rsh_qam11_tif_05.doc

May 7, 2018 | Author: Jay Brock | Category: Forecasting, Time Series, Moving Average, Seasonality, Regression Analysis
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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: #$"%&D'("#&$ !) %eression is always a superior forecastin method to e*ponential smoothin+ so reression should ,e used whenever the appropriate software is availa,le. Answer: FALSE Di: 1 "opic: #$"%&D'("#&$ -) "he three cateories 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 4udment 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 averae 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

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;) S A$D "#>E SE%#ES 1-) "he Delphi method solicits input from customers or potential customers reardin their future purchasin plans. Answer: FALSE Di: ! "opic: "/0ES &F F&%E(AS"S F&%E(AS"S 1) "he na?ve forecast for the ne*t period is the actual value o,served in the current period. Answer: "%'E Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ 13) >ean a,solute deviation @>AD) is simply the sum of forecast errors. Answer: FALSE Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ 15) "ime -series models ena,le the forecaster to include specic representations of various ualitative ualitative and uantitative factors. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 16) Four components of time series are trend+ t rend+ movin averae+ e*ponential smoothin+ and seasonality. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS

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17) "he fewer the periods over which one taBes a movin averae+ 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"#$= >&DELS 1;) #n a weihted movin averae+ the weihts assined must sum to 1. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !9) A sca2er diaram 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$D "#>E SE%#ES !1) Sca2er diarams can ,e useful in spo2in trends or cycles in data over time. Answer: "%'E Di: 1 "opic: S(A""E% D#A=%A>S A$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"#$= >&DELS !-) #n a second order e*ponential smoothin+ a low C ives less weiht to more recent t rends. Answer: "%'E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !) An advantae of e*ponential smoothin over a simple movin averae is that e*ponential smoothin reuires one to retain less data. Answer: "%'E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: %eective "hinBin !3) hen the smoothin constant G = 9+ the e*ponential smoothin model is euivalent to the na?ve forecastin model. Answer: FALSE Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&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 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

!6) A seasonal inde* of 1 means that the season is averae. Answer: "%'E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !7) "he process of isolatin linear trend and seasonal factors to develop a more accurate forecast is called reression. Answer: FALSE Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS !;) hen the smoothin constant G = 1+ the e*ponential smoothin model is euivalent to the na?ve forecastin model. Answer: "%'E Di: "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills -9) Adaptive smoothin is analoous to e*ponential smoothin where the coeHcients G and C are periodically updated to improve the forecast. Answer: "%'E Di: ! "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S -1) ias is the averae error of a forecast model. Answer: "%'E Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ -!) hich of the followin is not classied 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 4udmental forecastin techniue 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  (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

-) hich of the followin is considered a causal method of forecastinI A) e*ponential smoothin ) movin averae () oltJs method D) Delphi method E) $one of the a,ove Answer: E Di: ! "opic: "/0ES &F F&%E(AS"S -3) A raphical plot with sales on the Y  a*is and time on the X a*is is a A) ca2er diaram. ) trend pro4ection. () radar chart. D) line raph. E) ,ar chart. Answer: A Di: ! "opic: S(A""E% D#A=%A>S A$D "#>E SE%#ES -5) hich of the followin statements a,out sca2er diarams 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$D "#>E SE%#ES -6) hich of the followin is a techniue used to determine forecastin accuracyI A) e*ponential smoothin ) movin averae () reression D) Delphi method E) mean a,solute percent error Answer: E Di: 1 "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ -7) A medium -term forecast is considered to cover what lenth of timeI A) !- weeBs ) 1 month to 1 year () !- years D) 3-19 years E) !9 years Answer:  Di: ! "opic: #$"%&D'("#&$ 3 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

-;) hen is the e*ponential smoothin model euivalent 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 averae 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 hih 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 dierent 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(/

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-) hich of the followin methods ives an indication of the percentae 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 averae. A) 1 ) 1() 13 D) !7 E) 1!.3 Answer: A Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 3) As one increases the num,er of periods used in the calculation of a movin averae+ A) reater emphasis is placed on more recent data. ) less emphasis is placed on more recent data. () the emphasis placed on more recent data remains the same. D) it reuires a computer to automate the calculations. E) one is usually looBin for a lon -term prediction. Answer:  Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: %eective "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 averae+ 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

