Chapter 3 - Lecture 3 Expected Values of Discrete Random Va

Size: px
Start display at page:

Download "Chapter 3 - Lecture 3 Expected Values of Discrete Random Va"

Transcription

1 Chapter 3 - Lecture 3 Expected Values of Discrete Random Variables October 5th, 2009

2 Properties of expected value Standard deviation Shortcut formula Properties of the variance

3 Properties of expected value In State College there are families. I asked them how many kids they have and I get the following answers: 0 kids: 900 families 1 kid: 1700 families 2 kids: 4000 families 3 kids: 2100 families 4 kids: 700 families 5 kids: 400 families 6 kids: 200 families If I choose a family at random, how many kids I expect it to have? How did I calculate this?

4 Properties of expected value Expected value If X is a random variable with a set of all possible values denoted with D and pmf p(x). The expected value or mean value of X is: E(X ) = µ X = x D xp(x)

5 Properties of expected value Roll of a die I roll a die once. What is the expected value of the outcome?

6 Properties of expected value Birth of a child Let say the probability of a pregnant woman to give a birth to a boy is p. What is the expected number of X = the child is a boy? What is the expected number of Y = the child is a girl? How many boys I expect to see if I observe 100 births?

7 Properties of expected value If the random variable X has a set of possible values D then the expected value of the function of X, h(x ), is calculated by: E(h(X )) = µ h (X ) = x D h(x)p(x)

8 Properties of expected value Lets go back to the example with the kids per family in State College. Let s say that each family spends Y = X X dollars on grocery shopping each week where X is the number of kids in the family. How much money on average families in State College spend in grocery shopping per week.

9 Properties of expected value Properties E(aX ) = ae(x ) Proof? E(X + b) = E(X ) + b Proof? E(aX + b) =?

10 Outline Standard deviation Shortcut formula Properties of the variance Assume that I give to 5 students a test and their scores are 85, 85, 85, 85, 85. What is the average of those students? Assume that I give to 5 students a test and their scores are 50, 85, 100, 100, 90. What is the average of those students?

11 of a random variable Standard deviation Shortcut formula Properties of the variance Let X have pmf p(x) and expected value E(X ). Then the variance of X is given by: ( var(x ) = σx 2 = E (X E(X )) 2) = (x E(X )) 2 p(x) x D

12 Standard deviation Outline Standard deviation Shortcut formula Properties of the variance The standard deviation is another useful measure and is actually the square root of the variance: σ X = σx 2

13 Outline Standard deviation Shortcut formula Properties of the variance Lets go back to the example with the kids per family in State College. Find the variance and standard deviation of the number of kids per family in State College.

14 Shortcut formula for Standard deviation Shortcut formula Properties of the variance

15 Properties Outline Standard deviation Shortcut formula Properties of the variance var(ax ) = a 2 var(x ) Proof? var(x + b) = var(x ) Proof? var(ax + b) =?

16 Section 3.3 page , 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43

Chapter 3 - Lecture 5 The Binomial Probability Distribution

Chapter 3 - Lecture 5 The Binomial Probability Distribution Chapter 3 - Lecture 5 The Binomial Probability October 12th, 2009 Experiment Examples Moments and moment generating function of a Binomial Random Variable Outline Experiment Examples A binomial experiment

More information

Expectations. Definition Let X be a discrete rv with set of possible values D and pmf p(x). The expected value or mean value of X, denoted by E(X ) or

Expectations. Definition Let X be a discrete rv with set of possible values D and pmf p(x). The expected value or mean value of X, denoted by E(X ) or Definition Let X be a discrete rv with set of possible values D and pmf p(x). The expected value or mean value of X, denoted by E(X ) or µ X, is E(X ) = µ X = x D x p(x) Definition Let X be a discrete

More information

Mean of a Discrete Random variable. Suppose that X is a discrete random variable whose distribution is : :

Mean of a Discrete Random variable. Suppose that X is a discrete random variable whose distribution is : : Dr. Kim s Note (December 17 th ) The values taken on by the random variable X are random, but the values follow the pattern given in the random variable table. What is a typical value of a random variable

