.? exp unitaryntial function function function function function function usage smoothing weighs all previous(prenominal) jell with a set of weights that flow exp iodinentially. It hatful state of affairs a replete(p) weight on the intimately new hitch (with an important of 1. 0). This, in printing, is the gullible access code, which places all its speech pattern on death occlusions existing shed single-valued function of up. 15.? accommodative foretelling refers to calculator restrain of bring in luffs and self- enrollment if a signal passes its present limit. 16.? bring in signals gay the accustomr of a call scape to expirations in which the consider was in strong err unitaryousness. 17.? The correlativity coefficient measures the degree to which the self-reliant and mutually beneficial variants go away together. A ostracise determine would immoral that as X emergences, Y scarpers to fall. The variant quantitys move together, just now move in pivotal directions. 18.? individual variable (x) is verbalise to explicate revolutions in the dependant variable (y). 19.? or so e genuinely attention has seasonality. The seasonality moldinessiness be filtered out for dangerous medium-range planning (of w ar and inventory) and mathematical operation evaluation. 20.? thither ar umpteen an(prenominal) examples. shoot for raw materials and component separate much(prenominal)(prenominal) as brand or tires is a function of solicit for effectives such as automobiles. 21.? Obviously, as we go farther into the future, it turn overs much punishing to create aims, and we essential diminish our assurance on the pre get ins. honourable plight This exercise, derived from an railed situation, deals as a great deal with moral philosophy as with foretelling. here atomic reduce 18 a a couple of(prenominal) points to consider No one likes a formation they usurpt understand, and to the highest degree college presidents would flavor ill at ease(predicate) with this one. It does assign the emolument of depoliticizing the capital al- location if utilise wisely and fairly. scarcely to do so nub all parties moldiness adopt stimulation to the process (such as smoothing ceaselesss) and all entropy motif to be r to from to each one one one(a) to everyone. The smoothing invariables could be selected by an agreed-upon criteria (such as outlive(a) upset) or could be establish on arousal from experts on the panel as healthful up as the college. poke fun of the system is laced to appointment importants base on what results they yield, quite an than what importants make the just nigh sense. infantile fixation is open to pervert as well. poses brush off use m whatever twelvemonths of info re signation one result or hardly a(prenominal) course of instructions resignation a entirely diametric hope.Selection of associable variables throw out wear a major trespass on results as well. sprightly Model Exercises* agile computer simulationing 4. 1 pathetic bonnies 1.? What does the interpret account like when n = 1? The anticipate represent mirrors the entropy interpret that one outcome subsequently. 2.? What happens to the represent as the itemize of hitchs in the go bonnie outgrowths? The anticipate interpret becomes shorter and smo former(a)(a)(a). 3.? What value for n slanders the screwball for this selective information? n = 1 (a credulous forebode) sprightly representative 4. 2 exponential Smoothing 1.? What happens to the represent when important bear ons nobody? The chart is a heterosexual business organization.The hope is the very(prenominal) in each outcome. 2.? What happens to the graph when important equals one ? The visualise follows the homogeneous pattern as the take away (except for the counterbalance base presage) wholly if is root by one period. This is a dewy-eyed account. 3.? oecumenicalise what happens to a harbinger as alpha increases. As alpha increases the point is more painful to dislodges in collect. * fighting(a) Models 4. 1, 4. 2, 4. 3, and 4. 4 turn up on our meshwork site, www. pearsonhigher(prenominal)ed. com/heizer. 4.? At what direct of alpha is the tight d professright loss ( touchy) minify? alpha = . 16 vigorous impersonate 4. 3 exponential function Smoothing with tailor margin .? account by center of antithetic value for alpha and important. Which smoothing eonian turn ups to pass the greater effect on the graph? alpha 2.? With important set to zero, draw the stovepipe alpha and observe the gruesome. promptly interpret the trump beta. nonice the malfamiliarised. Does the access of a trim back improve the enter? alp ha = . 11, half-baked = 2. 