Random Variables A random variable is a rule that assigns exactly one value to each point in a sample space for an experiment. A random variable can be.

Slides:



Advertisements
Similar presentations
Continuous Random Variables Chapter 5 Nutan S. Mishra Department of Mathematics and Statistics University of South Alabama.
Advertisements

Random Variables Probability Continued Chapter 7.
Business and Finance College Principles of Statistics Eng. Heba Hamad 2008.
Chapter 3 Probability Distribution. Chapter 3, Part A Probability Distributions n Random Variables n Discrete Probability Distributions n Binomial Probability.
1 1 Slide © 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1 1 Slide © 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Continuous Random Variables. For discrete random variables, we required that Y was limited to a finite (or countably infinite) set of values. Now, for.
Expectation Random Variables Graphs and Histograms Expected Value.
Discrete Probability Distributions Random variables Discrete probability distributions Expected value and variance Binomial probability distribution.
1 1 Slide © 2006 Thomson/South-Western Chapter 5 Discrete Probability Distributions n Random Variables n Discrete Probability Distributions.
OMS 201 Review. Range The range of a data set is the difference between the largest and smallest data values. It is the simplest measure of dispersion.
QMS 6351 Statistics and Research Methods Probability and Probability distributions Chapter 4, page 161 Chapter 5 (5.1) Chapter 6 (6.2) Prof. Vera Adamchik.
Continuous Probability Distributions A continuous random variable can assume any value in an interval on the real line or in a collection of intervals.
1 1 Slide © 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
LECTURE UNIT 4.3 Normal Random Variables and Normal Probability Distributions.
Probability Distributions. A sample space is the set of all possible outcomes in a distribution. Distributions can be discrete or continuous.
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.
Chapter 5 Discrete Probability Distributions
1 1 Slide © 2016 Cengage Learning. All Rights Reserved. A random variable is a numerical description of the A random variable is a numerical description.
Chapter 5 Discrete Probability Distributions n Random Variables n Discrete Probability Distributions n Expected Value and Variance n Binomial Probability.
Business and Finance College Principles of Statistics Eng. Heba Hamad 2008.
1 1 Slide © 2007 Thomson South-Western. All Rights Reserved Chapter 5 Discrete Probability Distributions n Random Variables n Discrete.
1 1 Slide IS 310 – Business Statistics IS 310 Business Statistics CSU Long Beach.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
1 1 Slide Discrete Probability Distributions (Random Variables and Discrete Probability Distributions) Chapter 5 BA 201.
L7.1b Continuous Random Variables CONTINUOUS RANDOM VARIABLES NORMAL DISTRIBUTIONS AD PROBABILITY DISTRIBUTIONS.
Probability Distributions - Discrete Random Variables Outcomes and Events.
Discrete probability Business Statistics (BUSA 3101) Dr. Lari H. Arjomand
Discrete Probability Distributions n Random Variables n Discrete Probability Distributions n Expected Value and Variance n Binomial Probability Distribution.
Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal
1 1 Slide © 2009 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Statistical Applications Binominal and Poisson’s Probability distributions E ( x ) =  =  xf ( x )
BIA 2610 – Statistical Methods Chapter 5 – Discrete Probability Distributions.
1 1 Slide © 2004 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
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)
Chapter 5 Discrete Probability Distributions n Random Variables n Discrete Probability Distributions n Expected Value and Variance
4.1 Probability Distributions NOTES Coach Bridges.
 Random variables can be classified as either discrete or continuous.  Example: ◦ Discrete: mostly counts ◦ Continuous: time, distance, etc.
1 1 Slide © 2004 Thomson/South-Western Chapter 3, Part A Discrete Probability Distributions n Random Variables n Discrete Probability Distributions n Expected.
Business Statistics (BUSA 3101). Dr.Lari H. Arjomand Continus Probability.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Mistah Flynn.
CONTINUOUS RANDOM VARIABLES
Random Variables Learn how to characterize the pattern of the distribution of values that a random variable may have, and how to use the pattern to find.
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)
1 1 Slide © 2006 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Chapter 5. Continuous Random Variables. Continuous Random Variables Discrete random variables –Random variables whose set of possible values is either.
Chap 7.1 Discrete and Continuous Random Variables.
MATH Section 3.1.
Copyright ©2011 Brooks/Cole, Cengage Learning Random Variables Class 34 1.
PROBABILITY DISTRIBUTIONS DISCRETE RANDOM VARIABLES OUTCOMES & EVENTS Mrs. Aldous & Mr. Thauvette IB DP SL Mathematics.
Random Variables By: 1.
Continuous Random Variable
MATH 2311 Section 3.1.
Chapter 5 - Discrete Probability Distributions
Continuous Random Variables
Random Variable.
CONTINUOUS RANDOM VARIABLES
St. Edward’s University
St. Edward’s University
AP Statistics: Chapter 7
Econometric Models The most basic econometric model consists of a relationship between two variables which is disturbed by a random error. We need to use.
Random Variable.
Statistics Lecture 12.
Statistics for Business and Economics (13e)
Chapter 5: Discrete Probability Distributions
Econ 3790: Business and Economics Statistics
Chapter 5 Discrete Probability Distributions
Random Variables A random variable is a rule that assigns exactly one value to each point in a sample space for an experiment. A random variable can be.
Experiments, Outcomes, Events and Random Variables: A Revisit
Presentation transcript:

Random Variables A random variable is a rule that assigns exactly one value to each point in a sample space for an experiment. A random variable can be classified as being either discrete or continuous depending on the numerical values it assumes. A discrete random variable may assume either a finite number of values or an infinite sequence of values. A continuous random variable may assume any numerical value in an interval or collection of intervals.

Random Variables QuestionRandom Variable x Type Family x = Number of dependents in Discrete size family reported on tax return Distance from x = Distance in miles fromContinuous home to store home to the store site Own dog x = 1 if own no pet; Discrete or cat = 2 if own dog(s) only; = 3 if own cat(s) only; = 4 if own dog(s) and cat(s)

Probability Distributions The probability distribution for a random variable describes how probabilities are distributed over the values of the random variable. The probability distribution is defined by a probability function which provides the probability for each value of the random variable.

Continuous Probability Distributions A continuous random variable can assume any value in an interval on the real line or in a collection of intervals. It is not possible to talk about the probability of the random variable assuming a particular value. Instead, we talk about the probability of the random variable assuming a value within a given interval.

The probability of the random variable assuming a value within some given interval from x 1 to x 2 is defined to be the area under the graph of the probability density function between x 1 and x 2. Continuous Probability Distributions

Probability Density Function For a continuous random variable X, a probability density function is a function such that (1) (2) (3)

Example

Does it Qualify?

Finding Value of k…

Using CAS

More Complicated

Cont…

More CAS…

Statistics using PDF…

Cont…