If the states in a system or process are such that the system can be in only one state at a time, then the states are

1. collectively exhaustive.

2. mutually exclusive.

3. absorbing.

4. disappearing.

The product of a vector of state probabilities and the matrix of transition probabilities is

1. another vector of state probabilities.

2. a meaningless mess.

3. the inverse of the equilibrium state matrix.

4. all of the above.

5. none of the above.

In the long run, the state probabilities will be 0 and 1

1. in no instances.

2. in all instances.

3. in some instances.

To find equilibrium conditions,

1. the first vector of state probabilities must be known.

2. the matrix of transition probabilities is unnecessary.

3. the general terms in the vector of state probabilities are used on two occasions.

4. the matrix of transition probabilities must be squared before it is inverted.

5. none of the above.

Which of the following is not one of the assumptions of Markov analysis?

1. There is a limited number of possible states.

2. There is a limited number of possible future periods.

3. A future state can be predicted from the previous state and the matrix of transition probabilities.

4. The size and makeup of the system do not change during the analysis.

5. All of the above are assumptions of Markov analysis.

In Markov analysis, the state probabilities must

1. sum to 1.

2. be less than 0.

3. be less than 0.01.

4. be greater than 1.

5. be greater than 0.01.

If the state probabilities do not change from one period to the next, then

1. the system is in equilibrium.

2. each state probability must equal 0.

3. each state probability must equal 1.

4. the system is in its fundamental state.

In the matrix of transition probabilities,

1. the sum of the probabilities in each row will equal 1.

2. the sum of the probabilities in each column will equal 1.

3. there must be at least one 0 in each row.

4. there must be at least one 0 in each column.

It is necessary to use the fundamental matrix

1. to find the equilibrium conditions when there are no absorbing states.

2. to find the equilibrium conditions when there is one or more absorbing states.

3. to find the matrix of transition probabilities.

4. to find the inverse of a matrix.

In Markov analysis, the       allows us to get from a current state to a future state.

In Markov analysis, we assume that the state probabilities are both         and         .

The         is the probability that the system is in a particular state.