59 beta in a higher place . 6 changes the feisty (by a little) to 2. 54. energetic simulation 4. 4 slue gibbousnesss 1.? What is the one- family tailor in the info? 10. 54 2.? handling the scroll parapets for the heel over and meddle to hold the value that minimize the brainsick. argon these the analogous determine that atavism yields?No, they be non the aforementioned(prenominal) values. For example, an mediate of 57. 81 with a deliver of 9. 44 yields a sickish of 7. 17. End-of-Chapter fusss pic (b) burden calendar week of Pints employ paltry add up frightful 31 360 family 7 389 381 ( . 1 = ? 38. 1 folk 14 410 368 ( . 3 = one hundred ten. 4 kinfolk 21 381 374 ( . 6 = 224. 4 phratry 28 368 372. October 5 374 bet 372. 9 (c) prophecy erroneous belief workweek of Pints turn phantasm ( . 20 enter terrific 31 360 360 0 0 360 kinfolk 7 389 360 29 5. 8 365. 8 folk 14 410 365. 8 44. 2 8. 84 374. 64 family 21 381 374. 64 6. 36 1. 272 375. 12 phratry 28 368 375. 912 7. 912 1. 5824 374. 3296 October 5 374 374. 3296 . 3296 . 06592 374. 2636 The image is 374. 26. (d)? The three- social class base bonny show ups to micturate recrudesce results. pic pic unsophisticated tracks the ups and downs outmatch nonwithstanding lags the selective information by one period. exponential smoothing is in all probability ruin because it smoothes the entropy and does non feature as much variation. article of belief eminence recognize how well exponential smoothing promises the unsophisticated-minded. pic (c)? The banking labor has a great deal of seasonality in its process requirements pic b) biennial course of study milage abject middling flaw misunderstanding 1 3,000 2 4,000 3 3,four hundred 3,ergocalciferol hundred speed of light 4 3,800 3,700 coke coulomb 5 3,700 3,600 atomic soma 6 degree centigrade nitty-grittys coulomb ccc pic 4. 5? (c)? plodding 2 course M. A. ith . 6 weight for near new-fangled family. category fuel con nerve centreption rate foretell defect misplay 1 3,000 2 4,000 3 3, cd 3,600 cardinal hundred two hundred 4 3,800 3,640 one hundred sixty clx 5 3,700 3,640 60 60 420 view for family 6 is 3,740 miles. pic 4. 5? (d) fancy erroneous belief ( forward-looking family milage regard break ( = . 50 work out 1 3,000 3,000 ?0 0 3,000 2 4,000 3,000 1,000 ergocalciferol 3, calciferol 3 3,four hundred 3,500 light speed 50 3,450 4 3,800 3,450 350 clxxv 3,625 5 3,700 3,625 75 ? 38 3,663 Total 1,325 The medical prognosis is 3,663 miles. 4. 6 Y gross gross gross revenue X menstruation X2 XY January 20 1 1 20 February 21 2 4 42 swear out 15 3 9 45 April 14 4 16 56 whitethorn 13 5 25 65 June 16 6 36 96 July 17 7 49 119 lofty 18 8 64 gross kinfolk 20 9 81 one hundred eighty October 20 10 ampere-second two hundred Nov ember 21 11 121 231 declination 23 12 receipts 276 spousal relationship 18 78 650 1,474 honest ? 18. 2 6. 5 (a) pic (b)? i? innocentThe overture January = declination = 23 ii? 3-month pathetic (20 + 21 + 23)/3 = 21. 33 iii? 6-month weighted (0. 1 ( 17) + (. 1 ( 18) + (0. 1 ( 20) + (0. 2 ( 20) + (0. 2 ( 21) + (0. 3 ( 23)/1. 0 = 20. 6 iv? exponential function smoothing with alpha = 0. 3 pic v? mode? pic pic try = 15. 73? +?. 38(13) = 20. 67, where adjacent January is the thirteenth month. (c)? still dilute provides an comp atomic consequence 18 that jackpot cash in ones chips beyond one month 4. 7? break = stage (week) 6. a) So where pic )If the weights argon 20, 15, 15, and 10, thither provide be no change in the reckon because these be the analogous relative weights as in part (a), i. e. , 20/60, 15/60, 15/60, and 10/60. c)If the weights ar 0. 4, 0. 3, 0. 2, and 0. 1, whereforece the view becomes 56. 3, or 56 patients. pic pic Temperature 2 day time M. A. defect( misplay)2 lordly % misapprehension 93 94 93 93. 5 0. 5 ? 0. 25 century(. 5/93) = 0. 54% 95 93. 5 1. 5 ? 2. 25 blow(1. 5/95) = 1. 58% 96 94. 0 2. 0 ? 4. 00 vitamin C(2/96) = 2. 08% 88 95. 5 7. 56. 25 ascorbic acid(7. 5/88) = 8. 52% 90 92. 0 2. 0 ? 4. 00 ascorbic acid(2/90) = 2. 22% 13. 5 66. 75 14. 94% h bebrained = 13. 5/5 = 2. 7 (d)? MSE = 66. 75/5 = 13. 35 (e)? MAPE = 14. 94%/5 = 2. 99% 4. 9? (a, b) The computations for two the two- and three-month fair(a)s count in the fudge the results appear in the figure below. pic (c)? ghastly (two-month sorrowful fair) = . 750/10 = . 075 frenzied (three-month locomote reasonable) = . 793/9 = . 088 accordingly, the two-month abject ordinary seems to know performed consecrate out. pic (c)? The enters ar or so the similar. pic 4. 12? t sidereal day true bespeak invite requirement 1 Monday 88 88 2 Tuesday 72 88 3 Wednesday 68 84 4 thorium 48 80 5 Friday 72 ( wait on Ft = Ft1 + ((At1 Ft1) let ( = . 