Decision Science | Chapter 2 | Part 2 | MBA MCQs | DS

Decision Science MCQs

  

 

1. If the probability of making a transition from a state is 0, then that state is called a(n)
   1.   steady state.
   2.   absorbing state.
   3.   origin state
   4.   final state
2. Absorbing state probabilities are the same as
   1.   steady state probabilities.
   2.   transition probabilities
   3.   fundamental probabilities
   4.   None of the alternatives is true.
3. Markov analysis might be effectively used for
   1.   technology transfer studies.
   2.   university retention analysis.
   3.   accounts receivable analysis.
   4.   all of the above
4. The following is not an assumption of Markov analysis.
   1.   a. There is an infinite number of possible states.
   2.   b. The probability of changing states remains the same over time.
   3.   (a) and (c)
   4.   c . We can predict any future state from the previous state and the matrix of transition
      probabilities
5. The total cost for a waiting line does NOT specifically depend on
   1.   the cost of waiting.
   2.   the cost of service.
   3.   the cost of a lost customer
   4.   the number of units in the system.
6. Markov analysis assumes that conditions are both
   1.   complementary and collectively exhaustive.
   2.   collectively dependent and complementary
   3.   collectively dependent and mutually exclusive.
   4.   collectively exhaustive and mutually exclusive
7. Occasionally, a state is entered which will not allow going to another state in the future. This is called
   1.   an equilibrium state.
   2.   none of the above
   3.   market saturation
   4.   stable mobility.
8. Markov analysis is a technique that deals with the probabilities of future occurrences by
   1.   using Bayes' theorem.
   2.   analyzing presently known probabilities.
   3.   time series forecasting.
   4.   the maximal flow technique
9. In Markov analysis, the likelihood that any system will change from one period to the next is revealed by the
   1.   identity matrix.
   2.   matrix of transition probabilities
   3.   matrix of state probabilities.
   4.   transition-elasticities.
10. The condition that a system can be in only one state at any point in time is known as
    1.   Transient state.
    2.   Collectively exhaustive condition
    3.   Absorbent condition.
    4.   Mutually exclusive condition
11. At any period n, the state probabilities for the next period n+1 is given by the
    following formula:
    1.   n(n+1)=n(n)P
    2.   n(n+1)=n(n)Pn
    3.   n(n+1)=n(0)P
    4.   n(n+1)=(n+1)P
12. If we decide to use Markov analysis to study the transfer of technology,
    1.   our study will be methodologically flawed.
    2.   we can only study the transitions among three different technologies
    3.   only constant changes in the matrix of transition probabilities can be handled in the simple model
    4.   our study will have only limited value because the Markov analysis tells us "what" will
       happen, but not "why.
13. Markov analysis assumes that the states are both __________ and __________.
    1.   finite, recurrent
    2.   b) infinite, absorbing
    3.   generally inclusive, always independent
    4.   collectively exhaustive, mutually exclusive
14. A simulation model uses the mathematical expressions and logical relationships of the
    1.   estimated inferences.
    2.   computer model.
    3.   performance measures.
    4.   real system.
15. The ________ determine(s) the equilibrium of a Markov process.
    1.   original state probabilities
    2.   transition matrix
    3.   fundamental matrix
    4.   state vecto
16. Values for the probabilistic inputs to a simulation
    1.   are selected by the decision maker.
    2.   are controlled by the decision maker.
    3.   are calculated by fixed mathematical formulas.
    4.   are randomly generated based on historical information.
17. A quantity that is difficult to measure with certainty is called a
    1.   risk analysis.
    2.   project determinant.
    3.   probabilistic input.
    4.   profit/loss process.
18. A value for probabilistic input from a discrete probability distribution
    1.   is the value given by the RAND() function.
    2.   must be non-negative.
    3.   is between 0 and 1.
    4.   is given by matching the probabilistic input with an interval of random numbers.
19. The number of units expected to be sold is uniformly distributed between 300 and 500. If r is a random number between 0 and 1, then the proper expression for sales is
    1.   200(r)
    2.   r + 300
    3.   300 + 500(r)
    4.   300 + r(200)
20. In order to verify a simulation model
    1.   confirm that the model accurately represents the real system.
    2.   compare results from several simulation languages.
    3.   run the model long enough to overcome initial start-up results.
    4.   be sure that the procedures for calculations are logically correct.