NIPRL Chapter 2. Random Variables 2.1 Discrete Random Variables 2.2 Continuous Random Variables 2.3 The Expectation of a Random Variable 2.4 The Variance of a Random Variable 2.5 Jointly Distributed Random Variables 2.6 Combinations and Functions of Random Variables

NIPRL 2.1 Discrete Random Variable Definition of a Random Variable (1/2) Random variable –A numerical value to each outcome of a particular experiment

NIPRL Definition of a Random Variable (2/2) Example 1 : Machine Breakdowns –Sample space : –Each of these failures may be associated with a repair cost –State space : –Cost is a random variable : 50, 200, and 350

NIPRL Probability Mass Function (1/2) Probability Mass Function (p.m.f.) –A set of probability value assigned to each of the values taken by the discrete random variable – and –Probability :

NIPRL Probability Mass Function (1/2) Example 1 : Machine Breakdowns –P (cost=50)=0.3, P (cost=200)=0.2, P (cost=350)=0.5 – =

NIPRL Cumulative Distribution Function (1/2) Cumulative Distribution Function –Function : –Abbreviation : c.d.f

NIPRL Cumulative Distribution Function (2/2) Example 1 : Machine Breakdowns

NIPRL 2.2 Continuous Random Variables Example of Continuous Random Variables (1/1) Example 14 : Metal Cylinder Production –Suppose that the random variable is the diameter of a randomly chosen cylinder manufactured by the company. Since this random variable can take any value between 49.5 and 50.5, it is a continuous random variable.

NIPRL Probability Density Function (1/4) Probability Density Function (p.d.f.) –Probabilistic properties of a continuous random variable

NIPRL Probability Density Function (2/4) Example 14 –Suppose that the diameter of a metal cylinder has a p.d.f

NIPRL Probability Density Function (3/4) This is a valid p.d.f.

NIPRL Probability Density Function (4/4) The probability that a metal cylinder has a diameter between 49.8 and 50.1 mm can be calculated to be

NIPRL Cumulative Distribution Function (1/3) Cumulative Distribution Function

NIPRL Probability Density Function (2/3) Example 14

NIPRL Probability Density Function (3/3)

NIPRL 2.3 The Expectation of a Random Variable Expectations of Discrete Random Variables (1/2) Expectation of a discrete random variable with p.m.f Expectation of a continuous random variable with p.d.f f(x) The expected value of a random variable is also called the mean of the random variable

NIPRL Expectations of Discrete Random Variables (2/2) Example 1 (discrete random variable) –The expected repair cost is

NIPRL Expectations of Continuous Random Variables (1/2) Example 14 (continuous random variable) –The expected diameter of a metal cylinder is –Change of variable: y=x-50

NIPRL Expectations of Continuous Random Variables (2/2) Symmetric Random Variables –If has a p.d.f that is symmetric about a point so that –Then, (why?) –So that the expectation of the random variable is equal to the point of symmetry

NIPRL

2.3.3 Medians of Random Variables (1/2) Median –Information about the “middle” value of the random variable Symmetric Random Variable –If a continuous random variable is symmetric about a point, then both the median and the expectation of the random variable are equal to

NIPRL Medians of Random Variables (2/2) Example 14

NIPRL 2.4 The variance of a Random Variable Definition and Interpretation of Variance (1/2) Variance( ) –A positive quantity that measures the spread of the distribution of the random variable about its mean value –Larger values of the variance indicate that the distribution is more spread out –Definition: Standard Deviation –The positive square root of the variance –Denoted by

NIPRL Definition and Interpretation of Variance (2/2) Two distribution with identical mean values but different variances

NIPRL Examples of Variance Calculations (1/1) Example 1

NIPRL Chebyshev’s Inequality –If a random variable has a mean and a variance, then –For example, taking gives Chebyshev’s Inequality (1/1)

NIPRL Proof

NIPRL Quantiles of Random Variables (1/2) Quantiles of Random variables –The th quantile of a random variable X –A probability of that the random variable takes a value less than the th quantile Upper quartile –The 75th percentile of the distribution Lower quartile –The 25th percentile of the distribution Interquartile range –The distance between the two quartiles

NIPRL Quantiles of Random Variables (2/2) Example 14 –Upper quartile : –Lower quartile : –Interquartile range :

NIPRL 2.5 Jointly Distributed Random Variables Jointly Distributed Random Variables (1/4) Joint Probability Distributions –Discrete –Continuous