More information

Chapter 3 - Lecture 4 Moments and Moment Generating Funct

Chapter 3 - Lecture 4 Moments and Moment Generating Funct Chapter 3 - Lecture 4 and s October 7th, 2009 Chapter 3 - Lecture 4 and Moment Generating Funct Central Skewness Chapter 3 - Lecture 4 and Moment Generating Funct Central Skewness The expected value of

More information

Discrete probability distributions

Discrete probability distributions Discrete probability distributions Probability distributions Discrete random variables Expected values (mean) Variance Linear functions - mean & standard deviation Standard deviation 1 Probability distributions

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables In this chapter, we introduce a new concept that of a random variable or RV. A random variable is a model to help us describe the state of the world around us. Roughly, a RV can

More information

Chapter 16. Random Variables. Copyright 2010 Pearson Education, Inc.

Chapter 16. Random Variables. Copyright 2010 Pearson Education, Inc. Chapter 16 Random Variables Copyright 2010 Pearson Education, Inc. Expected Value: Center A random variable assumes a value based on the outcome of a random event. We use a capital letter, like X, to denote

More information

Lecture 18 Section Mon, Feb 16, 2009

Lecture 18 Section Mon, Feb 16, 2009 The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Feb 16, 2009 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income

More information

Shifting our focus. We were studying statistics (data, displays, sampling...) The next few lectures focus on probability (randomness) Why?

Shifting our focus. We were studying statistics (data, displays, sampling...) The next few lectures focus on probability (randomness) Why? Probability Introduction Shifting our focus We were studying statistics (data, displays, sampling...) The next few lectures focus on probability (randomness) Why? What is Probability? Probability is used

More information

Lecture 18 Section Mon, Sep 29, 2008

Lecture 18 Section Mon, Sep 29, 2008 The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Sep 29, 2008 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income

More information

Chapter 16. Random Variables. Copyright 2010, 2007, 2004 Pearson Education, Inc.

Chapter 16. Random Variables. Copyright 2010, 2007, 2004 Pearson Education, Inc. Chapter 16 Random Variables Copyright 2010, 2007, 2004 Pearson Education, Inc. Expected Value: Center A random variable is a numeric value based on the outcome of a random event. We use a capital letter,

More information

STA Module 3B Discrete Random Variables

STA Module 3B Discrete Random Variables STA 2023 Module 3B Discrete Random Variables Learning Objectives Upon completing this module, you should be able to 1. Determine the probability distribution of a discrete random variable. 2. Construct

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2017 Objectives During this lesson we will learn to: distinguish between discrete and continuous

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2018 Objectives During this lesson we will learn to: distinguish between discrete and continuous

More information

Chapter 3 Discrete Random Variables and Probability Distributions

Chapter 3 Discrete Random Variables and Probability Distributions Chapter 3 Discrete Random Variables and Probability Distributions Part 2: Mean and Variance of a Discrete Random Variable Section 3.4 1 / 16 Discrete Random Variable - Expected Value In a random experiment,

More information

STOR Lecture 7. Random Variables - I

STOR Lecture 7. Random Variables - I STOR 435.001 Lecture 7 Random Variables - I Shankar Bhamidi UNC Chapel Hill 1 / 31 Example 1a: Suppose that our experiment consists of tossing 3 fair coins. Let Y denote the number of heads that appear.

More information

STA Rev. F Learning Objectives. What is a Random Variable? Module 5 Discrete Random Variables

STA Rev. F Learning Objectives. What is a Random Variable? Module 5 Discrete Random Variables STA 2023 Module 5 Discrete Random Variables Learning Objectives Upon completing this module, you should be able to: 1. Determine the probability distribution of a discrete random variable. 2. Construct

More information

Chapter 5 Discrete Probability Distributions. Random Variables Discrete Probability Distributions Expected Value and Variance

Chapter 5 Discrete Probability Distributions. Random Variables Discrete Probability Distributions Expected Value and Variance Chapter 5 Discrete Probability Distributions Random Variables Discrete Probability Distributions Expected Value and Variance.40.30.20.10 0 1 2 3 4 Random Variables A random variable is a numerical description