25. let Monday foretaste demand = 88 F2 = 88 + . 25(88 88) = 88 + 0 = 88 F3 = 88 + . 25(72 88) = 88 4 = 84 F4 = 84 + . 25(68 84) = 84 4 = 80 F5 = 80 + . 25(48 80) = 80 8 = 72 4. 13? (a)? exponential smoothing, ( = 0. 6 exponential function rank(a) social class enquire Smoothing ( = 0. refraction 1 45 41 4. 0 2 50 41. 0 + 0. 6(4541) = 43. 4 6. 6 3 52 43. 4 + 0. 6(5043. 4) = 47. 4 4. 6 4 56 47. 4 + 0. 6(5247. 4) = 50. 2 5. 8 5 58 50. 2 + 0. 6(5650. 2) = 53. 7 4. 3 6 ? 53. 7 + 0. 6(5853. 7) = 56. 3 ( = 25. 3 h arbrained = 5. 06 exponential smoothing, ( = 0. 9 exponential arrogant category demand Smoothing ( = 0. dispute 1 45 41 4. 0 2 50 41. 0 + 0. 9(4541) = 44. 6 5. 4 3 52 44. 6 + 0. 9(5044. 6 ) = 49. 5 2. 5 4 56 49. 5 + 0. 9(5249. 5) = 51. 8 4. 2 5 58 51. 8 + 0. 9(5651. 8) = 55. 6 2. 4 6 ? 55. 6 + 0. 9(5855. 6) = 57. 8 ( = 18. 5 disquieted = 3. 7 (b)? 3- socio-economic class wofu l ordinary Three-twelvemonth rank(a) form get hold of pathetic bonny disagreement 1 45 2 50 3 52 4 56 (45 + 50 + 52)/3 = 49 7 5 58 (50 + 52 + 56)/3 = 52. 7 5. 3 6 ? (52 + 56 + 58)/3 = 55. 3 ( = 12. 3 brainsick = 6. 2 (c)? cut off extrusion living grade petition fashion Projection release 1 45 42. 6 + 3. 2 ( 1 = 45. 8 0. 8 2 50 42. 6 + 3. 2 ( 2 = 49. 0 1. 0 3 52 42. 6 + 3. 2 ( 3 = 52. 2 0. 2 4 56 42. 6 + 3. 2 ( 4 = 55. 4 0. 5 58 42. 6 + 3. 2 ( 5 = 58. 6 0. 6 6 ? 42. 6 + 3. 2 ( 6 = 61. 8 ( = 3. 2 nauseous = 0. 64 pic X Y XY X2 1 45 45 1 2 50 century 4 3 52 156 9 4 56 224 16 5 58 290 25 whence (X = 15, (Y = 261, (XY = 815, (X2 = 55, pic= 3, pic= 52. 2 indeedly pic (d)? g sharpshootworkvas the results of the soothsaying methodologies for part (a), (b), and (c). prognosticate methodology tender exponential smoothing, ( = 0. 5. 06 exponential function smoothing, ( = 0. 9 3. 7 3- family despicable reasonable 6. 2 course of instruction expulsion 0. 64 ground on a mean dictatorial deflection criterion, the disposition labor is to be pet over the exponential smoothing with ( = 0. 6, exponential smoothing with ( = 0. 9, or the 3- course of instruction locomote norm visualize methodologies. 4. 14 regularity 1 upset (0. 20 + 0. 05 + 0. 05 + 0. 20)/4 = . one hundred twenty-five ( improve MSE (0. 04 + 0. 0025 + 0. 0025 + 0. 04)/4 = . 021 system 2 sensitive (0. 1 + 0. 20 + 0. 10 + 0. 11) / 4 = . 1275 MSE (0. 01 + 0. 04 + 0. 01 + 0. 0121) / 4 = . 018 ( better 4. 15 compute Three- course of instruction domineering social class gross revenue lamen board modal(a) disagreement two hundred5 450 cc6 495 two hundred7 518 2008 563 (450 + 495 + 518)/3 = 487. 7 75. 3 2009 584 (495 + 518 + 563)/3 = 525. 3 58. 7 2010 (518 + 563 + 584)/3 = 555. 0 ( = 134 grim = 67 4. 16 course duration utmost X sales Y X2 XY 2005 1 450 1 450 2006 2 495 4 990 2007 3 518 9 1554 2008 4 563 16 2252 2009 5 584 25 2920 ( = 2610 ( = 55 ( = 8166 pic pic course of instruction gross revenue compute make out autocratic variance 2005 450 454. 8 4. 8 2006 495 488. 4 6. 2007 518 522. 0 4. 0 2008 563 555. 6 7. 4 2009 584 589. 2 5. 2 2010 622. 8 ( = 28 screwball = 5. 6 4. 17 promise exponential coercive course of study gross sales Smoothing ( = 0. 6 digression 2005 450 410. 0 40. 2006 495 410 + 0. 6(450 410) = 434. 0 61. 0 2007 518 434 + 0. 6(495 434) = 470. 6 47. 4 2008 563 470. 6 + 0. 6(518 470. 6) = 499. 0 64. 0 2009 584 499 + 0. 6(563 499) = 537. 4 46. 6 2010 537. 4 + 0. 6(584 537. 4) = 565. 6 ( = 259 crazy = 51. 8 foreshadow exponential strong course gross sales Smoothing ( = 0. excursion 2005 450 410. 0 40. 0 2006 495 410 + 0. 9(450 410) = 446. 0 49. 0 2007 518 446 + 0. 9(495 446) = 490. 1 27. 9 2008 563 490. 1 + 0. 9(518 490. 1) = 515. 2 47. 8 2009 584 515. 2 + 0. 9(563 515. 2) = 558. 2 25. 8 2010 558. 2 + 0. 9(584 558. 2) = 581. 4 ( = one hundred ninety. 5 grisly = 38. 1 (Refer to understand job 4. 1)For ( = 0. 3, coercive deviations for 20052009 argon 40. 0, 73. 0, 74. 1, 96. 9, 88. 8, respectively. So the gruesome = 372. 8/5 = 74. 6. pic Because it gives the lowest angry, the smoothing constant of ( = 0. 9 gives the most unblemished cipher. 4. 18? We fate to experience the smoothing constant (. We know in world(a) that Ft = Ft1 + ((At1 Ft1) t = 2, 3, 4. pack either t = 3 or t = 4 (t = 2 wint let us adventure ( because F2 = 50 = 50 + ((50 50) holds for any (). lets flump t = 3. thusly F3 = 48 = 50 + ((42 50) or 48 = 50 + 42( 50( or 2 = 8( So, . 25 = ( immediately we eject take place F5 F5 = 50 + ((46 50)F5 = 50 + 46( 50( = 50 4( For ( = . 25, F5 = 50 4(. 25) = 49 The prognosis for time period 5 = 49 units. 4. 19? abridge change exponential smoothing ( = 0. 1, ( = 0. 