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Lecture (10) Mathematical Expectation. The expected value of a variable is the value of a descriptor when averaged over a large number theoretically infinite.

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Presentation on theme: "Lecture (10) Mathematical Expectation. The expected value of a variable is the value of a descriptor when averaged over a large number theoretically infinite."— Presentation transcript:

1 Lecture (10) Mathematical Expectation

2 The expected value of a variable is the value of a descriptor when averaged over a large number theoretically infinite.

3 Mathematical Expectation (cont.) Another way to compute the variance

4 Example 1 TTTHHTHH 012 Sample SpaceNumber of Heads

5 Example 1 (cont.)

6 NUMBER OF HEADS P R O B A B I L I T Y Experiment: Toss Two Coins

7 Example 1 (cont) E.G. Toss 2 coins, count heads, compute expected value:  = 0   .25 = 1 E.G. Toss 2 coins, count heads, compute variance: variance = (0 - 1) 2 (.25) + (1 - 1) 2 (.50) + (2 - 1) 2 (.25) =.50

8 Example 2

9 Find the mean of the number of spots that appear when a die is tossed. The probability distribution is given below. Discrete Uniform Distribution Example

10 That is, when a die is tossed many times, the theoretical mean will be 3.5. That is, when a die is tossed many times, the theoretical mean will be 3.5. Discrete Uniform Distribution Example (cont.)

11 Binomial Distribution - Binomial Distribution - Example A coin is tossed four times. Find the mean, variance and standard deviation of the number of heads that will be obtained. Solution:Solution: n = 4, p = 1/2 and q = 1/2.  = n  p = (4)(1/2) = 2.  2 = n  p  q = (4)(1/2)(1/2) = 1.  = = 1.

12 Poisson Distribution

13 Uniform Distribution Example

14 If the probability density function has the form f(x) = ax for a random variable X between 0 and 2. (a)Find the value of a. (b) Find the median of X (c)Find P(1.0 < X < 2.0) Solution: (a) From the area under the whole density curve is 1, then we have Example

15 Quiz

16 Exponential Distribution

17 DistributionNormal x LogN Y =logx Gamma x Exp t Mean xx yy nk1/k Variance xx yy nk  1/k 2 Skewnesszero 2/n Comparison of Parameters of Dist’n


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