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DISCRETE RANDOM VARIABLES Monday 25 th February 2013 Learning objectives: To understand probability distributions for discrete random variables. To be.

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Presentation on theme: "DISCRETE RANDOM VARIABLES Monday 25 th February 2013 Learning objectives: To understand probability distributions for discrete random variables. To be."— Presentation transcript:

1 DISCRETE RANDOM VARIABLES Monday 25 th February 2013 Learning objectives: To understand probability distributions for discrete random variables. To be able to find the mean (expected value), variance and standard deviation of discrete random variables.

2 Discrete random variable A discrete random variable is one which may take on only a countable number of distinct values such as 0, 1, 2, 3, 4,... Examples of discrete random variables include the number of children in a family, the Friday night attendance at a cinema, the number of patients in a doctor's surgery, the number of defective light bulbs in a box of ten.

3 Probability distributions X represents the score when a dice is rolled. X is a discrete random variable. Score on dice, x P(X=x) Σ P(X=x) = 1 If X is a discrete random variable with probability function P(X=x) or p(x) then:

4 Cumulative distribution functions Score on dice, x P(X

5 Mean of X or expected value of X Score on dice, x P(X=x) μ = E(X) = Σ xP(X=x) If X is a discrete random variable with probability function P(X=x) or p(x) then the expected value of X:

6 Variance and standard deviation σ 2 = Var(X) = Σ (x – μ) 2 P(X=x) = Σ x 2 P(X=x) – μ 2 The variance of a distribution is the mean of the sum of the squared deviations from the mean. The standard deviation, σ, of a distribution is the positive square root of the variance. σ 2 = Var(x) = E(X 2 ) – μ 2

7 Variance and standard deviation Score on dice, X X2X2X2X2 P(X=x) σ 2 = Var(x) = E(X 2 ) – μ 2

8 Listing possibilities: have a system f c fffc cf cc fffffc fcf fcc cff cfc ccf ccc fffffffc ffcf ffcc fcff fcfc fccf fccc cfff cffc cfcf cfcc ccff ccfc cccf cccc 1 stick 2 sticks 3 sticks

9 Working out probabilities: f c fffc cf cc fffffc fcf fcc cff cfc ccf ccc fffffffc ffcf ffcc fcff fcfc fccf fccc cfff cffc cfcf cfcc ccff ccfc cccf cccc 1 stick 2 sticks 3 sticks Each stick falls independently, f=0.7c=0.3 1 x P(f,f,f,f) = 0.7 x 0.7 x 0.7 x 0.7= = x P(f,f,f,c)= x 0.3 x 4= 6 x P(f,f,c,c)=0.7 2 x x 6 = 4 x P(f,c,c,c) = 0.7 x x 4 = 1 x P(c,c,c,c) = =

10 Relative frequency The score is an example of discrete random variable. –Let S stand for score. Capital letters are used for random variables. –P(S=3) means ‘the probability that S=3 –P(S=3) = Number of flat sides up Score51234 s.12345P(S=s) Probability function Note s is used for individual values of the random variable S

11 P(X=x) as a stick/bar graph

12 TASK Exercise A – Page 53 & 54 Questions: 1, 2, 3, 5 & 6 Do rest at home.

13 Mean, variance and standard deviation If I were to throw times, I could work out the mean like the below. Multiply each of my probabilities by and then divide by s P(S=s) Probability function

14 Mean, variance and standard deviation However, multiplying and dividing by both top and bottom seems unnecessary and it is s P(S=s) Probability function

15 MEAN of: Discrete random Variables Mean S =Σs x P(S=s) The mean of a random variable is usually denoted by μ (‘mu’) Task B1, B2

16 VARIANCE Mean = 0x x x x x0.1=1.9 x01234 P(X=x) Probability function x x-μ (x-μ) 2 P(X=x) (x-μ) 2 x P(X=x)

17 Variance & Standard deviation x x-μ (x-μ) 2 P(X=x) (x-μ) 2 x P(X=x) Variance = σ Standard deviation = σ 1.22 The standard deviation or random variables is normally denoted as σ

18 TASK Page 56 question 2 Homework – test yourself


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