More information

Discrete Random Variables and Probability Distributions. Stat 4570/5570 Based on Devore s book (Ed 8)

Discrete Random Variables and Probability Distributions. Stat 4570/5570 Based on Devore s book (Ed 8) 3 Discrete Random Variables and Probability Distributions Stat 4570/5570 Based on Devore s book (Ed 8) Random Variables We can associate each single outcome of an experiment with a real number: We refer

More information

Have you ever wondered whether it would be worth it to buy a lottery ticket every week, or pondered on questions such as If I were offered a choice

Have you ever wondered whether it would be worth it to buy a lottery ticket every week, or pondered on questions such as If I were offered a choice Section 8.5: Expected Value and Variance Have you ever wondered whether it would be worth it to buy a lottery ticket every week, or pondered on questions such as If I were offered a choice between a million

More information

Probability Distributions for Discrete RV

Probability Distributions for Discrete RV Probability Distributions for Discrete RV Probability Distributions for Discrete RV Definition The probability distribution or probability mass function (pmf) of a discrete rv is defined for every number

More information

Econ 6900: Statistical Problems. Instructor: Yogesh Uppal

Econ 6900: Statistical Problems. Instructor: Yogesh Uppal Econ 6900: Statistical Problems Instructor: Yogesh Uppal Email: yuppal@ysu.edu Lecture Slides 4 Random Variables Probability Distributions Discrete Distributions Discrete Uniform Probability Distribution

More information

Lecture 37 Sections 11.1, 11.2, Mon, Mar 31, Hampden-Sydney College. Independent Samples: Comparing Means. Robb T. Koether.

Lecture 37 Sections 11.1, 11.2, Mon, Mar 31, Hampden-Sydney College. Independent Samples: Comparing Means. Robb T. Koether. : : Lecture 37 Sections 11.1, 11.2, 11.4 Hampden-Sydney College Mon, Mar 31, 2008 Outline : 1 2 3 4 5 : When two samples are taken from two different populations, they may be taken independently or not

More information

4 Random Variables and Distributions

4 Random Variables and Distributions 4 Random Variables and Distributions Random variables A random variable assigns each outcome in a sample space. e.g. called a realization of that variable to Note: We ll usually denote a random variable

More information

Lecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial

Lecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial Lecture 23 STAT 225 Introduction to Probability Models April 4, 2014 approximation Whitney Huang Purdue University 23.1 Agenda 1 approximation 2 approximation 23.2 Characteristics of the random variable:

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables ST 370 A random variable is a numerical value associated with the outcome of an experiment. Discrete random variable When we can enumerate the possible values of the variable

More information

No, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation.

No, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation. 1) If n 100 and p 0.02 in a binomial experiment, does this satisfy the rule for a normal approximation? Why or why not? No, because np 100(0.02) 2. The value of np must be greater than or equal to 5 to

More information

STT315 Chapter 4 Random Variables & Probability Distributions AM KM

STT315 Chapter 4 Random Variables & Probability Distributions AM KM Before starting new chapter: brief Review from Algebra Combinations In how many ways can we select x objects out of n objects? In how many ways you can select 5 numbers out of 45 numbers ballot to win

More information

Random Variables. Copyright 2009 Pearson Education, Inc.

Random Variables. Copyright 2009 Pearson Education, Inc. Random Variables Copyright 2009 Pearson Education, Inc. A random variable assumes a value based on the outcome of a random event. We use a capital letter, like X, to note a random variable. A particular

More information

Standard Deviation. Lecture 18 Section Robb T. Koether. Hampden-Sydney College. Mon, Sep 26, 2011

Standard Deviation. Lecture 18 Section Robb T. Koether. Hampden-Sydney College. Mon, Sep 26, 2011 Standard Deviation Lecture 18 Section 5.3.4 Robb T. Koether Hampden-Sydney College Mon, Sep 26, 2011 Robb T. Koether (Hampden-Sydney College) Standard Deviation Mon, Sep 26, 2011 1 / 42 Outline 1 Variability

More information

Chapter 8: The Binomial and Geometric Distributions

Chapter 8: The Binomial and Geometric Distributions Chapter 8: The Binomial and Geometric Distributions 8.1 Binomial Distributions 8.2 Geometric Distributions 1 Let me begin with an example My best friends from Kent School had three daughters. What is the

More information

Math Week in Review #10. Experiments with two outcomes ( success and failure ) are called Bernoulli or binomial trials.

