Chapter 12: Expected Values of Functions of Discrete Random Variables; Variance of Discrete Random Variables

Slides:



Advertisements
Similar presentations
Let X 1, X 2,..., X n be a set of independent random variables having a common distribution, and let E[ X i ] = . then, with probability 1 Strong law.
Advertisements

1 Continuous random variables Continuous random variable Let X be such a random variable Takes on values in the real space  (-infinity; +infinity)  (lower.
Joint Probability Distributions and Random Samples
Random Variables Probability Continued Chapter 7.
Chapter 4. Probability: The Study of Randomness
1 Def: Let and be random variables of the discrete type with the joint p.m.f. on the space S. (1) is called the mean of (2) is called the variance of (3)
Statistics Lecture 18. Will begin Chapter 5 today.
Probability Theory Part 2: Random Variables. Random Variables  The Notion of a Random Variable The outcome is not always a number Assign a numerical.
Review.
Continuous Random Variables and Probability Distributions
Assignment 2 Chapter 2: Problems  Due: March 1, 2004 Exam 1 April 1, 2004 – 6:30-8:30 PM Exam 2 May 13, 2004 – 6:30-8:30 PM Makeup.
Random Variable (RV) A function that assigns a numerical value to each outcome of an experiment. Notation: X, Y, Z, etc Observed values: x, y, z, etc.
1 1 Slide © 2006 Thomson/South-Western Chapter 5 Discrete Probability Distributions n Random Variables n Discrete Probability Distributions.
QBM117 Business Statistics
Probability theory 2011 Outline of lecture 7 The Poisson process  Definitions  Restarted Poisson processes  Conditioning in Poisson processes  Thinning.
A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.
The moment generating function of random variable X is given by Moment generating function.
Continuous Random Variables and Probability Distributions
CHAPTER 17 Ted Shi, Kevin Yen Betters, 1st PROBABILITY MODELS.
Chapter 5 Several Discrete Distributions General Objectives: Discrete random variables are used in many practical applications. These random variables.
Sampling Distributions  A statistic is random in value … it changes from sample to sample.  The probability distribution of a statistic is called a sampling.
1 1 Slide © 2003 South-Western/Thomson Learning TM Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
1 1 Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University © 2002 South-Western/Thomson Learning.
Chapter 5 Some Discrete Probability Distributions.
Section 06. The Bernoulli distribution is a special case where n=1!
4.2 Variances of random variables. A useful further characteristic to consider is the degree of dispersion in the distribution, i.e. the spread of the.
Variance and Standard Deviation  The variance of a discrete random variable is:  The standard deviation is the square root of the variance.
Continuous Distributions The Uniform distribution from a to b.
1 Chapter 16 Random Variables. 2 Expected Value: Center A random variable assumes a value based on the outcome of a random event.  We use a capital letter,
Chapter 5: Random Variables and Discrete Probability Distributions
The Mean of a Discrete RV The mean of a RV is the average value the RV takes over the long-run. –The mean of a RV is analogous to the mean of a large population.
BIA 2610 – Statistical Methods Chapter 5 – Discrete Probability Distributions.
Probability and Statistics Dr. Saeid Moloudzadeh Random Variables/ Distribution Functions/ Discrete Random Variables. 1 Contents Descriptive.
Chapter 7: Random Variables and Probability Distributions.
1 Poisson Probability Models The Poisson experiment typically models situations where rare events occur over a fixed amount of time or within a specified.
Slide 16-1 Copyright © 2004 Pearson Education, Inc.
Expectation. Let X denote a discrete random variable with probability function p(x) (probability density function f(x) if X is continuous) then the expected.
Math 4030 – 6a Joint Distributions (Discrete)
Probability Distributions, Discrete Random Variables
Chapter 3 Discrete Random Variables and Probability Distributions  Random Variables.2 - Probability Distributions for Discrete Random Variables.3.
MATH 256 Probability and Random Processes Yrd. Doç. Dr. Didem Kivanc Tureli 14/10/2011Lecture 3 OKAN UNIVERSITY.
C ONDITIONAL P ROBABILITY AND C ONDITIONAL E XPECTATION Name/ Taha Ben Omar ID/
Central Limit Theorem Let X 1, X 2, …, X n be n independent, identically distributed random variables with mean  and standard deviation . For large n:
Engineering Probability and Statistics - SE-205 -Chap 3 By S. O. Duffuaa.
Introduction to the Practice of Statistics Fifth Edition Chapter 5: Sampling Distributions Copyright © 2005 by W. H. Freeman and Company David S. Moore.
Chapter 7 Lesson 7.4a Random Variables and Probability Distributions 7.4: Mean and Standard Deviation of a Random Variable.
Random Variables By: 1.
Week 61 Poisson Processes Model for times of occurrences (“arrivals”) of rare phenomena where λ – average number of arrivals per time period. X – number.
CHAPTER 6 Random Variables
Statistics Lecture 19.
Engineering Probability and Statistics - SE-205 -Chap 3
Chapter 16 Random Variables.
Example: X = Cholesterol level (mg/dL)
In-Class Exercise: Discrete Distributions
Chapter 3 Discrete Random Variables and Probability Distributions
Random Variable X, with pmf p(x) or pdf f(x)
ASV Chapters 1 - Sample Spaces and Probabilities
ASV Chapters 1 - Sample Spaces and Probabilities
Random WALK, BROWNIAN MOTION and SDEs
Chapter 4 Discrete Probability Distributions.
Handout Ch 4 實習.
6.3 Sampling Distributions
Part II: Discrete Random Variables
6: Binomial Probability Distributions
Discrete Random Variables: Basics
Discrete Random Variables: Basics
Berlin Chen Department of Computer Science & Information Engineering
Continuous Distributions
Discrete Random Variables: Basics
Presentation transcript:

Chapter 12: Expected Values of Functions of Discrete Random Variables; Variance of Discrete Random Variables 1

Example: Expected Value of a Function Consider a situation where a discrete random variable can have the values from -2 to 2. (Like a person can take a seat up to 2 places to the left of center (-2) and 2 places to the right of center (+2).) What is E (X 2 )? 2

Example: (2 nd time) An individual who has automobile insurance form a certain company is randomly selected. Let X be the number of moving violations for which the individual was cited during the last 3 years. The PMF of X is a)If the cost of insurance depends on the following function of accidents, g(x) = (100x - 15), what is the expected value of the cost of the insurance? b)What is the expected value of X 2 ? x0123 p X (x)

Example: Variance A school class of 120 students are driven in 3 buses to a basketball game. There are 36 students in one of the buses, 40 students in another, and 44 on the third bus. When the buses arrive, one of the 120 students is randomly chosen. Let X denote the number of students on the bus of that randomly chosen student. Find the variance of X. 4

Example: Properties of Variance An individual who has automobile insurance form a certain company is randomly selected. Let X be the number of moving violations for which the individual was cited during the last 3 years. The PMF of X is a) If the cost of insurance depends on the following function of accidents, g(x) = (100x- 15), what is the variance of the cost of insurance? X0123 p X (x)

Example: Properties of Variance (cont) An individual who has automobile insurance form a certain company is randomly selected. Let X be the number of moving violations for which the individual was cited during the last 3 years. The PMF of X is b) If the cost of insurance depends on the following function of accidents, g(x) = (100x- 15), what is the variance of the cost of insurance for 3 people who are independent? X0123 p X (x)