Math a Discrete Random Variables

Slides:



Advertisements
Similar presentations
Chapter 2 Multivariate Distributions Math 6203 Fall 2009 Instructor: Ayona Chatterjee.
Advertisements

A.P. STATISTICS LESSON 7 – 1 ( DAY 1 ) DISCRETE AND CONTINUOUS RANDOM VARIABLES.
Use of moment generating functions. Definition Let X denote a random variable with probability density function f(x) if continuous (probability mass function.
Continuous Random Variable (1). Discrete Random Variables Probability Mass Function (PMF)
Review of Basic Probability and Statistics
Probability Theory Part 2: Random Variables. Random Variables  The Notion of a Random Variable The outcome is not always a number Assign a numerical.
Class notes for ISE 201 San Jose State University
Variance and Standard Deviation The Expected Value of a random variable gives the average value of the distribution The Standard Deviation shows how spread.
Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data.
The moment generating function of random variable X is given by Moment generating function.
Continuous Random Variables and Probability Distributions
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.
Discrete Probability Distributions A sample space can be difficult to describe and work with if its elements are not numeric.A sample space can be difficult.
Statistical Experiment A statistical experiment or observation is any process by which an measurements are obtained.
1 Dr. Jerrell T. Stracener, SAE Fellow Leadership in Engineering EMIS 7370/5370 STAT 5340 : PROBABILITY AND STATISTICS FOR SCIENTISTS AND ENGINEERS Systems.
5.3 Random Variables  Random Variable  Discrete Random Variables  Continuous Random Variables  Normal Distributions as Probability Distributions 1.
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.
Chapter 5: The Binomial Probability Distribution and Related Topics Section 1: Introduction to Random Variables and Probability Distributions.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 5 Discrete Random Variables.
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.
Math b (Discrete) Random Variables, Binomial Distribution.
4.1 Probability Distributions NOTES Coach Bridges.
4.1 Probability Distributions Important Concepts –Random Variables –Probability Distribution –Mean (or Expected Value) of a Random Variable –Variance and.
EQT 272 PROBABILITY AND STATISTICS
Chapter 16 Probability Models. Who Wants to Play?? $5 to play You draw a card: – if you get an Ace of Hearts, I pay you $100 – if you get any other Ace,
Math 4030 Midterm Exam Review. General Info: Wed. Oct. 26, Lecture Hours & Rooms Duration: 80 min. Close-book 1 page formula sheet (both sides can be.
Random Variables (1) A random variable (also known as a stochastic variable), x, is a quantity such as strength, size, or weight, that depends upon a.
Chapter 3 Discrete Random Variables and Probability Distributions  Random Variables.2 - Probability Distributions for Discrete Random Variables.3.
CY1B2 Statistics1 (ii) Poisson distribution The Poisson distribution resembles the binomial distribution if the probability of an accident is very small.
Math 4030 – 6a Joint Distributions (Discrete)
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)
Chapter 3 Discrete Random Variables and Probability Distributions  Random Variables.2 - Probability Distributions for Discrete Random Variables.3.
Discrete Random Variables
CHAPTER Discrete Models  G eneral distributions  C lassical: Binomial, Poisson, etc Continuous Models  G eneral distributions 
MATH Section 3.1.
Chapter 4 Discrete Probability Distributions 4.1 Probability Distributions I.Random Variables A random variable x represents a numerical value associated5with.
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:
Unit 4 Review. Starter Write the characteristics of the binomial setting. What is the difference between the binomial setting and the geometric setting?
AP Stats Chapter 7 Review Nick Friedl, Patrick Donovan, Jay Dirienzo.
1 Dr. Jerrell T. Stracener, SAE Fellow Leadership in Engineering EMIS 7370/5370 STAT 5340 : PROBABILITY AND STATISTICS FOR SCIENTISTS AND ENGINEERS Systems.
Random Variables By: 1.
Discrete Random Variables Section 6.1. Objectives Distinguish between discrete and continuous random variables Identify discrete probability distributions.
Mean, variance, standard deviation and expectation
MATH 2311 Section 3.1.
Random Variables and Probability Distribution (2)
CHAPTER 2 RANDOM VARIABLES.
Random Variables.
CH 23: Discrete random variables
Math a Descriptive Statistics Tables and Charts
STAT 311 REVIEW (Quick & Dirty)
Cumulative distribution functions and expected values
Random Variable.
Sample Mean Distributions
The distribution function F(x)
Lecture 13 Sections 5.4 – 5.6 Objectives:
Chapter. 5_Probability Distributions
AP Statistics: Chapter 7
Means and Variances of Random Variables
Expectation & Variance of a Discrete Random Variable
ASV Chapters 1 - Sample Spaces and Probabilities
Random Variable.
Lecture 34 Section 7.5 Wed, Mar 24, 2004
Chebychev, Hoffding, Chernoff
Lecture 23 Section Mon, Oct 25, 2004
RANDOM VARIABLES Random variable:
Chapter 5: Discrete Probability Distributions
Chapter 2. Random Variables
Sample Means Section 9.3.
Distributive Property
Presentation transcript:

Math 4030-3a Discrete Random Variables

Random variable A function that assigns a numerical value to each possible outcome in the sample space. One value for one outcome. i.e. different value must mean different outcomes. However, different outcomes may have the same value. Random variable may take discrete or continuous values. Sample space S R 5/28/2018

Probability Distribution of a discrete random variable: A list of probability values corresponding to all values of a discrete random variable X. i.e. for any value x that the random variable X takes. 5/28/2018

Probability histogram and bar chart; Cumulative distribution function F(x): If X takes values then 5/28/2018

Mean of a random variable Mean may or may not exist; Even exists, may or may not be a finite number; If X has only finitely many values, then mean exists and is a finite value. Average value Balance point Expect value 5/28/2018

Properties of Means: Linearity: Warning: 5/28/2018

Variance of a random variable Variance is a “mean”; Always nonnegative; Variance may or may not exist; Even exists, may or may not be a finite number; If X has only finitely many values, then variance exists and is a finite value. Measure the variability of X Spread of the histogram Un-predictable, risky, … 5/28/2018

Properties of variance Warning: 5/28/2018

Chebyshev’s Theorem If X is a random variable with mean  and standard deviation , then for any number k > 1, Equivalently, 5/28/2018