Presentation is loading. Please wait.

Presentation is loading. Please wait.

Chapter 5 Discrete Probability Distributions

Similar presentations


Presentation on theme: "Chapter 5 Discrete Probability Distributions"— Presentation transcript:

1 Chapter 5 Discrete Probability Distributions
5.3 EXPECTATION The Mean and Expectation (Expected Value) Some Applications 5.4 VARIANCE AND STANDARD DEVIATION

2 5.3 EXPECTATION 5.3.1 The Mean and Expectation (Expected Value)
Experimental approach Suppose we throw an unbiased die 120 times and record the results: Then we can calculate the mean score obtained where Scroe, x 1 2 3 4 5 6 Frequency, f 15 22 23 19 18 = = = ________ (3 d.p.)

3 Theoretical approach The probability distribution for the random variable X where X is ‘the number on the die’ is as shown: We can obtain a value for the ‘expected mean’ by multiplying each score by its corresponding probability and summing, so that Expected mean = = Score, x 1 2 3 4 5 6 P(X = x) 1/6

4 If we have a statistical experiment:
a practical approach results in a frequency distribution and a mean value, a theoretical approach results in a probability distribution and an expected value. The expectation of X (or expected value), written E(X) is given by E(X) =

5 Example 1 random variable X has a probability function defined as shown. Find E(X). -2 -1 1 2 P(X= x) 0.3 0.1 0.15 0.4 0.05

6 In general, if g(X) is any function of the discrete random variable X then
E[g(X)] =

7 Example In a game a turn consists of a tetrahedral die being thrown three times. The faces on the die are marked 1,2,3,4 and the number on which the die falls is noted. A man wins $ whenever x fours occur in a turn. Find his average win per turn.

8 Example The random variable X has probability function P(X = x) for x = 1,2,3. Calculate (a) E(3), (b) E(X), (c) E(5X), (d) E(5X+3), (e) 5E(X) + 3, (f) E(X2), (g) E(4X2- 3), (h) 4E(X2 ) – 3. Comment on your answers to parts (d) and (e) and parts (g) and (h). x 1 2 3 P(X = x) 0.1 0.6 0.3

9 E(a X + b) = a E(X) + b, where a and b are any constants.
E[f1(X)  f2(X)] = E[f1(X)]  E[f2(X)], where f1 and f2 are functions of X.

10 Some Applications

11 5.4 VARIANCE AND STANDARD DEVIATION
The variance of X, written Var(X), is given by Var(X) = E(X - )2


Download ppt "Chapter 5 Discrete Probability Distributions"

Similar presentations


Ads by Google