1. 1 1 Slide © 2005 Thomson/South-Western Final Exam (listed) for 2008: December 2 Due Day: December 9 (9:00AM) Exam Materials: All the Topics After Mid Term Exam

2. 2 2 Slide © 2005 Thomson/South-Western EMGT 501 HW Solutions Chapter 15 - SELF TEST 9 Chapter 15 - SELF TEST 35

3. 3 3 Slide © 2005 Thomson/South-Western 15-9a. MethodForecastMSE 3-Quarter80.732.53 4-Quarter80.552.81 The 3-quarter moving average forecast is better because it has the smallest MSE.

4. 4 4 Slide © 2005 Thomson/South-Western b. MethodForecastMSE  =.4 80.402.40  =.5 80.572.01 The a =.5 smoothing constant is better because it has the smallest MSE. c.The exponential smoothing is better because it has the smallest MSE.

5. 5 5 Slide © 2005 Thomson/South-Western 15-35a. xixi yiyi xiyixiyi xi2xi2 20 21 420 400 20 19 380 400 40 15 6001600 30 16 480 900 60 14 8403600 40 17 6801600 21010234008500

6. 6 6 Slide © 2005 Thomson/South-Western = 22.173 - 0.1478(50) = 14.783 or approximately 15 defective parts b.

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11. 11 Slide © 2005 Thomson/South-Western Chapter 16 Markov Processes n Transition Probabilities n Steady-State Probabilities n Absorbing States n Transition Matrix with Submatrices n Fundamental Matrix

12. 12 Slide © 2005 Thomson/South-Western Markov Processes n Markov process models are useful in studying the evolution of systems over repeated trials or sequential time periods or stages. n They have been used to describe the probability that: a machine that is functioning in one period will continue to function or break down in the next period. a machine that is functioning in one period will continue to function or break down in the next period. A consumer purchasing brand A in one period will purchase brand B in the next period. A consumer purchasing brand A in one period will purchase brand B in the next period.

13. 13 Slide © 2005 Thomson/South-Western Transition Probabilities n Transition probabilities govern the manner in which the state of the system changes from one stage to the next. These are often represented in a transition matrix.

14. 14 Slide © 2005 Thomson/South-Western Transition Probabilities n A system has a finite Markov chain with stationary transition probabilities if: there are a finite number of states, there are a finite number of states, the transition probabilities remain constant from stage to stage, and the transition probabilities remain constant from stage to stage, and the probability of the process being in a particular state at stage n+ 1 is completely determined by the state of the process at stage n (and not the state at stage n- 1). This is referred to as the memory-less property. the probability of the process being in a particular state at stage n+ 1 is completely determined by the state of the process at stage n (and not the state at stage n- 1). This is referred to as the memory-less property.

15. 15 Slide © 2005 Thomson/South-Western Steady-State Probabilities n The state probabilities at any stage of the process can be recursively calculated by multiplying the initial state probabilities by the state of the process at stage n. n The probability of the system being in a particular state after a large number of stages is called a steady-state probability.

16. 16 Slide © 2005 Thomson/South-Western Steady-State Probabilities Steady state probabilities can be found by solving the system of equations  P =  together with the condition for probabilities that  i = 1. Steady state probabilities can be found by solving the system of equations  P =  together with the condition for probabilities that  i = 1. Matrix P is the transition probability matrix Matrix P is the transition probability matrix Vector  is the vector of steady state probabilities. Vector  is the vector of steady state probabilities.

17. 17 Slide © 2005 Thomson/South-Western Absorbing States n An absorbing state is one in which the probability that the process remains in that state once it enters the state is 1. n If there is more than one absorbing state, then a steady-state condition independent of initial state conditions does not exist.

