rsh_qam11_tif_05.doc
Short Description
Download rsh_qam11_tif_05.doc...
Description
Quantitative Analysis Analysis for Management, 11e (Render) Chapter 5 Forecasting 1) A medium -term forecast typically covers a two - to four -year time horizon. Answer: FALSE Di: ! "opic: #$"%&D'("#&$ !) %eression is always a superior forecastin method to e*ponential smoothin+ so reression should ,e used whenever the appropriate software is availa,le. Answer: FALSE Di: 1 "opic: #$"%&D'("#&$ -) "he three cateories of forecastin models are time series+ uantitative+ and ualitative. Answer: FALSE Di: ! "opic: "/0ES &F F&%E(AS"S F&%E(AS"S ) "ime-series models a2empt to predict the future ,y usin historical data. Answer: "%'E Di: ! "opic: "/0ES &F F&%E(AS"S F&%E(AS"S 3) "ime-series models rely on 4udment in an a2empt to incorporate ualitative ualitative or su,4ective factors into the forecastin model. Answer: FALSE Di: 1 "opic: "/0ES &F F&%E(AS"S F&%E(AS"S 5) A movin averae forecastin method is a causal forecastin method. Answer: FALSE Di: ! "opic: "/0ES &F F&%E(AS"S F&%E(AS"S 6) An e*ponential forecastin method is a time -series forecastin method. Answer: "%'E Di: ! "opic: "/0ES &F F&%E(AS"S F&%E(AS"S 7) A trend-pro4ection forecastin method is a causal forecastin method. Answer: FALSE Di: ! "opic: "/0ES &F F&%E(AS"S F&%E(AS"S
1 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
;) S A$D "#>E SE%#ES 1-) "he Delphi method solicits input from customers or potential customers reardin their future purchasin plans. Answer: FALSE Di: ! "opic: "/0ES &F F&%E(AS"S F&%E(AS"S 1) "he na?ve forecast for the ne*t period is the actual value o,served in the current period. Answer: "%'E Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ 13) >ean a,solute deviation @>AD) is simply the sum of forecast errors. Answer: FALSE Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ 15) "ime -series models ena,le the forecaster to include specic representations of various ualitative ualitative and uantitative factors. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 16) Four components of time series are trend+ t rend+ movin averae+ e*ponential smoothin+ and seasonality. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS
! (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
17) "he fewer the periods over which one taBes a movin averae+ the more accurately the resultin forecast mirrors the actual data of the most recent time periods. Answer: "%'E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 1;) #n a weihted movin averae+ the weihts assined must sum to 1. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !9) A sca2er diaram for a time series may ,e plo2ed on a two -dimensional raph with the horizontal a*is representin the varia,le to ,e forecast @such as sales). Answer: FALSE Di: ! "opic: S(A""E% D#A=%A>S A$D "#>E SE%#ES !1) Sca2er diarams can ,e useful in spo2in trends or cycles in data over time. Answer: "%'E Di: 1 "opic: S(A""E% D#A=%A>S A$D "#>E SE%#ES !!) E*ponential smoothin cannot ,e used for data with a trend. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !-) #n a second order e*ponential smoothin+ a low C ives less weiht to more recent t rends. Answer: "%'E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !) An advantae of e*ponential smoothin over a simple movin averae is that e*ponential smoothin reuires one to retain less data. Answer: "%'E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: %eective "hinBin !3) hen the smoothin constant G = 9+ the e*ponential smoothin model is euivalent to the na?ve forecastin model. Answer: FALSE Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills !5) A seasonal inde* must ,e ,etween -1 and +1. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
