 # Class notes for ISE 201 San Jose State University

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Probability & Statistics for Engineers & Scientists, by Walpole, Myers, Myers & Ye ~ Chapter 4 Notes
Class notes for ISE 201 San Jose State University Industrial & Systems Engineering Dept. Steve Kennedy 1

Mean of a Set of Observations
Suppose an experiment involves tossing 2 coins. The result is either 0, 1, or 2 heads. Suppose the experiment is repeated 15 times, and suppose that 0 heads is observed 3 times, 1 head 8 times, and 2 heads 4 times. What is the average number of heads flipped? x bar = ( ) / 15 = ((0)(3) + (1)*(8) + (2)*(4)) / 15 = 1.07 This could also be written as a weighted average, x bar = (0)(3/15) + (1)(8/15) + (2)(4/15) = where 3/15, 8/15, etc. are the fraction of times the given number of heads came up. The average is also called the mean.

Mean of a Random Variable
A similar technique, taking the probability of an outcome times the value of the random variable for that outcome, is used to calculate the mean of a random variable. The mean or expected value  of a random variable X with probability distribution f (x), is  = E (X) = x x f(x) if discrete, or  = E (X) = x x f(x) dx if continuous

Mean of a Random Variable Depending on X
If X is a random variable with distribution f(x). The mean g(X) of the random variable g(X) is g(X) = E [g(X)] = x g(x) f(x) if discrete, or g(X) = E [g(X)] = x g(x) f(x) dx if continuous

Expected Value for a Joint Distribution
If X and Y are random variables with joint probability distribution f (x,y). The mean or expected value g(X,Y) of the random variable g (X,Y) is g(X,Y) = E [g(X,Y)] = x y g(x,y) f(x,y) if discrete, or g(X,Y) = E [g(X,Y)] = x y g(x,y) f(x,y) dy dx if continuous Note that the mean of a distribution is a single value, so it doesn't make sense to talk of the mean the distribution f (x,y).

Variance What was the variance of a set of observations?
The variance 2 of a random variable X with distribution f(x) is 2 = E [(X - )2] = x (x - )2 f(x) if discrete, or 2 = E [(X - )2] = x (x - )2 f(x) dx if continuous An equivalent and easier computational formula, also easy to remember, is 2 = E [X2] - E [X]2 = E [X2] - 2 "The expected value of X2 - the expected value of X...squared." Derivation from the previous formula is simple.

Variance of a Sample There's also a somewhat similar, better computational formula for s2. What is s2? What was the original formula for the variance of a sample? The formula is

Covariance If X and Y are random variables with joint probability distribution f (x,y), the covariance, XY , of X and Y is defined as XY = E [(X - X)(Y - Y)] The better computational formula for covariance is XY = E (XY) - X Y Note that although the standard deviation  can't be negative, the covariance XY can be negative. Covariance will be useful later when looking at the linear relationship between two random variables.

Correlation Coefficient
If X and Y are random variables with covariance XY and standard deviations X and Y respectively, the correlation coefficient XY is defined as XY = XY / ( X Y ) Correlation coefficient notes: What are the units of XY ? What is the possible range of XY ? What is the meaning of the correlation coefficient? If XY = 1 or -1, then there is an exact linear relationship between Y and X (i.e., Y = a + bX). If XY = 1, then b > 0, and if XY = -1, then b < 0. Can show this by calculating the covariance of X and a + bX, which simplifies to b / b2 = 1.

Linear Combinations of Random Variables
If a and b are constants, E (aX + b) = a E(X) + b Also holds if a = 0 or b = 0. If we add two functions, E [g(X)  h(X)] = E [g(X)]  E [h(X)] Also true for functions of two or more random variables. That is, E [g(X,Y)  h(X,Y)] = E [g(X,Y)]  E [h(X,Y)]

Functions of Two or More Random Variables
The expected value of the sum of two random variables is equal to the sum of the expected values. E (X  Y) = E(X)  E(Y) The expected value of the product of two independent random variables is equal to the product of the expected values. E (X Y) = E(X) E(Y)

Variance Relationships
For a random variable X with variance 2 2aX + b = a2 X2 So adding a constant does what? And multiplying by a constant does what? For two random variables X and Y, 2aX + bY = a2 X2 + b2 Y abXY What if X and Y are independent? XY = 0. Note that the correlation coefficient is also 0.

Chebyshev's Inequality
The probability that any random variable X will assume a value within k standard deviations of the mean is at least /k2. That is P ( - k < X <  + k)  /k2 This theorem is both very general and very weak. Very general, since it holds for any probability distribution. Very weak for the same reason, because it is a worst-case limit that holds for any distribution. If we know the distribution, we can get a better limit than this (how?), so this is only used when the distribution is unknown. Care must be taken, however, not to assume an underlying distribution when the distribution is really unknown.