Math Week in Review #10. Experiments with two outcomes ( success and failure ) are called Bernoulli or binomial trials. Math 141 Spring 2006 c Heather Ramsey Page 1 Section 8.4 - Binomial Distribution Math 141 - Week in Review #10 Experiments with two outcomes ( success and failure ) are called Bernoulli or binomial trials.

More information

Math 14 Lecture Notes Ch. 4.3

Math 14 Lecture Notes Ch. 4.3 4.3 The Binomial Distribution Example 1: The former Sacramento King's DeMarcus Cousins makes 77% of his free throws. If he shoots 3 times, what is the probability that he will make exactly 0, 1, 2, or

More information

MLLunsford 1. Activity: Mathematical Expectation

MLLunsford 1. Activity: Mathematical Expectation MLLunsford 1 Activity: Mathematical Expectation Concepts: Mathematical Expectation for discrete random variables. Includes expected value and variance. Prerequisites: The student should be familiar with

More information

Let X be the number that comes up on the next roll of the die.

Let X be the number that comes up on the next roll of the die. Chapter 6 - Discrete Probability Distributions 6.1 Random Variables Introduction If we roll a fair die, the possible outcomes are the numbers 1, 2, 3, 4, 5, and 6, and each of these numbers has probability

More information

Normal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial.

Normal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial. Lecture 21,22, 23 Text: A Course in Probability by Weiss 8.5 STAT 225 Introduction to Probability Models March 31, 2014 Standard Sums of Whitney Huang Purdue University 21,22, 23.1 Agenda 1 2 Standard

More information

Review of the Topics for Midterm I

Review of the Topics for Midterm I Review of the Topics for Midterm I STA 100 Lecture 9 I. Introduction The objective of statistics is to make inferences about a population based on information contained in a sample. A population is the

More information

X Prob

X Prob Wednesday, December 6, 2017 Warm-up Faked numbers in tax returns, invoices, or expense account claims often display patterns that aren t present in legitimate records. Some patterns, like too many round

More information

Lecture 9: Plinko Probabilities, Part III Random Variables, Expected Values and Variances

Lecture 9: Plinko Probabilities, Part III Random Variables, Expected Values and Variances Physical Principles in Biology Biology 3550 Fall 2018 Lecture 9: Plinko Probabilities, Part III Random Variables, Expected Values and Variances Monday, 10 September 2018 c David P. Goldenberg University

More information

15.063: Communicating with Data Summer Recitation 3 Probability II

15.063: Communicating with Data Summer Recitation 3 Probability II 15.063: Communicating with Data Summer 2003 Recitation 3 Probability II Today s Goal Binomial Random Variables (RV) Covariance and Correlation Sums of RV Normal RV 15.063, Summer '03 2 Random Variables

More information

Statistics 511 Additional Materials

Statistics 511 Additional Materials Discrete Random Variables In this section, we introduce the concept of a random variable or RV. A random variable is a model to help us describe the state of the world around us. Roughly, a RV can be thought

More information

Part 1 In which we meet the law of averages. The Law of Averages. The Expected Value & The Standard Error. Where Are We Going?