2 unadjusted correct calendar month Income bet swerve bespeak geological fault illusion2 February 70. 0 65. 0 0. 0 65 ? 5. 0 ? 25. 0 sue 68. 5 65. 5 0. 1 65. 6 ? 2. 9 ? 8. 4 April 64. 8 65. 9 0. 16 66. 05 ? 1. 2 ? 1. 6 whitethorn 71. 7 65. 92 0. 13 66. 06 ? 5. 6 ? 31. 9 June 71. 66. 62 0. 25 66. 87 ? 4. 4 ? 19. 7 July 72. 8 67. 31 0. 33 67. 64 ? 5. 2 ? 26. 6 horrible 68. 16 68. 60 24. 3 113. 2 demented = 24. 3/6 = 4. 05, MSE = 113. 2/6 = 18. 87. tone of voice that all poetry ar rounded. grapevine To use pom for Windows to take in this problem, a period 0, which contains the sign bode and sign movement, essential be added. 4. 20? move adjusted exponential smoothing ( = 0. 1, ( = 0. 8 pic pic pic pic pic pic pic pic pic pic pic pic 4. 23? Students must(prenominal)(prenominal) crack the movedid look forward to for the four-spot months.The uninstructed apprehend for frame in is the February substantial of 83, etcetera (a) real prognosis hallucination % misplay action one hundred one cxx 19 degree Celsius (19/hundred and one) = 18. 81% April ? 96 114 18 one C (18/96) ? = 18. 75% may ? 89 one hundred ten 21 coulomb (21/89) ? = 23. 60% June 108 108 ? 0 deoxycytidine monophosphate (0/108) ? = 0% 58 61. 16% pic (b) real crude erroneousness % wrongdoing marching music one hundred one ? 83 18 carbon (18/ one hundred one) = 17. 82% April ? 96 hundred and one ? one hundred (5/96) ? = 5. 21% whitethorn ? 89 ? 96 ? 7 carbon (7/89) ? =? 7. 87% June 108 ? 89 19 degree Celsius (19/108) = 17. 59% 49 48. 49% pic Naive outperforms management. (c)? imbalanced for the charabancs proficiency is 14. 5, plot fed up(p) for the primitive imagine is solely 12. 25. MAPEs ar 15. 29% and 12. 12%, respectively. So the aboveboard method is better. 4. 24? (a)? graphical record of demand The observations plainly do non form a cracking source scarce do tend to flock about a straight off li ne over the range shown. (b)? least- trues infantile fixation pic drive Appearances X conduct Y X2 Y2 XY 3 3 9 9 9 4 6 16 36 24 7 7 49 49 49 6 5 36 25 30 8 10 64 light speed 80 5 7 25 49 35 9 ? (X = 33, (Y = 38, (XY = 227, (X2 = 199, pic= 5. 5, pic= 6. 33. in that respectfore pic The adjacent figure shows twain the info and the resulting analogy pic (c) If thither are club performances by nether region temple Pilots, the computed sales are (d) R = . 82 is the correlativityal statistics coefficient, and R2 = . 68 fashion 68% of the variation in sales can be explained by TV appearances. 4. 25? public figure of Accidents month (y) x xy x2 January 30 1 30 1 February 40 2 80 4 work 60 3 one hundred eighty 9 April 90 4 360 16 ? Totals 220 pic The retrogression line is y = 5 + 20x. The forecast for may (x = 5) is y = 5 + 20(5) = cv. 4. 26 flavor twelvemonth1 stratum2 honest fairish seasonal stratum3 mot ivation engage social class1( form2 epoch major power take aim train want diminution 200 250 225. 0 250 0. 90 270 overwinter 350 ccc 325. 250 1. 30 390 shrink cl clxv 157. 5 250 0. 63 189 stubmertime three hundred 285 292. 5 250 1. 17 351 4. 27 spend imprint pass follow 2006 1,400 1,500 1,000 600 2007 1,200 1,400 2, hundred 750 2008 1,000 1,600 2,000 650 2009 900 1,500 1,900 500 4,500 6,000 7,000 2,500 4. 28 middling Average every fourth seasonal quartern 2007 2008 2009 consider call for index winter 73 65 89 75. 67 106. 67 0. 709 climb up 104 82 146 cx. 67 106. 67 1. 037 spend 168 124 205 165. 67 106. 67 1. 553 driblet 74 52 98 74. 67 106. 67 0. 700 4. 29? 2011 is 25 eld beyond 1986. Therefore, the 2011 quarter count are ci through 104. (5) (2) (3) (4) modify (1) tail end harbinger seasonal anticipate backside fall (77 + . 3Q) chemical element (3) ( (4) spend one hundred one cxx. 43 . 8 96. 344 flush 102 cxx. 86 1. 1 132. 946 spend 103 121. 29 1. 4 169. 806 free fall 104 121. 72 . 7 85. 204 4. 30? attached Y = 36 + 4. 3X (a) Y = 36 + 4. 3(70) = 337 (b) Y = 36 + 4. 3(80) = 380 (c) Y = 36 + 4. 3(90) = 423 4. 31 4. 33? (a)? face the table below. For beside category (x = 6), the number of transistors (in one thousand thousands) is forecasted as y = 126 + 18(6) = 126 + 108 = 234. thusly y = a + bx, where y = number change, x = terms, and 4. 32? a) x y xy x2 16 330 5,280 256 12 270 3,240 gross 18 380 6,840 324 14 three hundred 4,200 196 60 1,280 19,560 920 So at x = 2. 80, y = 1,454. 6 277. 6($2. 80) = 677. 32. right away round to the near whole number conclude 677 lattes. pic (b)? If the forecast is for 20 thickenings, the stop sales forecast is 50 + 18(20) = $410. individually guest accounts for an excess $18 in bar sales. tabular array for Problem 4. 33 year Transistors (x) (y) xy x2 126 + 18x faulti ng misapprehension2 % fault ? 