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.

Slides:



Advertisements
Similar presentations
Discrete random variables
Advertisements

Random Variables.  A random variable assumes a value based on the outcome of a random event. ◦ We use a capital letter, like X, to denote a random variable.
Random Variables. Definitions A random variable is a variable whose value is a numerical outcome of a random phenomenon,. A discrete random variable X.
Chapter 7 Discrete Distributions. Random Variable - A numerical variable whose value depends on the outcome of a chance experiment.
Chapter 5.1 & 5.2: Random Variables and Probability Mass Functions
Chapter 2 Discrete Random Variables
1 Discrete Probability Distributions. 2 Random Variable Random experiment is an experiment with random outcome. Random variable is a variable related.
Random Variables.
Lec 18 Nov 12 Probability – definitions and simulation.
Probability Distributions
Problem 1 The following facts are known regarding two events A and B: Pr(A∩B) = 0.2,Pr(AUB) = 0.6,Pr(A | B) = 0.5 Find the following: (i)Pr (A) (ii)Pr.
A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.
Discrete Probability Distributions
Eighth lecture Random Variables.
Chapter 5 Discrete Random Variables and Probability Distributions
CHAPTER 6: RANDOM VARIABLES AND EXPECTATION
Warm-up The mean grade on a standardized test is 88 with a standard deviation of 3.4. If the test scores are normally distributed, what is the probability.
Random Variables A random variable A variable (usually x ) that has a single numerical value (determined by chance) for each outcome of an experiment A.
Stat 1510: Introducing Probability. Agenda 2  The Idea of Probability  Probability Models  Probability Rules  Finite and Discrete Probability Models.
Week71 Discrete Random Variables A random variable (r.v.) assigns a numerical value to the outcomes in the sample space of a random phenomenon. A discrete.
Chapter 1 Probability and Distributions Math 6203 Fall 2009 Instructor: Ayona Chatterjee.
Chapter 4 Probability Distributions
Unit 5 Section : Mean, Variance, Standard Deviation, and Expectation  Determining the mean, standard deviation, and variance of a probability.
Free Powerpoint Templates ROHANA BINTI ABDUL HAMID INSTITUT E FOR ENGINEERING MATHEMATICS (IMK) UNIVERSITI MALAYSIA PERLIS MADAM ROHANA BINTI ABDUL HAMID.
Random Variables A random variable is simply a real-valued function defined on the sample space of an experiment. Example. Three fair coins are flipped.
1 Lecture 7: Discrete Random Variables and their Distributions Devore, Ch
ENGG 2040C: Probability Models and Applications Andrej Bogdanov Spring Random variables part one.
Random Variables. A random variable X is a real valued function defined on the sample space, X : S  R. The set { s  S : X ( s )  [ a, b ] is an event}.
 5-1 Introduction  5-2 Probability Distributions  5-3 Mean, Variance, and Expectation  5-4 The Binomial Distribution.
2.1 Introduction In an experiment of chance, outcomes occur randomly. We often summarize the outcome from a random experiment by a simple number. Definition.
Chapter 5 Discrete Probability Distributions. Introduction Many decisions in real-life situations are made by assigning probabilities to all possible.
EQT 272 PROBABILITY AND STATISTICS
Distributions.ppt - © Aki Taanila1 Discrete Probability Distributions.
Discrete Distributions. Random Variable - A numerical variable whose value depends on the outcome of a chance experiment.
Copyright © 2006 The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Review of Statistics I: Probability and Probability Distributions.
President UniversityErwin SitompulPBST 3/1 Dr.-Ing. Erwin Sitompul President University Lecture 3 Probability and Statistics
AP STATISTICS Section 7.1 Random Variables. Objective: To be able to recognize discrete and continuous random variables and calculate probabilities using.
Chapter 5 Discrete Random Variables Probability Distributions
1 Keep Life Simple! We live and work and dream, Each has his little scheme, Sometimes we laugh; sometimes we cry, And thus the days go by.
Random Variables. Numerical Outcomes Consider associating a numerical value with each sample point in a sample space. (1,1) (1,2) (1,3) (1,4) (1,5) (1,6)
EQT 272 PROBABILITY AND STATISTICS
MATH 256 Probability and Random Processes Yrd. Doç. Dr. Didem Kivanc Tureli 14/10/2011Lecture 3 OKAN UNIVERSITY.
Review Know properties of Random Variables
The Mean of a Discrete Random Variable Lesson
MA305 Mean, Variance Binomial Distribution By: Prof. Nutan Patel Asst. Professor in Mathematics IT-NU A-203 patelnutan.wordpress.com MA305 Mathematics.
Chapter 12: Expected Values of Functions of Discrete Random Variables; Variance of Discrete Random Variables
PROBABILITY DISTRIBUTIONS DISCRETE RANDOM VARIABLES OUTCOMES & EVENTS Mrs. Aldous & Mr. Thauvette IB DP SL Mathematics.
1 Chapter 4 Mathematical Expectation  4.1 Mean of Random Variables  4.2 Variance and Covariance  4.3 Means and Variances of Linear Combinations of Random.
Random Variables Lecture Lecturer : FATEN AL-HUSSAIN.
Week 5 Discrete Random Variables and Probability Distributions Statistics for Social Sciences.
Random Variables By: 1.
APPENDIX A: A REVIEW OF SOME STATISTICAL CONCEPTS
Random Variable 2013.
Random variables (r.v.) Random variable
Discrete and Continuous Random Variables
CHAPTER 2 RANDOM VARIABLES.
5.2 Mean, Variance, Standard Deviation, and Expectation
Discrete Probability Distributions
Chapter. 5_Probability Distributions
Chapter 16.
Discrete Distributions
Discrete Distributions
I flip a coin two times. What is the sample space?
RANDOM VARIABLES, EXPECTATIONS, VARIANCES ETC.
Discrete Distributions.
M248: Analyzing data Block A UNIT A3 Modeling Variation.
Discrete Distributions
Experiments, Outcomes, Events and Random Variables: A Revisit
Random Variables and Probability Distributions
Presentation transcript:

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

Example 1 Two fair coins tossed. Let X = No of Heads OutcomesProbabilityValue of X HH¼2 HT¼1 TH¼1 TT¼0

Random Variable Discrete RV – finite or countable number of values Continuous RV – taking values in an interval

Probability Distribution Probability distribution of a discrete RV described by what is known as a Probability Mass Function (PMF). Probability distribution of a continuous RV described by what is known as a Probability Density Function (PDF).

Probability Mass Function (PMF) p(x) = Pr (X = x) satisfying p(x) ≥ 0 for all x ∑p(x) = 1

Example 1 (Contd) X = No of Heads. The PMF of X is: xPr (X = x) 01/4 12/4=1/2 21/4

Example 1 (Contd)

Probability Density Function (PDF) f(x) satisfying f(x) ≥ 0 for all x ∫f(x) dx = 1 ∫ a b f(x) dx = Pr (a < X < b) Pr (X = a) = 0

Example 2

Example 3

Expectation E (X) = ∑x p (x) for a Discrete RV E (X) = ∫x f (x) dx for a Continuous RV

Expectation E (X 2 )= ∑x 2 p (x) for a Discrete RV E (X 2 ) = ∫x 2 f (x) dx for a Continuous RV

Expectation E (g(X)) = ∑g(x) p (x) for a Discrete RV E (g(X)) = ∫g(x) f (x) dx for a Continuous RV

Variance Var (X) = E (X 2 ) – (E(X)) 2

Standard Deviation SD (X) = √Var (X)

Properties of Expectation E (c) = c for a constant c E (c X) = c E (X) for a constant c E (c X + d) = c E (X) + d for constants c & d

Properties of Variance Var (c) = 0 for a constant c Var (c X) = c 2 Var (X) for a constant c Var (c X + d) = c 2 Var (X) for constants c & d

Example 1 (Contd) X = No of Heads. Find the following: (a)E (X)Ans:1 (b)E (X 2 )Ans:1.5 (c)E ((X+10) 2 )Ans: (d)E (2 X )Ans:2.25 (e)Var (X)Ans:0.5 (f)SD (X)Ans:1/√2

Example 4 If X is a random variable with the probability density function f (x) = 2 (1 - x) for 0 < x < 1 find the following: (a)E (X)Ans:1/3 (b)E (X 2 )Ans:1/6 (c)E ((X+10) 2 )Ans: (d)Var (X)Ans:1/18 (e)SD (X)Ans:1/(3 √ 2)

Example 5 An urn contains 4 balls numbered 1, 2, 3 & 4. Let X denote the number that occurs if one ball is drawn at random from the urn. What is the PMF of X?

Example 5 (Contd) Two balls are drawn from the urn without replacement. Let X be the sum of the two numbers that occur. Derive the PMF of X.

Example 6 The church lottery is going to give away a £3,000 car and 10,000 tickets at £1 a piece. (a)If you buy 1 ticket, what is your expected gain. (Ans: -0.7) (b)What is your expected gain if you buy 100 tickets? (Ans: -70) (c)Compute the variance of your gain in these two instances. (Ans: & 89100)

Example 7 A box contains 20 items, 4 of them are defective. Two items are chosen without replacement. Let X = No of defective items chosen. Find the PMF of X.

Example 8 You throw two fair dice, one green and one red. Find the PMF of X if X is defined as: A) Sum of the two numbers B) Difference of the two numbers C) Minimum of the two numbers D) Maximum of the two numbers

Example 9 If X has the PMF p (x) = ¼ for x = 2, 4, 8, 16 compute the following: (a)E (X)Ans:7.5 (b)E (X 2 )Ans:85 (c)E (1/X)Ans: 15/64 (d)E (2 X/2 )Ans:139/2 (e)Var (X)Ans:115/4 (f)SD (X)Ans:√115/2

Example 10 If X is a random variable with the probability density function f (x) = 10 exp (-10 x) for x > 0 find the following: (a)E (X)Ans:0.1 (b)E (X 2 )Ans:0.02 (c)E ((X+10) 2 )Ans: (d)Var (X)Ans:0.01 (e)SD (X)Ans:0.1

Example 11 If X is a random variable with the probability density function f (x) = (1/ √ (2  )) exp (-0.5 x 2 ) for -  < x <  find the following: (a)E (X)Ans:0 (b)E (X 2 )Ans:1 (c)E ((X+10) 2 )Ans:101 (d)Var (X)Ans:1 (e)SD (X)Ans:1

Example 12 A game is played where a person pays to roll two fair six-sided dice. If exactly one six is shown uppermost, the player wins £5. If exactly 2 sixes are shown uppermost, then the player wins £20. How much should be charged to play this game is the player is to break-even?

Example 13 Mr. Smith buys a £4000 insurance policy on his son’s violin. The premium is £50 per year. If the probability that the violin will need to be replaced is 0.8%, what is the insurance company’s gain (if any) on this policy?