NIPRL Jointly Distributed Random Variables (2/4) Joint Cumulative Distribution Function –Discrete –Continuous

NIPRL Jointly Distributed Random Variables (3/4) Example 19 : Air Conditioner Maintenance –A company that services air conditioner units in residences and office blocks is interested in how to schedule its technicians in the most efficient manner –The random variable X, taking the values 1,2,3 and 4, is the service time in hours –The random variable Y, taking the values 1,2 and 3, is the number of air conditioner units

NIPRL Jointly Distributed Random Variables (4/4) Joint p.m.f Joint cumulative distribution function Y= number of units X=service time

NIPRL Marginal Probability Distributions (1/2) Marginal probability distribution –Obtained by summing or integrating the joint probability distribution over the values of the other random variable –Discrete –Continuous

NIPRL Marginal Probability Distributions (2/2) Example 19 –Marginal p.m.f of X –Marginal p.m.f of Y

NIPRL Example 20: (a jointly continuous case) Joint pdf: Marginal pdf’s of X and Y:

NIPRL Conditional Probability Distributions (1/2) Conditional probability distributions –The probabilistic properties of the random variable X under the knowledge provided by the value of Y –Discrete –Continuous –The conditional probability distribution is a probability distribution.

NIPRL Conditional Probability Distributions (2/2) Example 19 –Marginal probability distribution of Y –Conditional distribution of X

NIPRL Independence and Covariance (1/5) Two random variables X and Y are said to be independent if –Discrete –Continuous –How is this independency different from the independence among events?

NIPRL Independence and Covariance (2/5) Covariance –May take any positive or negative numbers. –Independent random variables have a covariance of zero –What if the covariance is zero?

NIPRL Independence and Covariance (3/5) Example 19 (Air conditioner maintenance)

NIPRL Independence and Covariance (4/5) Correlation: –Values between -1 and 1, and independent random variables have a correlation of zero

NIPRL Independence and Covariance (5/5) Example 19: (Air conditioner maintenance)

NIPRL What if random variable X and Y have linear relationship, that is, where That is, Corr(X,Y)=1 if a>0; -1 if a<0.

NIPRL 2.6 Combinations and Functions of Random Variables Linear Functions of Random Variables (1/4) Linear Functions of a Random Variable –If X is a random variable and for some numbers then and Standardization -If a random variable has an expectation of and a variance of, has an expectation of zero and a variance of one.

NIPRL Linear Functions of Random Variables (2/4) Example 21:Test Score Standardization –Suppose that the raw score X from a particular testing procedure are distributed between -5 and 20 with an expected value of 10 and a variance 7. In order to standardize the scores so that they lie between 0 and 100, the linear transformation is applied to the scores.

NIPRL Linear Functions of Random Variables (3/4) –For example, x=12 corresponds to a standardized score of y=(4 ⅹ 12)+20=68

NIPRL Linear Functions of Random Variables (4/4) Sums of Random Variables –If and are two random variables, then and –If and are independent, so that then

NIPRL Properties of

NIPRL Linear Combinations of Random Variables (1/5) Linear Combinations of Random Variables –If is a sequence of random variables and and are constants, then –If, in addition, the random variables are independent, then

NIPRL Linear Combinations of Random Variables (2/5) Averaging Independent Random Variables –Suppose that is a sequence of independent random variables with an expectation and a variance. –Let –Then and –What happened to the variance?

NIPRL Linear Combinations of Random Variables (3/5)

NIPRL Linear Combinations of Random Variables (4/5) Example 21 –The standardized scores of the two tests are –The final score is

NIPRL Linear Combinations of Random Variables (5/5) –The expected value of the final score is –The variance of the final score is

NIPRL Nonlinear Functions of a Random Variable (1/3) Nonlinear function of a Random Variable –A nonlinear function of a random variable X is another random variable Y=g(X) for some nonlinear function g. –There are no general results that relate the expectation and variance of the random variable Y to the expectation and variance of the random variable X

NIPRL Nonlinear Functions of a Random Variable (2/3) –For example, –Consider –What is the pdf of Y? 01x f(x)=1 E(x)= y f(y)=1/y E(y)=1.718

NIPRL Nonlinear Functions of a Random Variable (3/3) CDF methd –The p.d.f of Y is obtained by differentiation to be Notice that

NIPRL Determining the p.d.f. of nonlinear relationship between r.v.s: Given and,what is ? If are all its real roots, that is, then where

NIPRL Example: determine -> one root is possible in