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I don\'t really understand this class or stats in general. could you explain as

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Question

I don't really understand this class or stats in general. could you explain as simply as possible? Thank You! I will rate! :)

5. (10 pts) Suppose, that whether or not it rains today depends on previous weather conditions through the last three days. (a) Show how this system may be analyzed by using a Markov Chain. How many states are needed? (b) Suppose that if it has rained for all of the past three days, then it will rain today with probability 0.8; if it didn't rain for the past three days, it will rain today with probability 0.2; and in any other case the weather today will, with probability 0.6 be the same as the weather yesterday. Determine P(transition matrix) for this Markov Chain.

Explanation / Answer

Define the following states. Let i = the day. For example if today is day i, then yesterday is day i-1, and the day before is day i-2. Table 1 is a summary of the states. Y denotes that it did rain on day i, where N denotes that it did not rain on day i.

Table 1: State Definition

Rain (Y/N)

0

Y

Y

Y

1

N

Y

Y

2

Y

N

Y

3

Y

Y

N

4

N

N

Y

5

Y

N

N

6

N

Y

N

7

N

N

N

Now we need to determine which transition probabilities Pij are 0. Table 2 should help.

Table 2: Definitions of Transition Probabilitities

Pij

Day j=i+1

Day i

Day i-1

Day i-2

P00

Y

Y

Y

Y

P10=0

Y

N

Y

Y

P20=0

Y

Y

N

Y

P30

Y

Y

Y

N

P40=0

Y

N

N

Y

P50=0

Y

Y

N

N

P60=0

Y

N

Y

N

P70=0

Y

N

N

N

P01

N

Y

Y

Y

P11=0

N

N

Y

Y

P21=0

N

Y

N

Y

P31

N

Y

Y

N

P41=0

N

N

N

Y

P51=0

N

Y

N

N

P61=0

N

N

Y

N

P71=0

N

N

N

N

P02=0

Y

Y

Y

Y

P12

Y

N

Y

Y

P22=0

Y

Y

N

Y

P32=0

Y

Y

Y

N

P42=0

Y

N

N

Y

P52=0

Y

Y

N

N

P62

Y

N

Y

N

P72=0

Y

N

N

N

P03=0

Y

Y

Y

Y

P13=0

Y

N

Y

Y

P23

Y

Y

N

Y

P33=0

Y

Y

Y

N

P43=0

Y

N

N

Y

P53

Y

Y

N

N

P63=0

Y

N

Y

N

P73=0

Y

N

N

N

P04=0

N

Y

Y

Y

P14

N

N

Y

Y

P24=0

N

Y

N

Y

P34=0

N

Y

Y

N

P44=0

N

N

N

Y

P54=0

N

Y

N

N

P64

N

N

Y

N

P74=0

N

N

N

N

P05=0

Y

Y

Y

Y

P15=0

Y

N

Y

Y

P25=0

Y

Y

N

Y

P35=0

Y

Y

Y

N

P45

Y

N

N

Y

P55=0

Y

Y

N

N

P65=0

Y

N

Y

N

P75

Y

N

N

N

P06=0

N

Y

Y

Y

P16=0

N

N

Y

Y

P26

N

Y

N

Y

P36=0

N

Y

Y

N

P46=0

N

N

N

Y

P56

N

Y

N

N

P66=0

N

N

Y

N

P76=0

N

N

N

N

P07=0

N

Y

Y

Y

P17=0

N

N

Y

Y

P27=0

N

Y

N

Y

P37=0

N

Y

Y

N

P47

N

N

N

Y

P57=0

N

Y

N

N

P67=0

N

N

Y

N

P77

N

N

N

N

Now we define the transition matrix P
  

P00

P01

P02=0

P03=0

P04=0

P05=0

P06=0

P07=0

P10=0

P11=0

P12

P13=0

P14

P15=0

P16=0

P17=0

P20=0

P21=0

P22=0

P23

P24=0

P25=0

P26

P27=0

P30

P31

P32=0

P33=0

P34=0

P35=0

P36=0

P37=0

P40=0

P41=0

P42=0

P43=0

P44=0

P45

P46=0

P47

P50=0

P51=0

P52=0

P53

P54=0

P55=0

P56

P57=0

P60=0

P61=0

P62

P63=0

P64

P65=0

P66=0

P67=0

P70=0

P71=0

P72=0

P73=0

P74=0

P75

P76=0

P77


In Problem 3 we are given that P00=.8, P75=.2. By examining Table 2, we can see that we are also given that P30=.6, P23=.6, P53=.6, P14=.6, P64=.6, P47=.6, P77=.6. We can put these values into P, and use the fact that each row adds up to 1 to find the rest of the probabilities.
  

.8

.2

0

0

0

0

0

0

0

0

.4

0

.6

0

0

0

0

0

0

.6

0

0

.4

0

.6

.4

0

0

0

0

0

0

0

0

0

0

0

.4

0

.6

0

0

0

.6

0

0

.4

0

0

0

.4

0

.6

0

0

0

0

0

0

0

0

.2

0

.8

Rain (Y/N)

State Day i Day i -1 Day i-2

0

Y

Y

Y

1

N

Y

Y

2

Y

N

Y

3

Y

Y

N

4

N

N

Y

5

Y

N

N

6

N

Y

N

7

N

N

N