18. 18 Slide © 2005 Thomson/South-Western Transition Matrix with Submatrices n If a Markov chain has both absorbing and nonabsorbing states, the states may be rearranged so that the transition matrix can be written as the following composition of four submatrices: I, 0, R, and Q : I 0 I 0 R Q R Q

19. 19 Slide © 2005 Thomson/South-Western Transition Matrix with Submatrices I = an identity matrix indicating one always remains in an absorbing state once it is reached I = an identity matrix indicating one always remains in an absorbing state once it is reached 0 = a zero matrix representing 0 probability of 0 = a zero matrix representing 0 probability of transitioning from the absorbing states to the transitioning from the absorbing states to the nonabsorbing states nonabsorbing states R = the transition probabilities from the nonabsorbing states to the absorbing states R = the transition probabilities from the nonabsorbing states to the absorbing states Q = the transition probabilities between the nonabsorbing states Q = the transition probabilities between the nonabsorbing states

20. 20 Slide © 2005 Thomson/South-Western Fundamental Matrix n The fundamental matrix, N, is the inverse of the difference between the identity matrix and the Q matrix. N = ( I - Q ) -1 N = ( I - Q ) -1

21. 21 Slide © 2005 Thomson/South-Western NR Matrix n The NR matrix is the product of the fundamental ( N ) matrix and the R matrix. n It gives the probabilities of eventually moving from each nonabsorbing state to each absorbing state. n Multiplying any vector of initial nonabsorbing state probabilities by NR gives the vector of probabilities for the process eventually reaching each of the absorbing states. Such computations enable economic analyses of systems and policies.

22. 22 Slide © 2005 Thomson/South-Western Example: North’s Hardware Henry, a persistent salesman, calls North's Hardware Store once a week hoping to speak Hardware Store once a week hoping to speak with the store's buying agent, Shirley. If Shirley does not accept Henry's call this week, the probability she will do the same next week is.35. On the other hand, if she accepts Henry's call this week, the probability she will not do so next week is.20.

23. 23 Slide © 2005 Thomson/South-Western Example: North’s Hardware n Transition Matrix Next Week's Call Next Week's Call Refuses Accepts Refuses Accepts This Refuses.35.65 This Refuses.35.65 Week's Week's Call Accepts.20.80 Call Accepts.20.80

24. 24 Slide © 2005 Thomson/South-Western n Steady-State Probabilities Question How many times per year can Henry expect to talk to Shirley? Answer To find the expected number of accepted calls per year, find the long-run proportion (probability) of a call being accepted and multiply it by 52 weeks. continued... continued... Example: North’s Hardware

25. 25 Slide © 2005 Thomson/South-Western n Steady-State Probabilities Answer (continued) Let  1 = long run proportion of refused calls  2 = long run proportion of accepted calls  2 = long run proportion of accepted calls Then, Then,.35.65.35.65 [     ] = [     ] [     ] = [     ].20.80.20.80 continued... Example: North’s Hardware

26. 26 Slide © 2005 Thomson/South-Western n Steady-State Probabilities Answer (continued)   +   =   (1)   +   =   (1)   +   =   (2)   +   =   (2)   +   = 1 (3)   +   = 1 (3) Solve for   and    Solve for   and    continued... continued... Example: North’s Hardware

27. 27 Slide © 2005 Thomson/South-Western Example: North’s Hardware n Steady-State Probabilities Answer (continued) Solving using equations (2) and (3). (Equation 1 is redundant.) Substitute   = 1 -   into (2) to give: Solving using equations (2) and (3). (Equation 1 is redundant.) Substitute   = 1 -   into (2) to give:.65(1 -  2 ) +   =  2.65(1 -  2 ) +   =  2 This gives   =.76471. Substituting back into equation (3) gives   =.23529. Thus the expected number of accepted calls per year is: (.76471)(52) = 39.76 or about 40 (.76471)(52) = 39.76 or about 40

28. 28 Slide © 2005 Thomson/South-Western n State Probability Question What is the probability Shirley will accept Henry's next two calls if she does not accept his call this week? What is the probability Shirley will accept Henry's next two calls if she does not accept his call this week? Example: North’s Hardware

29. 29 Slide © 2005 Thomson/South-Western Example: North’s Hardware n State Probability Answer Answer P =.35(.35) =.1225 P =.35(.65) =.2275 P =.65(.20) =.1300 Refuses Refuses Refuses Refuses Accepts Accepts Accepts.35.35.65.20.80.65 P =.65(.80) =.5200