Part 1 In which we meet the law of averages. The Law of Averages. The Expected Value & The Standard Error. Where Are We Going? 1 The Law of Averages The Expected Value & The Standard Error Where Are We Going? Sums of random numbers The law of averages Box models for generating random numbers Sums of draws: the Expected Value Standard

More information

Lecture 9. Probability Distributions. Outline. Outline

Lecture 9. Probability Distributions. Outline. Outline Outline Lecture 9 Probability Distributions 6-1 Introduction 6- Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7- Properties of the Normal Distribution

More information

Sampling & populations

Sampling & populations Sampling & populations Sample proportions Sampling distribution - small populations Sampling distribution - large populations Sampling distribution - normal distribution approximation Mean & variance of

More information

Midterm Exam III Review

Midterm Exam III Review Midterm Exam III Review Dr. Joseph Brennan Math 148, BU Dr. Joseph Brennan (Math 148, BU) Midterm Exam III Review 1 / 25 Permutations and Combinations ORDER In order to count the number of possible ways

More information

Lecture 9. Probability Distributions

Lecture 9. Probability Distributions Lecture 9 Probability Distributions Outline 6-1 Introduction 6-2 Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7-2 Properties of the Normal Distribution

More information

Binomal and Geometric Distributions

Binomal and Geometric Distributions Binomal and Geometric Distributions Sections 3.2 & 3.3 Cathy Poliak, Ph.D. cathy@math.uh.edu Office in Fleming 11c Department of Mathematics University of Houston Lecture 7-2311 Cathy Poliak, Ph.D. cathy@math.uh.edu

More information

Week 7. Texas A& M University. Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4

Week 7. Texas A& M University. Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4 Week 7 Oğuz Gezmiş Texas A& M University Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4 Oğuz Gezmiş (TAMU) Topics in Contemporary Mathematics II Week7 1 / 19

More information

Figure 1: 2πσ is said to have a normal distribution with mean µ and standard deviation σ. This is also denoted

Figure 1: 2πσ is said to have a normal distribution with mean µ and standard deviation σ. This is also denoted Figure 1: Math 223 Lecture Notes 4/1/04 Section 4.10 The normal distribution Recall that a continuous random variable X with probability distribution function f(x) = 1 µ)2 (x e 2σ 2πσ is said to have a

More information

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 - Would the correlation between x and y in the table above be positive or negative? The correlation is negative. -

More information

The Binomial Distribution

The Binomial Distribution MATH 382 The Binomial Distribution Dr. Neal, WKU Suppose there is a fixed probability p of having an occurrence (or success ) on any single attempt, and a sequence of n independent attempts is made. Then

More information

Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman:

Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman: Math 224 Fall 207 Homework 5 Drew Armstrong Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman: Section 3., Exercises 3, 0. Section 3.3, Exercises 2, 3, 0,.

More information

Statistics vs. statistics

Statistics vs. statistics Statistics vs. statistics Question: What is Statistics (with a capital S)? Definition: Statistics is the science of collecting, organizing, summarizing and interpreting data. Note: There are 2 main ways

More information

Introduction to Statistics I

Introduction to Statistics I Introduction to Statistics I Keio University, Faculty of Economics Continuous random variables Simon Clinet (Keio University) Intro to Stats November 1, 2018 1 / 18 Definition (Continuous random variable)

More information

5. In fact, any function of a random variable is also a random variable

5. In fact, any function of a random variable is also a random variable Random Variables - Class 11 October 14, 2012 Debdeep Pati 1 Random variables 1.1 Expectation of a function of a random variable 1. Expectation of a function of a random variable 2. We know E(X) = x xp(x)

More information

Random variables. Discrete random variables. Continuous random variables.

Random variables. Discrete random variables. Continuous random variables. Random variables Discrete random variables. Continuous random variables. Discrete random variables. Denote a discrete random variable with X: It is a variable that takes values with some probability. Examples:

More information

Overview. Definitions. Definitions. Graphs. Chapter 4 Probability Distributions. probability distributions

Overview. Definitions. Definitions. Graphs. Chapter 4 Probability Distributions. probability distributions Chapter 4 Probability Distributions 4-1 Overview 4-2 Random Variables 4-3 Binomial Probability Distributions 4-4 Mean, Variance, and Standard Deviation for the Binomial Distribution 4-5 The Poisson Distribution

More information

CHAPTER 6 Random Variables

CHAPTER 6 Random Variables CHAPTER 6 Random Variables 6.1 Discrete and Continuous Random Variables The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers Discrete and Continuous Random