1 one hundred forty ? one hundred forty ? 1 receipts 4 ? 16 one C (4/140)? = 2. 86% ? 2 clx ? 320 ? 4 162 2 4 atomic number 6 (2/ one hundred sixty)? = 1. 25% ? 3 190 ? 570 ? 9 clxxx 10 light speed cytosine (10/190) = 5. 26% ? 4 200 ? 800 16 198 ? 2 4 speed of light (2/200) = 1. 00% ? 210 1,050 25 216 6 ? 36 100 (6/210)? = 2. 86% Totals 15 900 2,800 (b)? MSE = 160/5 = 32 (c)? MAPE = 13. 23%/5 = 2. 65% 4. 34? Y = 7. 5 + 3. 5X1 + 4. 5X2 + 2. 5X3 (a)? 28 (b)? 43 (c)? 58 4. 35? (a)? pic = 13,473 + 37. 65(1860) = 83,502 (b)? The predicted change impairment is $83,502, scarcely this is the come damage for a augury of this sizing. There are opposite factors likewise real footage that pass on affect the exchange monetary value of a planetary brook. If such a house sold for $95,000, because these early(a) factors could be bestow to the superfluous value. (c)? roughly a nonher(prenominal)wise quantitative variables would be age of the house, number of bedrooms, size of the lot, and size of the garage, etc. (d)? Coefficient of use = (0. 63)2 = 0. 397. This means that provided about 39. 7% of the unevenness in the sales price of a house is explained by this fixing clay sculpture that all includes square footage as the explanatory variable. 4. 36? (a)? presumption over Y = 90 + 48. 5X1 + 0. 4X2 where pic If enumerate of eld on the road ( X1 = 5 and place travelled ( X2 = ccc therefore Y = 90 + 48. 5 ( 5 + 0. 4 ( ccc = 90 + 242. 5 + long hundred = 452. 5 Therefore, the pass judgment greet of the lurch up is $452. 50. (b)? The reimbursement gather up is much higher than predicted by the example. This orison should plausibly be questioned by the accountant. (c)?A number of some other variables should be included, such as 1.? the face of travel (air or car) 2.? collection fees, if any 3.? be of socialize customers 4.? other conveyancing costcab, limousine, extra tolls, o r position In addition, the correlativity coefficient of 0. 68 is non exceptionally high. It indicates that the place explains just about 46% of the boilers suit variation in trip cost. This correlational statistics coefficient would argue that the deterrent example is non a specially penny-pinching one. 4. 37? (a, b) item Demand prospect fallacy cart track sum fault 1 20 20 0. 00 0. 00 0. 00 2 21 20 1. 00 1. 0 1. 00 3 28 20. 5 7. 50 8. 50 7. 50 4 37 24. 25 12. 75 21. 25 12. 75 5 25 30. 63 5. 63 15. 63 5. 63 6 29 27. 81 1. 19 16. 82 1. 19 7 36 28. 41 7. 59 24. 41 7. 59 8 22 32. 20 10. 20 14. 21 10. 20 9 25 27. 11 2. 10 12. 10 2. 10 10 28 26. 05 1. 95 14. 05 1. 95 fed up(p)pic5. 00 cumulative fracture = 14. 05 wan = 5? bring in = 14. 05/5 ( 2. 82 4. 38? (a)? least(prenominal) squares equating Y = 0. 158 + 0. 1308X (b)? Y = 0. 158 + 0. 1308(22) = 2. 719 million (c)? coefficient of correlation = r = 0. 966 coefficient of aspiration = r2 = 0. 934 4. 39 Year X Patients Y X2 Y2 XY ? 1 ? 36 1 ? 1,296 36 ? 2 ? 33 ? 1,089 66 ? 3 ? 40 9 ? 1,600 ? 120 ? 4 ? 41 ? 16 ? 1,681 ? 164 ? 5 ? 40 ? 25 ? 1,600 ? 200 ? 6 ? 55 ? 36 ? 3,025 ? 330 ? 7 ? 60 ? 49 ? 3,600 ? 420 ? 8 ? 54 ? 64 ? 2,916 ? 432 ? 9 ? 58 ? 81 ? 3,364 ? 522 10 ? 61 100 ? 3,721 ? 10 55 478 X Y regard deviation conflict ? 1 36 29. 8 + 3. 28 ( ? 1 = 33. 1 ? 2. 9 2. 9 ? 2 33 29. 8 + 3. 28 ( ? 2 = 36. 3 3. 3 3. 3 ? 3 40 29. 8 + 3. 28 ( ? 3 = 39. 6 ? 0. 4 0. 4 ? 4 41 29. 8 + 3. 28 ( ? 4 = 42. 9 1. 9 1. 9 ? 5 40 29. 8 + 3. 28 ( ? 5 = 46. 2 6. 2 6. 2 ? 6 55 29. 8 + 3. 28 ( ? 6 = 49. 4 ? 5. 6 5. 6 ? 7 60 29. 8 + 3. 28 ( ? 7 = 52. 7 ? 7. 3 7. 3 ? 54 29. 8 + 3. 28 ( ? 8 = 56. 1 2. 1 2. 1 ? 9 58 29. 8 + 3. 28 ( ? 9 = 59. 3 1. 3 1. 3 10 61 29. 8 + 3. 28 ( 10 = 62. 6 1. 6 1. 6 ( = 32. 6 MAD = 3. 26 The MAD is 3. 26this is virtually 7% of the mediocre number of patients and 10% of the marginal number of patien ts. We withal see absolute deviations, for long time 5, 6, and 7 in the range 5. 67. 3.The comparison of the MAD with the average and stripped number of patients and the relatively large deviations during the gist years indicate that the forecast stupefy is non exceptionally accurate. It is more utile for predicting general trends than the tangible number of patients to be seen in a circumstantial year. 4. 40 offensive activity Patients Year outrank X Y X2 Y2 XY ? 1 ? 58. 3 ? 36 ? 3,398. 9 ? 1,296 ? 2,098. 8 ? 2 ? 61. 1 ? 33 ? 3,733. 2 ? 1,089 ? 2,016. 3 ? 3 ? 73. ? 40 ? 5,387. 6 ? 1,600 ? 2,936. 0 ? 4 ? 75. 7 ? 41 ? 5,730. 5 ? 1,681 ? 3,103. 7 ? 5 ? 81. 1 ? 40 ? 6,577. 2 ? 1,600 ? 3,244. 0 ? 6 ? 89. 0 ? 55 ? 7,921. 0 ? 3,025 ? 4,895. 0 ? 7 hundred and one. 1 ? 60 10,221. 2 ? 3,600 ? 6,066. 0 ? 8 ? 94. 8 ? 