!6) A seasonal inde* of 1 means that the season is averae. Answer: "%'E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !7) "he process of isolatin linear trend and seasonal factors to develop a more accurate forecast is called reression. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !;) hen the smoothin constant G = 1+ the e*ponential smoothin model is euivalent to the na?ve forecastin model. Answer: "%'E Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills -9) Adaptive smoothin is analoous to e*ponential smoothin where the coeHcients G and C are periodically updated to improve the forecast. Answer: "%'E Di: ! "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S -1) ias is the averae error of a forecast model. Answer: "%'E Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ -!) hich of the followin is not classied as a ualitative forecastin modelI A) e*ponential smoothin ) Delphi method () 4ury of e*ecutive opinion D) sales force composite E) consumer marBet survey Answer: A Di: 1 "opic: "/0ES &F F&%E(AS"S --) A 4udmental forecastin techniue that uses decision maBers+ sta personnel+ and respondent to determine a forecast is called A) e*ponential smoothin. ) the Delphi method. () 4ury of e*ecutive opinion. D) sales force composite. E) consumer marBet survey. Answer: Di: ! "opic: "/0ES &F F&%E(AS"S (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
-) hich of the followin is considered a causal method of forecastinI A) e*ponential smoothin ) movin averae () oltJs method D) Delphi method E) $one of the a,ove Answer: E Di: ! "opic: "/0ES &F F&%E(AS"S -3) A raphical plot with sales on the Y a*is and time on the X a*is is a A) ca2er diaram. ) trend pro4ection. () radar chart. D) line raph. E) ,ar chart. Answer: A Di: ! "opic: S(A""E% D#A=%A>S A$D "#>E SE%#ES -5) hich of the followin statements a,out sca2er diarams is trueI A) "ime is always plo2ed on the y -a*is. ) #t can depict the relationship amon three varia,les simultaneously. () #t is helpful when forecastin with ualitative data. D) "he varia,le to ,e forecasted is placed on the y -a*is. E) #t is not a ood tool for understandin time -series data. Answer: D Di: ! "opic: S(A""E% D#A=%A>S A$D "#>E SE%#ES -6) hich of the followin is a techniue used to determine forecastin accuracyI A) e*ponential smoothin ) movin averae () reression D) Delphi method E) mean a,solute percent error Answer: E Di: 1 "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ -7) A medium -term forecast is considered to cover what lenth of timeI A) !- weeBs ) 1 month to 1 year () !- years D) 3-19 years E) !9 years Answer: Di: ! "opic: #$"%&D'("#&$ 3 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
-;) hen is the e*ponential smoothin model euivalent to the na?ve forecastin modelI A) G = 9 ) G = 9.3 () G = 1 D) durin the rst period in which it is used E) never Answer: ( Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 9) Enrollment in a particular class for the last four semesters has ,een 1!9+ 1!5+ 119+ and 1-9. Suppose a one semester movin averae was used to forecast enrollment @this is sometimes referred to as a na?ve forecast). "hus+ the forecast for the second semester would ,e 1!9+ for the third semester it would ,e 1!5+ and for the last semester it would ,e 119. hat would the >SE ,e for this situationI A) 1;5.99 ) !-9.56 () 199.99 D) !.99 E) $one of the a,ove Answer: Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ AA(S: Analytic SBills 1) hich of the followin methods tells whether the forecast tends to ,e too hih or too lowI A) >AD ) >SE () >A0E D) decomposition E) ,ias Answer: E Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ !) Assume that you have tried three dierent forecastin models. For the rst+ the >AD = !.3+ for the second+ the >SE = 19.3+ and for the third+ the >A0E = !.6. e can then say: A) the third method is the ,est. ) the second method is the ,est. () methods one and three are prefera,le to method two. D) method two is least preferred. E) $one of the a,ove Answer: E Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/
5 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
-) hich of the followin methods ives an indication of the percentae of forecast errorI A) >AD ) >SE () >A0E D) decomposition E) ,ias Answer: ( Di: 1 "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ ) Daily demand for newspapers for the last 19 days has ,een as follows: 1!+ 1-+ 15+ 13+ 1!+ 17+ 1+ 1!+ 1-+ 13 @listed from oldest to most recent). Forecast sales for the ne*t day usin a two -day movin averae. A) 1 ) 1() 13 D) !7 E) 1!.3 Answer: A Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 3) As one increases the num,er of periods used in the calculation of a movin averae+ A) reater emphasis is placed on more recent data. ) less emphasis is placed on more recent data. () the emphasis placed on more recent data remains the same. D) it reuires a computer to automate the calculations. E) one is usually looBin for a lon -term prediction. Answer: Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: %eective "hinBin 5) Enrollment in a particular class for the last four semesters has ,een 1!!+ 1!7+ 199+ and 133 @listed from oldest to most recent). "he ,est forecast of enrollment ne*t semester+ ,ased on a three -semester movin averae+ would ,e A) 115.6. ) 1!5.-. () 157.-. D) 1-3.9. E) 1!6.6. Answer: E Di: 1 "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills
6 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
6) hich of the followin methods produces a particularly sti penalty in periods with lare forecast errorsI A) >AD ) >SE () >A0E D) decomposition E) ,ias Answer: Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ AA(S: %eective "hinBin 7) Sales for ,o*es of =irl Scout cooBies over a -month period were forecasted as follows: 199+ 1!9+ 113+ and 1!-. "he actual results over the -month period were as follows: 119+ 11+ 11;+ 113. hat was the >AD of the -month forecastI A) 9 ) 3 () 6 D) 197 E) $one of the a,ove Answer: ( Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ AA(S: Analytic SBills ;) Sales for ,o*es of =irl Scout cooBies over a -month period were forecasted as follows: 199+ 1!9+ 113+ and 1!-. "he actual results over the -month period were as follows: 119+ 11+ 11;+ 113. hat was the >SE of the -month forecastI A) 9 ) 3 () 6 D) 197 E) $one of the a,ove Answer: E Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ AA(S: Analytic SBills 39) Daily demand for newspapers for the last 19 days has ,een as follows: 1!+ 1-+ 15+ 13+ 1!+ 17+ 1+ 1!+ 1-+ 13 @listed from oldest to most recent). Forecast sales for the ne*t day usin a three -day weihted movin averae where the weihts are -+ 1+ and 1 @the hihest weiht is for the most recent num,er). A) 1!.7 ) 1-.9 () 69.9 D) 1.9 E) $one of the a,ove Answer: D Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 7 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
31) Daily demand for newspapers for the last 19 days has ,een as follows: 1!+ 1-+ 15+ 13+ 1!+ 17+ 1+ 1!+ 1-+ 13 @listed from oldest to most recent). Forecast sales for the ne*t day usin a two -day weihted movin averae where the weihts are - and 1 are A) 1.3. ) 1-.3. () 1. D) 1!.!3. E) 1!.63. Answer: A Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 3!) hich of the followin is not considered to ,e one of the components of a time seriesI A) trend ) seasonality () variance D) cycles E) random variations Answer: ( Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 3-) Enrollment in a particular class for the last four semesters has ,een 1!9+ 1!5+ 119+ and 1-9 @listed from oldest to most recent). Develop a forecast of enrollment ne*t semester usin e*ponential smoothin with an alpha = 9.!. Assume that an initial forecast for the rst semester was 1!9 @so the forecast and the actual were the same). A) 117.;5 ) 1!1.16 () 1-9 D) 1!9 E) $one of the a,ove Answer: Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 3) Demand for soccer ,alls at a new sportin oods store is forecasted usin the followin reression euation: Y = ;7 + !.!X where X is the num,er of months that the store has ,een in e*istence. Let April ,e represented ,y X = . April is assumed to have a seasonality inde* of 1.13. hat is the forecast for soccer ,all demand for the month of April @rounded to the nearest inteer)I A) 1!) 196 () 199 D) 113 E) $one of the a,ove Answer: Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills ; (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
33) A time -series forecastin model in which the forecast for the ne*t period is the actual value for the current period is the A) Delphi model. ) oltJs model. () na?ve model. D) e*ponential smoothin model. E) weihted movin averae. Answer: ( Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 35) #n picBin the smoothin constant for an e*ponential smoothin model+ we should looB for a value that A) produces a nice -looBin curve. ) euals the utility level that matches with our deree of risB aversion. () produces values which compare well with actual values ,ased on a standard measure of error. D) causes the least computational eort. E) $one of the a,ove Answer: ( Di: 1 "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 36) #n the e*ponential smoothin with trend ad4ustment forecastin method+ is the A) slope of the trend line. ) new forecast. () /-a*is intercept. D) independent varia,le. E) trend smoothin constant. Answer: E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 37) "he computer monitorin of tracBin sinals and self -ad4ustment is referred to as A) e*ponential smoothin. ) adaptive smoothin. () trend pro4ections. D) trend smoothin. E) runnin sum of forecast errors @%FSE). Answer: Di: ! "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S