More information

Lecture 8. The Binomial Distribution. Binomial Distribution. Binomial Distribution. Probability Distributions: Normal and Binomial

Lecture 8. The Binomial Distribution. Binomial Distribution. Binomial Distribution. Probability Distributions: Normal and Binomial Lecture 8 The Binomial Distribution Probability Distributions: Normal and Binomial 1 2 Binomial Distribution >A binomial experiment possesses the following properties. The experiment consists of a fixed

More information

STAT 111 Recitation 3

STAT 111 Recitation 3 STAT 111 Recitation 3 Linjun Zhang stat.wharton.upenn.edu/~linjunz/ September 23, 2017 Misc. The unpicked-up homeworks will be put in the STAT 111 box in the Stats Department lobby (It s on the 4th floor

More information

Binomial and Geometric Distributions

Binomial and Geometric Distributions Binomial and Geometric Distributions Section 3.2 & 3.3 Cathy Poliak, Ph.D. cathy@math.uh.edu Office hours: T Th 2:30 pm - 5:15 pm 620 PGH Department of Mathematics University of Houston February 11, 2016

More information

Statistics, Their Distributions, and the Central Limit Theorem

Statistics, Their Distributions, and the Central Limit Theorem Statistics, Their Distributions, and the Central Limit Theorem MATH 3342 Sections 5.3 and 5.4 Sample Means Suppose you sample from a popula0on 10 0mes. You record the following sample means: 10.1 9.5 9.6

More information

Discrete Probability Distribution

Discrete Probability Distribution 1 Discrete Probability Distribution Key Definitions Discrete Random Variable: Has a countable number of values. This means that each data point is distinct and separate. Continuous Random Variable: Has

More information

Probability Distributions

Probability Distributions Chapter 6 Discrete Probability Distributions Section 6-2 Probability Distributions Definitions Let S be the sample space of a probability experiment. A random variable X is a function from the set S into

More information

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise. Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x

More information

Problem Set 07 Discrete Random Variables

Problem Set 07 Discrete Random Variables Name Problem Set 07 Discrete Random Variables MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find the mean of the random variable. 1) The random

More information

6. Continous Distributions

6. Continous Distributions 6. Continous Distributions Chris Piech and Mehran Sahami May 17 So far, all random variables we have seen have been discrete. In all the cases we have seen in CS19 this meant that our RVs could only take

More information

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions April 9th, 2018 Lecture 20: Special distributions Week 1 Chapter 1: Axioms of probability Week 2 Chapter 3: Conditional probability and independence Week 4 Chapters 4, 6: Random variables Week 9 Chapter

More information

Statistics for Business and Economics: Random Variables (1)

Statistics for Business and Economics: Random Variables (1) Statistics for Business and Economics: Random Variables (1) STT 315: Section 201 Instructor: Abdhi Sarkar Acknowledgement: I d like to thank Dr. Ashoke Sinha for allowing me to use and edit the slides.

More information

Examples: Random Variables. Discrete and Continuous Random Variables. Probability Distributions

Examples: Random Variables. Discrete and Continuous Random Variables. Probability Distributions Random Variables Examples: Random variable a variable (typically represented by x) that takes a numerical value by chance. Number of boys in a randomly selected family with three children. Possible values:

More information

Probability Theory. Mohamed I. Riffi. Islamic University of Gaza

Probability Theory. Mohamed I. Riffi. Islamic University of Gaza Probability Theory Mohamed I. Riffi Islamic University of Gaza Table of contents 1. Chapter 2 Discrete Distributions The binomial distribution 1 Chapter 2 Discrete Distributions Bernoulli trials and the

More information

Example - Let X be the number of boys in a 4 child family. Find the probability distribution table:

Example - Let X be the number of boys in a 4 child family. Find the probability distribution table: Chapter8 Probability Distributions and Statistics Section 8.1 Distributions of Random Variables tthe value of the result of the probability experiment is a RANDOM VARIABLE. Example - Let X be the number

More information

Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance

Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance Prof. Tesler Math 186 Winter 2017 Prof. Tesler Ch. 5: Confidence Intervals, Sample Variance Math 186 / Winter 2017 1 / 29 Estimating parameters

More information

MA 1125 Lecture 14 - Expected Values. Wednesday, October 4, Objectives: Introduce expected values.