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6) hich of the followin methods produces a particularly sti penalty in periods with lare forecast errorsI A) >AD ) >SE () >A0E D) decomposition E) ,ias Answer:  Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ AA(S: %eective "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 weihted movin averae where the weihts are -+ 1+ and 1 @the hihest weiht 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 (opyriht 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 weihted movin averae where the weihts 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 reression euation: 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 inteer)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 ; (opyriht 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) weihted movin averae. 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. ) euals the utility level that matches with our deree of risB aversion. () produces values which compare well with actual values ,ased on a standard measure of error. D) causes the least computational eort. 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"#$= >&DELS 37) "he computer monitorin of tracBin sinals and self -ad4ustment is referred to as A) e*ponential smoothin. ) adaptive smoothin. () trend pro4ections. D) trend smoothin. E) runnin sum of forecast errors @%FSE). Answer:  Di: ! "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S

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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 neative intercept term always implies that the dependent varia,le is decreasin over time. E) "hey are often developed usin linear reression. Answer: D Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 59) hen ,oth trend and seasonal components are present in time series+ which of the followin is most appropriateI A) the use of centered movin averaes ) the use of movin averaes () the use of simple e*ponential smoothin D) the use of weihted movin averaes E) the use of dou,le smoothin Answer: A Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS 51) A tracBin sinal was calculated for a particular set of demand forecasts. "his tracBin sinal was positive. "his would indicate that A) demand is reater than the forecast. ) demand is less than the forecast. () demand is eual to the forecast. D) the >AD is neative. 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 averae. A) 19 ) 199 () 9.3 D) 9 E) 1 Answer: E Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS

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5-) "he errors in a particular forecast are as follows: + --+ !+ 3+ -1. hat is the tracBin sinal of the forecastI A) 9.!75 ) !.---() 3 D) 1. E) !.3 Answer:  Di: "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S AA(S: Analytic SBills 5) Demand for a particular type of ,a2ery uctuates from one weeB to the ne*t. A study of the last si* weeBs provides the followin demands @in dozens): + 3+ -+ !+ 7+ 19 @last weeB). @a) Forecast demand for the ne*t weeB usin a two -weeB movin averae. @,) Forecast demand for the ne*t weeB usin a three-weeB movin averae. Answer: @a) @7 + 19)! = ; @,) @! + 7 + 19)- = 5.56 Di: 1 "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 53) Daily hih temperatures in the city of ouston for the last weeB have ,een: ;-+ ;+ ;-+ ;3+ ;!+ 75+ ;7 @yesterday). @a) Forecast the hih temperature today usin a three-day movin averae. @,) Forecast the hih temperature today usin a two-day movin averae. @c) (alculate the mean a,solute deviation ,ased on a two -day movin averae+ 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

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55) For the data ,elow:

>onth  Manuary Fe,ruary >arch April >ay  Mune

Automo,ile a2ery Sales !9 !1 13 1 115

>onth Muly Auust Septem,er &cto,er $ovem,er Decem,er

Automo,ile a2ery Sales 16 17 !9 !9 !1 !-

@a) Develop a sca2er diaram. @,) Develop a three -month movin averae. @c) (ompute >AD. Answer: @a) sca2er diaram

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(b)

>onth  Manuary Fe,ruary >arch April >ay  Mune  Muly Auust 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 Auust Septem,er &cto,er $ovem,er Decem,er

Automo,ile "ire Sales 57 199 79 79 7 ;!

Develop a sca2er diaram. (ompute a three -month movin averae. (ompute the >SE.

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Answer: @a)

sca2er diaram

(b)

>onth  Manuary Fe,ruary >arch April >ay  Mune  Muly Auust 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 Averae

Suared 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 (opyriht 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 diaram. Develop a si* -year movin averae forecast. Find >A0E.

Answer: @a) sca2er diaram

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(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 (opyriht 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 throuh >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 throuh >ay are: !7+ !;.3+ !;.93+ and -7.9-3. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 69) Averae startin salaries for students usin a placement service at a university have ,een steadily increasin. A study of the last four raduatin classes indicates the followin averae salaries: P-9+999+ P-!+999+ P-+399+ and P-5+999 @last raduatin class). 0redict the startin salary for the ne*t raduatin class usin a simple e*ponential smoothin model with G = 9.!3. Assume that the initial forecast was P-9+999 @so that the forecast and the actual were the same). Answer: Forecast for ne*t period = P-!+5!3 Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills 61) 'se simple e*ponential smoothin with G = 9.-- to forecast the tire sales for Fe,ruary throuh >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,. throuh >ay = !-.;7+ !!.;;55+ !7.!666+ and -9.1551 Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS AA(S: Analytic SBills

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6!) "he followin ta,le represents the new mem,ers that have ,een acuired ,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 !.67 -.3517 .55196

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 collee 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;.-67 119.535! 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; (opyriht 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 (opyriht 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 19 13! 159

/ear 1;;; !999 !991 !99!