19 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
3;) hich of the followin is not a characteristic of trend pro4ectionsI A) "he varia,le ,ein predicted is the Y varia,le. ) "ime is the X varia,le. () #t is useful for predictin the value of one varia,le ,ased on time trend. D) A neative intercept term always implies that the dependent varia,le is decreasin over time. E) "hey are often developed usin linear reression. Answer: D Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 59) hen ,oth trend and seasonal components are present in time series+ which of the followin is most appropriateI A) the use of centered movin averaes ) the use of movin averaes () the use of simple e*ponential smoothin D) the use of weihted movin averaes E) the use of dou,le smoothin Answer: A Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 51) A tracBin sinal was calculated for a particular set of demand forecasts. "his tracBin sinal was positive. "his would indicate that A) demand is reater than the forecast. ) demand is less than the forecast. () demand is eual to the forecast. D) the >AD is neative. E) $one of the a,ove Answer: A Di: ! "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S 5!) A seasonal inde* of KKKKKKKK indicates that the season is averae. A) 19 ) 199 () 9.3 D) 9 E) 1 Answer: E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS
11 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
5-) "he errors in a particular forecast are as follows: + --+ !+ 3+ -1. hat is the tracBin sinal of the forecastI A) 9.!75 ) !.---() 3 D) 1. E) !.3 Answer: Di: "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S AA(S: Analytic SBills 5) Demand for a particular type of ,a2ery uctuates from one weeB to the ne*t. A study of the last si* weeBs provides the followin demands @in dozens): + 3+ -+ !+ 7+ 19 @last weeB). @a) Forecast demand for the ne*t weeB usin a two -weeB movin averae. @,) Forecast demand for the ne*t weeB usin a three-weeB movin averae. Answer: @a) @7 + 19)! = ; @,) @! + 7 + 19)- = 5.56 Di: 1 "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 53) Daily hih temperatures in the city of ouston for the last weeB have ,een: ;-+ ;+ ;-+ ;3+ ;!+ 75+ ;7 @yesterday). @a) Forecast the hih temperature today usin a three-day movin averae. @,) Forecast the hih temperature today usin a two-day movin averae. @c) (alculate the mean a,solute deviation ,ased on a two -day movin averae+ coverin all days in which you can have a forecast and an actual temperature. Answer: @a) @;! + 75 + ;7)- = ;! @,) @75 + ;7)! = ;! @c) >AD = @ ) 3 = !9.3 3 = .1 + + + + Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ and "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills
1! (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
55) For the data ,elow:
>onth Manuary Fe,ruary >arch April >ay Mune
Automo,ile a2ery Sales !9 !1 13 1 115
>onth Muly Auust Septem,er &cto,er $ovem,er Decem,er
Automo,ile a2ery Sales 16 17 !9 !9 !1 !-
@a) Develop a sca2er diaram. @,) Develop a three -month movin averae. @c) (ompute >AD. Answer: @a) sca2er diaram
1(opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
(b)
>onth Manuary Fe,ruary >arch April >ay Mune Muly Auust Septem,er &cto,er $ovem,er Decem,er Manuary @c)
Automo,ile a2ery Sales !9 !1 13 1 115 16 17 !9 !9 !1 !-
-->onth >ovin Av.
-
A,solute Deviation -
-
-
-
-
-
17.56 15.56 1 1.-13.-16 17.-1;.-!9.-!1.--
.56 -.56 ! !.56 -.56 1.56 1.56 !.56 -
>AD = !.73
Di: "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ and "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 56) For the data ,elow:
>onth Manuary Fe,ruary >arch April >ay Mune @a) @,) @c)
Automo,ile "ire Sales 79 7 59 35 3! 5
>onth Muly Auust Septem,er &cto,er $ovem,er Decem,er
Automo,ile "ire Sales 57 199 79 79 7 ;!
Develop a sca2er diaram. (ompute a three -month movin averae. (ompute the >SE.