MA 1125 Lecture 14 - Expected Values. Wednesday, October 4, Objectives: Introduce expected values. MA 5 Lecture 4 - Expected Values Wednesday, October 4, 27 Objectives: Introduce expected values.. Means, Variances, and Standard Deviations of Probability Distributions Two classes ago, we computed the

More information

Sampling Distribution

Sampling Distribution MAT 2379 (Spring 2012) Sampling Distribution Definition : Let X 1,..., X n be a collection of random variables. We say that they are identically distributed if they have a common distribution. Definition

More information

Chapter 6: Random Variables and Probability Distributions

Chapter 6: Random Variables and Probability Distributions Chapter 6: Random Variables and Distributions These notes reflect material from our text, Statistics, Learning from Data, First Edition, by Roxy Pec, published by CENGAGE Learning, 2015. Random variables

More information

Some Discrete Distribution Families

Some Discrete Distribution Families Some Discrete Distribution Families ST 370 Many families of discrete distributions have been studied; we shall discuss the ones that are most commonly found in applications. In each family, we need a formula

More information

M3S1 - Binomial Distribution

M3S1 - Binomial Distribution M3S1 - Binomial Distribution Professor Jarad Niemi STAT 226 - Iowa State University September 28, 2018 Professor Jarad Niemi (STAT226@ISU) M3S1 - Binomial Distribution September 28, 2018 1 / 28 Outline

More information

Learning Goals: * Determining the expected value from a probability distribution. * Applying the expected value formula to solve problems.

Learning Goals: * Determining the expected value from a probability distribution. * Applying the expected value formula to solve problems. Learning Goals: * Determining the expected value from a probability distribution. * Applying the expected value formula to solve problems. The following are marks from assignments and tests in a math class.

More information

Random Variables and Applications OPRE 6301

Random Variables and Applications OPRE 6301 Random Variables and Applications OPRE 6301 Random Variables... As noted earlier, variability is omnipresent in the business world. To model variability probabilistically, we need the concept of a random

More information

Bernoulli and Binomial Distributions

Bernoulli and Binomial Distributions Bernoulli and Binomial Distributions Bernoulli Distribution a flipped coin turns up either heads or tails an item on an assembly line is either defective or not defective a piece of fruit is either damaged

More information

A probability distribution shows the possible outcomes of an experiment and the probability of each of these outcomes.

A probability distribution shows the possible outcomes of an experiment and the probability of each of these outcomes. Introduction In the previous chapter we discussed the basic concepts of probability and described how the rules of addition and multiplication were used to compute probabilities. In this chapter we expand

More information

Chapter 4 Probability Distributions

Chapter 4 Probability Distributions Slide 1 Chapter 4 Probability Distributions Slide 2 4-1 Overview 4-2 Random Variables 4-3 Binomial Probability Distributions 4-4 Mean, Variance, and Standard Deviation for the Binomial Distribution 4-5

More information

Section 6.5. The Central Limit Theorem

Section 6.5. The Central Limit Theorem Section 6.5 The Central Limit Theorem Idea Will allow us to combine the theory from 6.4 (sampling distribution idea) with our central limit theorem and that will allow us the do hypothesis testing in the

More information

Section Distributions of Random Variables

Section Distributions of Random Variables Section 8.1 - Distributions of Random Variables Definition: A random variable is a rule that assigns a number to each outcome of an experiment. Example 1: Suppose we toss a coin three times. Then we could

More information

The Bernoulli distribution

The Bernoulli distribution This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this

More information

Section Distributions of Random Variables

Section Distributions of Random Variables Section 8.1 - Distributions of Random Variables Definition: A random variable is a rule that assigns a number to each outcome of an experiment. Example 1: Suppose we toss a coin three times. Then we could