30. 30 Slide © 2005 Thomson/South-Western n State Probability Question What is the probability of Shirley accepting exactly one of Henry's next two calls if she accepts his call this week? Example: North’s Hardware

31. 31 Slide © 2005 Thomson/South-Western Example: North’s Hardware n State Probability Answer Answer The probability of exactly one of the next two calls being accepted if this week's call is accepted can be found by adding the probabilities of (accept next week and refuse the following week) and (refuse next week and accept the following week) = The probability of exactly one of the next two calls being accepted if this week's call is accepted can be found by adding the probabilities of (accept next week and refuse the following week) and (refuse next week and accept the following week) =.12 +.16 =.28

32. 32 Slide © 2005 Thomson/South-Western The vice president of personnel at Jetair Aerospace has noticed that yearly shifts in personnel can be modeled by a Markov process. The transition matrix is: Next Year Next Year Same Pos. Promotion Retire Quit Fired Same Pos. Promotion Retire Quit Fired Current Year Current Year Same Position.55.10.05.20.10 Same Position.55.10.05.20.10 Promotion.70.20 0.10 0 Promotion.70.20 0.10 0 Retire 0 0 1 0 0 Retire 0 0 1 0 0 Quit 0 0 0 1 0 Quit 0 0 0 1 0 Fired 0 0 0 0 1 Fired 0 0 0 0 1 Example: Jetair Aerospace

33. 33 Slide © 2005 Thomson/South-Western Example: Jetair Aerospace n Transition Matrix Next Year Next Year Retire Quit Fired Same Promotion Retire Quit Fired Same Promotion Current Year Current Year Retire 1 0 0 0 0 Retire 1 0 0 0 0 Quit 0 1 0 0 0 Quit 0 1 0 0 0 Fired 0 0 1 0 0 Fired 0 0 1 0 0 Same.05.20.10.55.10 Same.05.20.10.55.10 Promotion 0.10 0.70.20 Promotion 0.10 0.70.20

34. 34 Slide © 2005 Thomson/South-Western n Fundamental Matrix -1 -1 -1 -1 1 0.55.10.45 -.10 1 0.55.10.45 -.10 N = ( I - Q ) -1 = - = N = ( I - Q ) -1 = - = 0 1.70.20 -.70.80 0 1.70.20 -.70.80 Example: Jetair Aerospace

35. 35 Slide © 2005 Thomson/South-Western Example: Jetair Aerospace n Fundamental Matrix The determinant, d = a  a  - a  a  = (.45)(.80) - (-.70)(-.10) =.29 = (.45)(.80) - (-.70)(-.10) =.29 Thus, Thus,.80/.29.10/.29 2.76.34.80/.29.10/.29 2.76.34 N = = N = =.70/.29.45/.29 2.41 1.55.70/.29.45/.29 2.41 1.55

36. 36 Slide © 2005 Thomson/South-Western Example: Jetair Aerospace n NR Matrix The probabilities of eventually moving to the absorbing states from the nonabsorbing states are given by: 2.76.34.05.20.10 2.76.34.05.20.10 NR = x NR = x 2.41 1.55 0.10 0 2.41 1.55 0.10 0

37. 37 Slide © 2005 Thomson/South-Western Example: Jetair Aerospace n NR Matrix (continued) Retire Quit Fired Retire Quit Fired Same.14.59.28 Same.14.59.28 NR = NR = Promotion.12.64.24 Promotion.12.64.24

38. 38 Slide © 2005 Thomson/South-Western Example: Jetair Aerospace n Absorbing States Question What is the probability of someone who was just promoted eventually retiring?... quitting?... being fired?

39. 39 Slide © 2005 Thomson/South-Western Example: Jetair Aerospace n Absorbing States (continued) Answer The answers are given by the bottom row of the NR matrix. The answers are therefore: Eventually Retiring =.12 Eventually Retiring =.12 Eventually Quitting =.64 Eventually Quitting =.64 Eventually Being Fired =.24 Eventually Being Fired =.24