1 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
Answer: @a)
sca2er diaram
(b)
>onth Manuary Fe,ruary >arch April >ay Mune Muly Auust Septem,er &cto,er $ovem,er Decem,er Manuary
Automo,ile "ire Sales 79 7 59 35 3! 5 57 199 79 79 7 ;! -
-->onth "ire Averae
Suared Error -
-
-
-
-
-
6.6 55.6 35.9 36.51.66.7!.6 75.6 71.73.--
-;.5; !15.9; 5 11.; 1;6.5; 6.!; 6.!; 6.!; 11.;
@c) >SE = !5.!5 Di: "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ and "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills
13 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
57) For the data ,elow: /ear 1;;9 1;;1 1;;! 1;;1;; 1;;3 1;;5 @a) @,) @c)
Automo,ile Sales 115 193 !; 3; 197 ; !6
/ear 1;66 1;;7 1;;; !999 !991 !99! !99-
Automo,ile Sales 11; - - 7 353 111
Develop a sca2er diaram. Develop a si* -year movin averae forecast. Find >A0E.
Answer: @a) sca2er diaram
15 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
(b)
/ear 1;;9 1;;1 1;;! 1;;1;; 1;;3 1;;5 1;66 1;;7 1;;; !999 !991 !99! !99-
$um,er of Automo,iles 115 193 !; 3; 197 ; !6 11; - - 7 353 111
Forecast N N N N N N 73.! 69.6!.6 6-.3 5;.3;.3!.3 37.7
Error
-37.!
7.6 --7.6 --;.3 -!1.-5.1!.3 3!.!
Error Actual
!.13 9.1 1.1 1.15 9. 9.1! 9.1; 9.6
@c) >A0E = .65 ∗ 199O = 65O Di: "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ and "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills
16 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
5;) 'se simple e*ponential smoothin with G = 9.- to forecast ,a2ery sales for Fe,ruary throuh >ay. Assume that the forecast for Manuary was for !! ,a2eries.
>onth Manuary Fe,ruary >arch April
Automo,ile a2ery Sales ! -!7 3;
Answer: Forecasts for Fe,ruary throuh >ay are: !7+ !;.3+ !;.93+ and -7.9-3. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 69) Averae startin salaries for students usin a placement service at a university have ,een steadily increasin. A study of the last four raduatin classes indicates the followin averae salaries: P-9+999+ P-!+999+ P-+399+ and P-5+999 @last raduatin class). 0redict the startin salary for the ne*t raduatin class usin a simple e*ponential smoothin model with G = 9.!3. Assume that the initial forecast was P-9+999 @so that the forecast and the actual were the same). Answer: Forecast for ne*t period = P-!+5!3 Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 61) 'se simple e*ponential smoothin with G = 9.-- to forecast the tire sales for Fe,ruary throuh >ay. Assume that the forecast for Manuary was for !! sets of tires.
>onth Manuary Fe,ruary >arch April
Automo,ile a2ery Sales !7 !1 -; -
Answer: Forecast for Fe,. throuh >ay = !-.;7+ !!.;;55+ !7.!666+ and -9.1551 Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills
17 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
6!) "he followin ta,le represents the new mem,ers that have ,een acuired ,y a tness center. >onth Man Fe, >arch April
$ew mem,ers 3 59 36 53
Assumin G = 9.-+ C = 9.+ an initial forecast of 9 for Manuary+ and an initial trend ad4ustment of 9 for Manuary+ use e*ponential smoothin with trend ad4ustment to come up with a forecast for >ay on new mem,ers. Answer:
>onth Man Fe, >arch April >ay
$ew mem,ers 3 59 36 53
Ft 9 1.3 6.6 3!.!3!5 37.36911
"t 9 9.5 !.67 -.3517 .55196
F#"t 9 !.1 39.!17 33.71 5-.!-!!
