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Define "discrete data" to the person sitting next to you. Now define "random" and "variable"
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Transcript of Define "discrete data" to the person sitting next to you. Now define "random" and "variable"
Define "discrete data" to the person sitting next to you.
Now define "random" and "variable"
Which of the following could be described as all three - discrete, random and variable?
The score on a diceThe number of days
in a particular month
The length of a song in the charts
The number of songs on an album
in the chartsThe number of
days it is before it rains again
Now try Q1 and 2 page154
A Probability Distribution
This is a table showing the probabilty of each outcome of an experiment.
Consider Q2 from p154 - A fair die is thrown 4 times and the number of times it scores 6, Y, is noted.
Write down all the possible values for y.Work out the probabilities for each one.
Show this answer in table form.
What should all your probabilities add up to?
Can you think of a formula you can use to summarise these probabilities?
P(X = x) = 4Cx(5/6)4-x (1/6)x x = 1, 2, 3, 4
X 0 1 2 3 4
P(X = x)
Below is the example of a probability distribution
Please complete with your values from earlier
And here is an example of its probabilty function
Do all the values add to 1?If not why not?
Exercise 8AQ 3 - 9
If a particular value of a discrete random variable, X, is x then
P(X ≤ x) is written as F(x)
This is known as a cumulative distribution function and can be written as a function or a
table
Here is the distribution from our previous example with the cumulative distribution for you to add
below it.
X 0 1 2 3 4
P(X = x)F(X)
X 0 1 2 3 4
P(X = x)F(X)
Don't forget this would be
less than 1 if we hadn't rounded to
2 dp
Sometimes the function F(x) is defined in algebraic terms and so this row can be filled in directly from the
function rather than adding the probabilities. You could even be given the function F(x) and work
backwards to find the P(x). The following example shows this.
An example question involving a cumulative distribution function
Exercise 8BQ 1 - 8
The Expected Value of a Discrete Random Variable -
- The MeanConsider the simple random variable - score on a six
sided unbiased die
Write down the probability distribution for this in a table and as a function
Add a row for the cumulative distribution and write down its function F(x)
What do you think will be the mean value of a score on a die. Throw a die 20 times and calculate the mean.
As a class lets combine these scores to work out the mean of ........ throws.
Would you consider this mean to be more or less accurate than your first estimate? Why?
Was the mean value of the score on an unbiased die as you expected?
Recall how you find the mean of a frequency distribution. We could have put our dice scores into
a frequency table. If we had put the whole class scores in it would have looked something like this:
Score Frequency
1
2
3
4
5
6
If we throw the dice
enough times the scores in this column should all be roughly the
same, as the probability of scoring each
value are identical.
fx
That would mean the score in this column look like this
1f
2f
3f
4f
5f
6f
21f6f
Where the value of f is determined by a sixth of the
number of times the dice was thrown
The mean is 21f / 6f
=3.5
As we saw in our experiment
1/6
Mean of a probability distribution
Score P(X = x)
1
2
3
4
5
6
px
1
The mean is 21/6 / 1
=3.5
1/6
1/6
1/6
1/6
1/6
1/6
21/6
6/6
5/6
4/6
3/6
2/6
Realise the sum of this column is always 1 so the mean of a
probability distribution can be calculated by simply adding this
column
The mean of a discrete random
variable or probability
distribution is usually referred to as the "expected
value" and denoted by E(x)
Variance of a probability distribution
1/6
Score P(X = x)
1
2
3
4
5
6
px
1
1/6
1/6
1/6
1/6
1/6
1/6
21/6
6/6
5/6
4/6
3/6
2/6
px2
1/6
36/6
25/6
16/6
9/6
4/6
91/6
91/6 - (21/6)2
211/12
The variance of a discrete random variable or probability distribution can be caluculated by
Var(x) = px2 - [E(x)]2
The text book refers to px2 as E(x2)
An Example Question
An example where you can use the known mean of a distribution to find an unkown
probability - working backwards
Exercises 8C Q 3 - 7
andExercise 8D
Q 2 - 6
Consider a game now that uses two fair cubical dice and requires the total of their scores plus an
additional 10.
What would the mean score be?
Could you write that using the notation we have learned in this chapter?
How could you write the Variance of this score?
What would it be?
Now it gets even more complicated
12/36
Score P(Y = y)
12
13
14
15
16
17
py
1
1/36
2/36
3/36
4/36
5/36
6/36
17
102/36
80/36
60/36
42/36
26/36
py2
144/36
1734/36
1280/36
900/36
588/36
338/36
10614/36
18
19
20
21
22
5/36
4/36
3/36
2/36
1/3622/36
42/36
60/36
76/36
90/36
484/36
882/36
1200/36
1444/36
1620/36
Var(Y) =
1769/6 - (17)2
55/6
Let Y = X + W + 10where w is that a different die is
thrown. This is the same distribution as x but needs to be considered
separately as it is a separate throw
E(x+w+10) = 17
Var(x+w+10) = 55/6
Xwhere x is the distribution shown earlier of throwing a
fair cubical die
E(x) = 3.5
Var(x) = 211/12
Compare the two distributions
Consider a game, Z, now that doubles a die score then adds another 10.
What would the mean score be?
Could you write that using the notation we have learned in this chapter?
How could you write the Variance of this score?
What would it be?
Score P(Z = z)
12
14
16
pz
1
1/6
17
pz2
3002/3
18
20
22
12/6144/6
1/6
1/6
1/6
1/6
1/6
22/6
20/6
18/6
16/6
14/6
484/6
400/6
324/6
256/6
196/6
Var(Z) =
902/3 - (17)2
112/3
Let Z = 2X + 10
E(2x+10) = 17
Var(2x+10) = 112/3
Xwhere x is the distribution shown earlier of throwing a
fair cubical die
E(x) = 3.5
Var(x) = 211/12
Compare the two distributions
In Summary
Now do read through the examples in your text book and do Exercise 8E
Discrete Uniform Distributions
Our example with a fair cubical dice is also an example of a Uniform Distribution
This is because the probabilities of scoring each number are equal - one sixth
The formulae:E(X) = (n+1)/2
andVar(X) = (n+1)(n-1)/12
are perhaps more useful ways of calculating in these instances
Now try Exercise 8F to practice these types of question