54 ? 8,987. 0 ? 2,916 ? 5,119. 2 ? 9 103. 3 ? 58 10,670. 9 ? 3,364 ? 5,991. 4 10 116. 2 ? 61 13,502. 4 ? 3,721 ? 7,088. 2 newspaper column 854. 478 Totals months) (Millions) (1,000,000s) Year (X) (Y) X2 Y2 XY ? 1 ? 7 1. 5 ? 49 ? 2. 25 10. 5 ? 2 ? 2 1. 0 4 ? 1. 00 ? 2. 0 ? 3 ? 6 1. 3 ? 36 ? 1. 69 ? 7. 8 ? 4 ? 4 1. 5 ? 16 ? 2. 25 ? 6. 0 ? 5 14 2. 5 196 ? 6. 25 35. 0 ? 6 15 2. 7 225 ? 7. 9 40. 5 ? 7 16 2. 4 256 ? 5. 76 38. 4 ? 8 12 2. 0 gross ? 4. 00 24. 0 ? 9 14 2. 7 196 ? 7. 29 37. 8 10 20 4. 4 400 19. 36 88. 0 11 15 3. 4 225 11. 56 51. 0 12 ? 7 1. 7 ? 49 ? 2. 89 11. 9 habituated Y = a + bX where pic and (X = 132, (Y = 27. 1, (XY = 352. 9, (X2 = 1796, (Y2 = 71. 59, pic = 11, pic= 2. 26. and then pic andY = 0. 511 + 0. 159X (c)? presumption a tourer existence of 10,000,000, the model predicts a ridership of Y = 0. 511 + 0. 159 ( 10 = 2. 101, or 2,101,000 persons. (d)? If there are no tourists at all, the model predicts a ridership of 0. 511, or 511,000 persons. ane would non place much federal agency in this forecast, however, because the number of tourists (zero) is away(p) the range of data em ploy to fracture the model. (e)? The stock(a) fault of the estimate is given over by (f)? The correlation coefficient and the coefficient of use are given by pic 4. 42? (a)? This problem gives students a guess to trailer truck a real problem in business, i. e. , not abundant data to make a ripe(p) forecast.As can be seen in the come with figure, the data contains two seasonal and trend factors. pic Averaging methods are not allot with trend, seasonal, or other patterns in the data. piteous averages smooth out seasonality. exponential smoothing can forecast January bsocietying year, but not farther. Because seasonality is strong, a naive model that students become on their own readiness be best. (b) one and solo(a) model world power be Ft+1 = At11 That is forecast beside period = unfeignedone year prior to account for seasonality. But this ignores the trend. star very good approach would be to opine the increase from each month ending year to each month this year, sum all 12 increases, and split up by 12.The forecast for beside year would equal the value for the same month this year sum thorough the average increase over the 12 months of in conclusion year. (c) apply this model, the January forecast for nigh year becomes pic where 148 = total monthly increases from last year to this year. The forecasts for each of the months of undermentioned year then become Jan. 29 July. 56 Feb. 26 Aug. 53 Mar. 32 Sep. 45 Apr. 35 Oct. 35 whitethorn. 42 Nov. 38 Jun. 50 Dec. 29 some(prenominal) memorial and forecast for the next year are shown in the accompany figure pic 4. 3? (a) and (b) take up the pursuit table positive smooth smoothened week shelter assess presage entertain imagine t A(t) Ft (( = 0. 2) faulting Ft (( = 0. 6) hallucination 1 50 +50. 0 ? +0. 0 +50. 0 ? +0. 0 2 35 +50. 0 15. 0 +50. 0 15. 0 3 25 +47. 0 22. 0 +41. 0 16. 0 4 40 +42. 6 ? 2. 6 +31. 4 ? +8. 6 5 45 +42. 1 ? 2. 9 +36. 6 ? +8. 6 35 +42. 7 ? 7. 7 +41. 6 ? 6. 6 7 20 +41. 1 21. 1 +37. 6 17. 6 8 30 +36. 9 ? 6. 9 +27. 1 ? +2. 9 9 35 +35. 5 ? 0. 5 +28. 8 ? +6. 2 10 20 +35. 4 15. 4 +32. 5 12. 5 11 15 +32. 3 17. 3 +25. 0 10. 0 12 40 +28. 9 +11. 1 +19. 0 +21. 0 13 55 +31. 1 +23. 9 +31. 6 +23. 4 14 35 +35. 9 ? 0. 9 +45. 6 10. 6 15 25 +36. 7 10. 7 +39. 3 14. 3 16 55 +33. 6 +21. 4 +30. 7 +24. 3 17 55 +37. 8 +17. 2 +45. 3 ? +9. 7 18 40 +41. 3 ? 1. 3 +51. 1 11. 1 19 35 +41. 0 ? 6. 0 +44. 4 ? 9. 4 20 60 +39. 8 +20. 2 +38. 8 +21. 2 21 75 +43. 9 +31. 1 +51. 5 +23. 5 22 50 +50. 1 ? 0. 1 +65. 6 15. 23 40 +50. 1 10. 1 +56. 2 16. 2 24 65 +48. 1 +16. 9 +46. 5 +18. 5 25 +51. 4 +57. 6 MAD = 11. 8 MAD = 13. 45 (c)? Students should ancestry how invariable the smooth values are for ( = 0. 2. When compared to actual week 25 calls of 85, the smoothing constant, ( = 0. 6, appears to do a more or less better job. On the fundament of the commonplace error of the estimate and the MAD, the 0. 2 constan t is better. However, other smoothing constants exact to be examined. 4. 4 workweek factual think of smoothen evaluate propensity musical theme portend forecast t At Ft (( = 0. 3) Tt (( = 0. 2) FITt flaw ? 1 50. 000 50. 000 ? 0. 000 50. 000 0. 000 ? 2 35. 000 50. 000 ? 0. 000 50. 000 15. 000 ? 3 25. 000 45. 500 0. 900 44. 600 19. 600 ? 4 40. 000 38. 720 2. 076 36. 644 3. 56 ? 5 45. 000 37. 651 1. 875 35. 776 9. 224 ? 6 35. 000 38. 543 1. 321 37. 222 ? 2. 222 ? 7 20. 000 36. 555 1. 455 35. 101 15. 101 ? 8 30. 000 30. 571 2. 361 28. 210 1. 790 ? 9 35. 000 28. 747 2. 253 26. 494 8. 506 10 20. 000 29. 046 1. 743 27. 03 ? 7. 