>ay forecast = 37.36 Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 6-) "he followin ta,le represents the num,er of applicants at popular private collee in the last four years. >onth !996 !997 !99; !919
$ew mem,ers 19+956 19+;9 11+115 19+;;;
Assumin G = 9.!+ C = 9.-+ an initial forecast of 19+999 for !996+ and an initial trend ad4ustment of 9 for !996+ use e*ponential smoothin with trend ad4ustment to come up with a forecast for !911 on the num,er of applicants. Answer:
>onth !996 !997 !99; !919 !911
Q of applicants 19+956 19+;9 11+115 19+;;;
Ft 19+999 1991-. 19!91.; 19-!.!3 195-.1!
"t 9 .9! 3;.-67 119.535! 1-7.9!1;
F#"t 19999 19916.! 19!51.-1 193!.; 1966!.13
!911 Forecast = 19+5- Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills
1; (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
6) =iven the followin data+ if >AD = 1.!3+ determine what the actual demand must have ,een in period ! @A !). "ime 0eriod 1 !
Actual @A) ! A! = I 5
Forecast @F) 3 5
1 -
1 !
Answer: A! = - or A! = 3 Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ AA(S: Analytic SBills 63) (alculate @a) >AD+ @,) >SE+ and @c) >A0E for the followin forecast versus actual sales ures. @0lease round to four decimal places for >A0E.) Forecast 199 119 1!9 1-9
Actual ;3 197 1!1-9
Answer: @a) >AD = 19 = !.3 @,) >SE = -7 = ;.3 @c) >A0E = @9.9;35)199 = !.-;O Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ AA(S: Analytic SBills
!9 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
65) 'se the sales data iven ,elow to determine: /ear 1;;3 1;;5 1;;6 1;;7
Sales @units) 1-9 19 13! 159
/ear 1;;; !999 !991 !99!
Sales @units) 15; 17! 1; I
@a) the least suares trend line. @,) the predicted value for !99! sales. @c) the >AD. @d) the unad4usted forecastin >SE. Answer: @a) = 11;.1 + 19.5X @,) 11;.1 + 19.5@7) = !9!.7! @c) >AD = 1.91 @d) >SE = 1.61 Di: "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ and "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 66) For the data ,elow:
/ear 1;;9 1;;1 1;;! 1;;1;; 1;;3 1;;5
Automo,ile Sales 115 193 !; 3; 197 ; !6
/ear 1;66 1;;7 1;;; !999 !991 !99! !99-
Automo,ile Sales 11; - - 7 353 111
@a) Determine the least suares reression line. @,) Determine the predicted value for !99. @c) Determine the >AD. @d) Determine the unad4usted forecastin >SE. Answer: @a) = 73.13 - 1.7X @,) 73.13 - 1.7 @13) = 37.13 @c) >AD = -9.9; @d) >SE = 1+1!1.55 Di: "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ and "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills
!1 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
67) =iven the followin asoline data: &DELS AA(S: Analytic SBills
!(opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
79) "he followin ta,le represents the actual vs. forecasted amount of new customers acuired ,y a ma4or credit card company: >onth Man Fe, >arch April >ay @a) @,)
Actual 19! 1936 19; 195; 1953
Forecast 1919 19!3 111 193193;
hat is the tracBin sinalI ased on the answer in part @a)+ comment on the accuracy of this forecast.
Answer:
>onth Man Fe, >arch April >ay
Actual 19! 1936 19; 195; 1953
Forecast 1919 19!3 111 193193;
Error 1 -! -;! 15 5
%SFE 1 5 -5 --9 -!