Sales @units) 15; 17! 1; I

@a) the least suares 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 suares reression 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 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

67) =iven the followin asoline data: &DELS AA(S: Analytic SBills

!(opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

79) "he followin ta,le represents the actual vs. forecasted amount of new customers acuired ,y a ma4or credit card company: >onth  Man Fe, >arch April >ay @a) @,)

Actual 19! 1936 19; 195; 1953

Forecast 1919 19!3 111 193193;

hat is the tracBin sinalI 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 111 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 slihtly less than the forecast. Di: "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S AA(S: Analytic SBills 71) hat are the eiht steps to forecastinI Answer: @1) Determine the use of the forecastUwhat o,4ective are we tryin to o,tainI @!) Select the items or uantities that are to ,e forecasted. @-) Determine the time horizon of the forecastUis it 1 to -9 days @short term)+ 1 month to 1 year @medium term)+ or more than 1 year @lon term)I @) Select the forecastin model or models. @3) =ather the data needed to maBe the forecast+ @5) Validate the forecastin model+ @6) >aBe the forecast+ and @7) #mplement the results. Di: "opic: #$"%&D'("#&$ 7!) #n eneral terms+ descri,e what causal forecastin models are. Answer: (ausal forecastin models incorporate varia,les or factors that miht inuence 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$D "#>E SE%#ES 73) riey descri,e the 4ury of e*ecutive opinion forecastin method. Answer: "he 4ury of e*ecutive opinion forecastin model uses the opinions of a small roup of hih -level manaers+ often in com,ination with statistical models+ and results in a roup estimate of demand. Di: ! "opic: "/0ES &F F&%E(AS"S 75) riey descri,e the consumer marBet survey forecastin method. Answer: #t is a forecastin method that solicits input from customers or potential customers reardin their future purchasin plans. Di: ! "opic: "/0ES &F F&%E(AS"S 76) Descri,e the na?ve forecastin method. Answer: "he forecast for the ne*t period is the actual value o,served in the current period. Di: ! "opic: >EAS'%ES &F F&%E(AS" A(('%A(/ 77) riey descri,e why the sca2er diaram is helpful. Answer: Sca2er diarams show the relationships ,etween model varia,les. Di: 1 "opic: S(A""E% D#A=%A>S A$D "#>E SE%#ES 7;) E*plain+ ,riey+ why most forecastin error measures use either the a,solute or the suare of the error. Answer: A deviation is eually important whether it is a,ove or ,elow the actual. "his also prevents neative 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 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

;-) E*plain+ ,riey+ why the larer num,er of periods included in a movin averae forecast+ the less well the forecast identies rapid chanes in the varia,le of interest. Answer: "he larer the num,er of periods included in the movin averae forecast+ the less the averae is chaned ,y the addition or deletion of a sinle 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+ ,riey+ why+ in the e*ponential smoothin forecastin method+ the larer the value of the smoothin constant+ G+ the ,e2er the forecast will ,e in allowin the user to see rapid chanes in the varia,le of interest. Answer: "he larer the value of G+ the reater is the weiht placed on the most recent values. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;5) #n e*ponential smoothin+ discuss the dierence ,etween G and C. Answer: G is a weiht applied to ad4ust for the dierence ,etween last period actual and forecasted value. C is a trend smoothin constant. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;6) #n eneral terms+ descri,e the dierence ,etween a eneral linear reression model and a trend pro4ection. Answer: A trend pro4ection is a reression model where the independent varia,le is always time. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;7) #n eneral terms+ descri,e a centered movin averae. Answer: An averae of the values centered at a particular point in time. "his is used to compute seasonal indices when trend is present. Di: ! "opic: "#>E -SE%#ES F&%E(AS"#$= >&DELS ;;) "he decomposition approach to forecastin @usin trend and seasonal components) may ,e helpful when a2emptin to forecast a time -series. (ould an analoous approach ,e used in multiple reression analysisI E*plain ,riey. Answer: An analoous 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 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

199) hat is one advantae of usin causal models over time -series or ualitative modelsI Answer: 'se of the causal model reuires that the forecaster ain an understandin of the relationships+ not merely the freuency 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: %eective "hinBin 191) Discuss the use of a tracBin sinal. Answer: A tracBin sinal measures how well predictions t actual data. y se2in tracBin limits+ a manaer is sinaled to reevaluate the forecastin method. Di: ! "opic: >&$#"&%#$= A$D (&$"%&LL#$= F&%E(AS"S

!6 (opyriht 8 !91! 0earson Education+ #nc. pu,lishin as 0rentice all

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