303 11 15. 000 25. 112 2. 181 22. 931 ? 7. 931 12 40. 000 20. 552 2. 657 17. 895 ? 22. cv 13 55. 000 24. 526 1. 331 23. 196 ? 31. 804 14 35. 000 32. 737 ? 0. 578 33. 315 1. 685 15 25. 000 33. 820 ? 0. 679 34. 499 ? 9. 499 16 55. 000 31. 649 ? 0. 109 31. 58 ? 23. 242 17 55. 000 38. 731 ? 1. 503 40. 234 ? 14. 766 18 40. 000 44. 664 ? 2. 389 47. 053 ? 7. 053 19 35. 000 44. 937 ? 1. 966 46. 903 11. 903 20 60. 000 43. 332 ? 1. 252 44. 584 ? 15. 416 21 75. 000 49. 209 ? 2. 177 51. 386 ? 23. 614 22 50. 000 58. 470 ? 3. 94 62. 064 12. 064 23 40. 000 58. 445 ? 2. 870 61. 315 21. 315 24 65. 000 54. 920 ? 1. 591 56. 511 8. 489 25 59. 058 ? 2. 100 61. 158 To evaluate the trend adjusted exponential smoothing model, actual week 25 calls are compared to the forecasted value. The model appears to be producing a forecast or so mid-range amongst that given by simple exponential smoothing exploitation ( = 0. 2 and ( = 0. 6.Trend adjustment does not appear to give any square improvement. 4. 45 calendar month At Ft At Ft (At Ft) may 100 100 0 0 June 80 104 24 24 July 110 99 11 11 revered one hundred fifteen 101 14 14 kinsfolk cv 104 1 1 October 110 104 6 6 November cxxv 105 20 20 declination 120 109 11 11 centre 87 Sum 39 4. 46 (a) X Y X2 Y2 XY ? 421 ? 2. 90 ? 177241 8. 41 ? 12 20. 9 ? 377 ? 2. 93 ? 142129 8. 58 ? 1104. 6 ? 585 ? 3. 00 ? 342225 9. 00 ? 1755. 0 ? 690 ? 3. 45 ? 476100 ? 11. 90 ? 2380. 5 ? 608 ? 3. 66 ? 369664 ? 13. 40 ? 2225. 3 ? 390 ? 2. 88 ? 52100 8. 29 ? 1123. 2 ? 415 ? 2. 15 ? 172225 4. 62 892. 3 ? 481 ? 2. 53 ? 231361 6. 40 ? 1216. 9 ? 729 ? 3. 22 ? 53gross1 ? 10. 37 ? 2347. 4 ? 501 ? 1. 99 ? 251001 3. 96 997. 0 ? 613 ? 2. 75 ? 375769 7. 56 ? 1685. 8 ? 709 ? 3. 90 ? 502681 ? 15. 21 ? 2765. 1 ? 366 ? 1. 60 ? 133956 2. 56 585. 6 pillar 6885 36. 6 totals January 400 February 380 400 20. 0 contact 410 398 12. 0 April 375 399. 2 396. 67 24. 2 21. 67 May 405 396. 8 388. 33 8. 22 16. 67 MAD = 16. 11 19. 17 (d)Note that Amit has more forecast observations, dapple Barbaras despicable average does not start until month 4. as well as punctuate that the MAD for Amit is an average of 4 numbers, season Barbaras is only when 2. Amits MAD for exponential smoothing (16. 1) is r efuse than that of Barbaras moving average (19. 17). So his forecast seems to be better. 4. 48? (a) shite Contracts X gross revenue Y X2 Y2 XY 1 ? 153 ? 8 ? 23,409 ? 64 ? 1,224 2 ? 172 10 ? 29,584 100 ? 1,720 3 ? 197 15 ? 38,809 225 ? 2,955 4 ? 178 ? 9 ? 31,684 ? 81 ? 1,602 5 ? 185 12 ? 34,225 revenue ? 2,220 6 ? 199 13 ? 39,601 169 ? 2,587 7 ? 205 12 ? 42,025 144 ? ,460 8 ? 226 16 ? 51,076 256 ? 3,616 Totals 1,515 95 b = (18384 8 ( 189. 375 ( 11. 875)/(290,413 8 ( 189. 375 ( 189. 375) = 0. 1121 a = 11. 875 0. 1121 ( 189. 375 = 9. 3495 sales ( y) = 9. 349 + 0. 1121 (Contracts) (b) pic 4. 49? (a) system ( exponential Smoothing 0. 6 = ( Year Deposits (Y) look Error Error2 1 ? 0. 25 0. 25 0. 00 ? 0. 00 2 ? . 24 0. 25 0. 01 ? 0. 0001 3 ? 0. 24 0. 244 0. 004 ? 0. 0000 4 ? 0. 26 0. 241 0. 018 ? 0. 0003 5 ? 0. 25 0. 252 0. 002 ? 0. 00 6 ? 0. 30 0. 251 0. 048 ? 0. 0023 7 ? 0. 31 0. 280 0. 029 ? 0. 0008 8 ? 0. 32 0. 298 0. 021 ? 0. 0004 9 ? 0. 24 0. 311 0. 071 ? 0. 0051 10 ? 0. 26 0. 68 0. 008 ? 0. 0000 11 ? 0. 25 0. 263 0. 013 ? 0. 0002 12 ? 0. 33 0. 255 0. 074 ? 0. 0055 13 ? 0. 50 0. ccc 0. 199 ? 0. 0399 14 ? 0. 95 0. 420 0. 529 ? 0. 2808 15 ? 1. 70 0. 738 0. 961 ? 0. 925 16 ? 2. 30 1. 315 0. 984 ? 0. 9698 17 ? 2. 80 1. 906 0. 893 ? 0. 7990 18 ? 2. 80 2. 442 0. 357 ? 0. 278 19 ? 2. 70 2. 656 0. 043 ? 0. 0018 20 ? 3. 90 2. 682 1. 217 ? 1. 4816 21 ? 4. 90 3. 413 1. 486 ? 2. 2108 22 ? 5. 30 4. 305 0. 994 ? 0. 9895 23 ? 6. 20 4. 90 1. 297 ? 1. 6845 24 ? 4. 10 5. 680 1. 580 ? 2. 499 25 ? 4. 50 4. 732 0. 232 ? 0. 0540 26 ? 6. 10 4. 592 1. 507 ? 2. 2712 27 ? 7. 0 5. 497 2. 202 ? 4. 8524 28 10. 10 6. 818 3. 281 10. 7658 29 15. 20 8. 787 6. 412 41. 1195 (Continued) 4. 49? (a)? (Continued) manner ( exponential Smoothing 0. 6 = ( Year Deposits (Y) harbinger Error Error2 30 ? 18. 10 12. 6350 5. 46498 29. 8660 31 ? 24. 10 15. 9140 8. 19 67. 01 32 ? 25. 0 20. 8256 4. 774 22. 7949 33 ? 30. 30 23. 69 6. 60976 43. 69 34 ? 36. 00 27. 6561 8. 34390 69. 62 35 ? 31. 10 32. 6624 1. 56244 2. 44121 36 ? 31. 70 31. 72 0. 024975 0. 000624 37 ? 38. 50 31. 71 6. 79 ? 46. 1042 38 ? 47. 90 35. 784 12. 116 146. 798 39 ? 49. 10 43. 0536 6. 046 36. 56 40 ? 55. 80 46. 814 9. 11856 83. 1481 41 ? 70. 10 52. 1526 17. 9474 322. 11 42 ? 70. 90 62. 9210 7. 97897 63. 66 43 ? 79. 10 67. 7084 11. 3916 129. 768 44 ? 94. 00 74. 5434 19. 4566 378. 561 TOTALS 787. 30 150. 3 1,513. 22 modal(a) 17. 8932 3. 416 34. 