More information

Problem A Grade x P(x) To get "C" 1 or 2 must be 1 0.05469 B A 2 0.16410 3 0.27340 4 0.27340 5 0.16410 6 0.05470 7 0.00780 0.2188 0.5468 0.2266 Problem B Grade x P(x) To get "C" 1 or 2 must 1 0.31150 be

More information

Section Random Variables and Histograms

Section Random Variables and Histograms Section 3.1 - Random Variables and Histograms Definition: A random variable is a rule that assigns a number to each outcome of an experiment. Example 1: Suppose we toss a coin three times. Then we could

More information

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Business and Economics Chapter 5 Continuous Random Variables and Probability Distributions Ch. 5-1 Probability Distributions Probability Distributions Ch. 4 Discrete Continuous Ch. 5 Probability

More information

Statistics (This summary is for chapters 17, 28, 29 and section G of chapter 19)

Statistics (This summary is for chapters 17, 28, 29 and section G of chapter 19) Statistics (This summary is for chapters 17, 28, 29 and section G of chapter 19) Mean, Median, Mode Mode: most common value Median: middle value (when the values are in order) Mean = total how many = x

More information

HHH HHT HTH THH HTT THT TTH TTT

HHH HHT HTH THH HTT THT TTH TTT AP Statistics Name Unit 04 Probability Period Day 05 Notes Discrete & Continuous Random Variables Random Variable: Probability Distribution: Example: A probability model describes the possible outcomes

More information

Central Limit Theorem

Central Limit Theorem Central Limit Theorem Lots of Samples 1 Homework Read Sec 6-5. Discussion Question pg 329 Do Ex 6-5 8-15 2 Objective Use the Central Limit Theorem to solve problems involving sample means 3 Sample Means

More information

5.2 Random Variables, Probability Histograms and Probability Distributions

5.2 Random Variables, Probability Histograms and Probability Distributions Chapter 5 5.2 Random Variables, Probability Histograms and Probability Distributions A random variable (r.v.) can be either continuous or discrete. It takes on the possible values of an experiment. It

More information

Measures of Variation. Section 2-5. Dotplots of Waiting Times. Waiting Times of Bank Customers at Different Banks in minutes. Bank of Providence

Measures of Variation. Section 2-5. Dotplots of Waiting Times. Waiting Times of Bank Customers at Different Banks in minutes. Bank of Providence Measures of Variation Section -5 1 Waiting Times of Bank Customers at Different Banks in minutes Jefferson Valley Bank 6.5 6.6 6.7 6.8 7.1 7.3 7.4 Bank of Providence 4. 5.4 5.8 6. 6.7 8.5 9.3 10.0 Mean

More information

Simple Random Sample

Simple Random Sample Simple Random Sample A simple random sample (SRS) of size n consists of n elements from the population chosen in such a way that every set of n elements has an equal chance to be the sample actually selected.

More information

Chapter 5 Student Lecture Notes 5-1. Department of Quantitative Methods & Information Systems. Business Statistics

Chapter 5 Student Lecture Notes 5-1. Department of Quantitative Methods & Information Systems. Business Statistics Chapter 5 Student Lecture Notes 5-1 Department of Quantitative Methods & Information Systems Business Statistics Chapter 5 Discrete Probability Distributions QMIS 120 Dr. Mohammad Zainal Chapter Goals

More information

Chapter 8. Variables. Copyright 2004 Brooks/Cole, a division of Thomson Learning, Inc.

Chapter 8. Variables. Copyright 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 8 Random Variables Copyright 2004 Brooks/Cole, a division of Thomson Learning, Inc. 8.1 What is a Random Variable? Random Variable: assigns a number to each outcome of a random circumstance, or,

More information

6.1 Discrete & Continuous Random Variables. Nov 4 6:53 PM. Objectives

6.1 Discrete & Continuous Random Variables. Nov 4 6:53 PM. Objectives 6.1 Discrete & Continuous Random Variables examples vocab Objectives Today we will... - Compute probabilities using the probability distribution of a discrete random variable. - Calculate and interpret

More information

Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the

Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the mean, use the CLT for the mean. If you are being asked to

More information