1 -! ;! 15 5
@a) %SFE>AD = -!-! = -9.63 >AD @,) "he answer in part @a) indicates an accurate forecast+ one where overall+ the actual amount of new customers was slihtly less than the forecast. Di: "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S AA(S: Analytic SBills 71) hat are the eiht steps to forecastinI Answer: @1) Determine the use of the forecastUwhat o,4ective are we tryin to o,tainI @!) Select the items or uantities that are to ,e forecasted. @-) Determine the time horizon of the forecastUis it 1 to -9 days @short term)+ 1 month to 1 year @medium term)+ or more than 1 year @lon term)I @) Select the forecastin model or models. @3) =ather the data needed to maBe the forecast+ @5) Validate the forecastin model+ @6) >aBe the forecast+ and @7) #mplement the results. Di: "opic: #$"%&D'("#&$ 7!) #n eneral terms+ descri,e what causal forecastin models are. Answer: (ausal forecastin models incorporate varia,les or factors that miht inuence the uantity ,ein forecasted. Di: ! "opic: "/0ES &F F&%E(AS"S 7-) #n eneral terms+ descri,e what ualitative forecastin models are. Answer: S A$D "#>E SE%#ES 73) riey descri,e the 4ury of e*ecutive opinion forecastin method. Answer: "he 4ury of e*ecutive opinion forecastin model uses the opinions of a small roup of hih -level manaers+ often in com,ination with statistical models+ and results in a roup estimate of demand. Di: ! "opic: "/0ES &F F&%E(AS"S 75) riey descri,e the consumer marBet survey forecastin method. Answer: #t is a forecastin method that solicits input from customers or potential customers reardin their future purchasin plans. Di: ! "opic: "/0ES &F F&%E(AS"S 76) Descri,e the na?ve forecastin method. Answer: "he forecast for the ne*t period is the actual value o,served in the current period. Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ 77) riey descri,e why the sca2er diaram is helpful. Answer: Sca2er diarams show the relationships ,etween model varia,les. Di: 1 "opic: S(A""E% D#A=%A>S A$D "#>E SE%#ES 7;) E*plain+ ,riey+ why most forecastin error measures use either the a,solute or the suare of the error. Answer: A deviation is eually important whether it is a,ove or ,elow the actual. "his also prevents neative errors from cancelin positive errors that would understate the true size of the errors. Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ ;9) List four measures of historical forecastin errors. Answer: >AD+ >SE+ >A0E+ and ias Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ ;1) #n eneral terms+ descri,e what time-series forecastin models are. Answer: forecastin models that maBe use of historical data Di: 1 "opic: "/0ES &F F&%E(AS"S ;!) List four components of time -series data. Answer: trend+ seasonality+ cycles+ and random variations Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS
!3 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
;-) E*plain+ ,riey+ why the larer num,er of periods included in a movin averae forecast+ the less well the forecast identies rapid chanes in the varia,le of interest. Answer: "he larer the num,er of periods included in the movin averae forecast+ the less the averae is chaned ,y the addition or deletion of a sinle num,er. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;) State the mathematical e*pression for e*ponential smoothin. Answer: Ft+1 = Ft + G@Y t - Ft) Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;3) E*plain+ ,riey+ why+ in the e*ponential smoothin forecastin method+ the larer the value of the smoothin constant+ G+ the ,e2er the forecast will ,e in allowin the user to see rapid chanes in the varia,le of interest. Answer: "he larer the value of G+ the reater is the weiht placed on the most recent values. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;5) #n e*ponential smoothin+ discuss the dierence ,etween G and C. Answer: G is a weiht applied to ad4ust for the dierence ,etween last period actual and forecasted value. C is a trend smoothin constant. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;6) #n eneral terms+ descri,e the dierence ,etween a eneral linear reression model and a trend pro4ection. Answer: A trend pro4ection is a reression model where the independent varia,le is always time. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;7) #n eneral terms+ descri,e a centered movin averae. Answer: An averae of the values centered at a particular point in time. "his is used to compute seasonal indices when trend is present. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;;) "he decomposition approach to forecastin @usin trend and seasonal components) may ,e helpful when a2emptin to forecast a time -series. (ould an analoous approach ,e used in multiple reression analysisI E*plain ,riey. Answer: An analoous approach would ,e possi,le usin time as one independent varia,le and usin a set of dummy varia,les to represent the seasons. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS
!5 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
199) hat is one advantae of usin causal models over time -series or ualitative modelsI Answer: 'se of the causal model reuires that the forecaster ain an understandin of the relationships+ not merely the freuency of variationW i.e.+ the forecaster ains a reater understandin of the pro,lem than the other methods. Di: ! "opic: "/0ES &F F&%E(AS"S AA(S: %eective "hinBin 191) Discuss the use of a tracBin sinal. Answer: A tracBin sinal measures how well predictions t actual data. y se2in tracBin limits+ a manaer is sinaled to reevaluate the forecastin method. Di: ! "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S
!6 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all
View more...
Comments