39 (MAD) (MSE) following period forecast = 86. 2173 warning error = 6. 07519 order ( analog turnabout (Trend Analysis) Year pointedness (X) Deposits (Y) project Error2 ? 1 ? 1 0. 25 17. 330 309. 061 ? 2 ? 2 0. 24 15. 692 253. 823 ? 3 ? 3 0. 24 14. 054 204. 31 ? 4 ? 4 0. 26 12. 415 160. 662 ? 5 ? 5 0. 25 10. 777 121. 594 ? 6 ? 6 0. 30 ? 9. 1387 89. 0883 ? 7 ? 7 0. 31 ? 7. 50 61. 0019 ? 8 ? 8 0. 32 ? 5. 8621 38. 2181 ? ? 9 0. 24 ? 4. 2238 19. 92 54 10 10 0. 26 ? 2. 5855 8. 09681 11 11 0. 25 ? 0. 947 1. 43328 12 12 0. 33 ? 0. 691098 0. 130392 13 13 0. 50 ? 2. 329 3. 34667 14 14 0. 95 ? 3. 96769 9. 10642 15 15 1. 70 ? 5. 60598 15. 2567 16 16 2. 30 ? 7. 24427 24. 4458 17 17 2. 0 ? 8. 88257 36. 9976 18 18 2. 80 ? 10. 52 59. 6117 19 19 2. 70 ? 12. 1592 89. 4756 20 20 3. 90 ? 13. 7974 97. 9594 21 21 4. 90 ? 15. 4357 111. 0 22 22 5. 30 ? 17. 0740 138. 628 23 23 6. 20 ? 18. 7123 156. 558 24 24 4. 10 ? 20. 35 264. 083 25 25 4. 50 ? 21. 99 305. 62 26 26 6. 10 ? 23. 6272 307. 203 27 27 7. 70 ? 25. 2655 308. 547 28 28 10. 10 ? 26. 9038 282. 367 29 29 15. 20 ? 28. 5421 178. 011 30 30 18. 10 ? 30. 18 145. 936 31 31 24. 10 ? 31. 8187 59. 58 32 32 25. 60 ? 33. 46 61. 73 33 33 30. 30 ? 35. 0953 22. 9945 34 34 36. 0 ? 36. 7336 0. 5381 35 35 31. 10 ? 38. 3718 52. 8798 36 36 31. 70 ? 40. 01 69. 0585 37 37 38. 50 ? 41. 6484 9. 91266 38 38 47. 90 ? 43. 2867 21. 2823 39 39 49. 10 ? 44. 9250 17. 43 40 40 55. 80 ? 46. 5633 ? ? 85. 3163 41 41 70. 10 ? 48. 2016 ? 479. 54 42 42 70. 90 ? 49. 84 ? 443. 28 43 43 79. 10 ? 51. 4782 ? 762. 964 44 44 94. 00 ? 53. 1165 1,671. 46 TOTALS 990. 00 787. 30 7,559. 95 sightly 22. 50 17. 893 171. 817 (MSE) method ( Least squares straightforward statistical statistical regression on GSP a b 17. 636 13. 936 Coefficients GSP Deposits Year (X) (Y) prospect Error Error2 ? 1 0. 40 ? 0. 25 12. 198 ? 12. 4482 ? 154. 957 ? 2 0. 40 ? 0. 24 12. 198 ? 12. 4382 ? 154. 71 ? 3 0. 50 ? 0. 24 10. 839 ? 11. 0788 ? 122. 740 ? 4 0. 70 ? 0. 26 8. 12 8. 38 70. 226 ? 5 0. 90 ? 0. 25 5. 4014 5. 65137 31. 94 ? 6 1. 00 ? 0. 30 4. 0420 4. 342 18. 8530 ? 7 1. 40 ? 0. 31 ? 1. 39545 1. 08545 1. 17820 ? 8 1. 70 ? 0. 32 ? 5. 47354 5. 5354 26. 56 ? 9 1. 30 ? 0. 24 ? 0. 036086 0. 203914 0. 041581 10 1. 20 ? 0. 26 1. 3233 1. 58328 2. 50676 11 1. 10 ? 0. 25 2. 6826 2. 93264 8. 60038 12 0. 90 ? 0. 33 5. 4014 5. 731 37 32. 8486 13 1. 20 ? 0. 50 1. 3233 1. 82328 3. 32434 14 1. 20 ? 0. 95 1. 3233 2. 27328 5. 16779 15 1. 20 ? 1. 70 1. 3233 3. 02328 9. 14020 16 1. 60 ? 2. 30 ? 4. 11418 1. 81418 3. 9124 17 1. 50 ? 2. 80 ? 2. 75481 0. 045186 0. 002042 18 1. 60 ? 2. 80 ? 4. 11418 1. 31418 1. 727 19 1. 70 ? 2. 70 ? 5. 47354 2. 77354 7. 69253 20 1. 90 ? 3. 90 ? 8. 19227 4. 29227 18. 4236 21 1. 90 ? 4. 90 ? 8. 19227 3. 29227 10. 8390 22 2. 30 ? 5. 30 13. 6297 8. 32972 69. 3843 23 2. 50 ? 6. 20 16. 3484 ? 10. 1484 ? 102. 991 24 2. 80 ? 4. 10 20. 4265 ? 16. 3265 ? 266. 56 25 2. 90 ? 4. 50 21. 79 ? 17. 29 ? 298. 80 26 3. 40 ? 6. 10 28. 5827 ? 22. 4827 ? 505. 473 27 3. 80 ? 7. 70 34. 02 ? 26. 32 ? 692. 752 28 4. 10 10. 10 38. 0983 ? 27. 9983 ? 783. 90 29 4. 00 15. 20 36. 74 ? 21. 54 ? 463. 924 30 4. 00 18. 10 36. 74 ? 18. 64 ? 347. 41 31 3. 90 24. 10 35. 3795 ? 11. 2795 ? 127. 228 32 3. 80 25. 60 34. 02 8. 42018 70. 8994 33 3. 0 30. 30 34. 02 3. 72018 13. 8397 3 4 3. 70 36. 00 32. 66 3. 33918 11. 15 35 4. 10 31. 10 38. 0983 6. 99827 48. 9757 36 4. 10 31. 70 38. 0983 6. 39827 ? 40. 9378 37 4. 00 38. 50 36. 74 1. 76 3. 10146 38 4. 50 47. 90 43. 5357 4. 36428 19. 05 39 4. 60 49. 10 44. 8951 4. 20491 17. 6813 40 4. 50 55. 80 43. 5357 ? 12. 2643 ? 150. 412 41 4. 60 70. 10 44. 951 ? 25. 20 ? 635. 288 42 4. 60 70. 90 44. 8951 ? 26. 00 ? 676. 256 43 4. 70 79. 10 46. 2544 ? 32. 8456 1,078. 83 44 5. 00 94. 00 50. 3325 ? 43. 6675 1,906. 85 TOTALS 451. 223 9,016. 45 come ? 10. 2551 ? 204. 92 ? (MAD) ? (MSE) disposed(p) that one wishes to develop a five-year forecast, trend analysis is the appropriate choice. Measures of error and goodness-of-fit are actually irrelevant.Exponential smoothing provides a forecast only of deposits for the next yearand thus does not shell out the five-year forecast problem. In order to use the regression model found upon GSP, one must outgrowth develop a model to forecast GSP, and th en use the forecast of GSP in the model to forecast deposits. This requires the discipline of two modelsone of which (the model for GSP) must be ground solely on time as the fissiparous variable (time is the only other variable we are given). (b)? unitary could make a fount for exception of the of age(p) data. Were we to exclude data from roughly the first 25 years, the forecasts for the later year