LECTURE 12 TUESDAY, 10 MARCH STA 291 Spring 2009 1.

Slides:



Advertisements
Similar presentations
STA291 Statistical Methods Lecture 13. Last time … Notions of: o Random variable, its o expected value, o variance, and o standard deviation 2.
Advertisements

Note 6 of 5E Statistics with Economics and Business Applications Chapter 4 Useful Discrete Probability Distributions Binomial, Poisson and Hypergeometric.
5.1 Sampling Distributions for Counts and Proportions.
1 Business 90: Business Statistics Professor David Mease Sec 03, T R 7:30-8:45AM BBC 204 Lecture 17 = Finish Chapter “Some Important Discrete Probability.
Bernoulli Distribution
THE BINOMIAL RANDOM VARIABLE. BERNOULLI RANDOM VARIABLE BernoulliA random variable X with the following properties is called a Bernoulli random variable:
Slide 1 Statistics Workshop Tutorial 7 Discrete Random Variables Binomial Distributions.
5-3 Binomial Probability Distributions
BCOR 1020 Business Statistics Lecture 10 – February 19, 2008.
Class notes for ISE 201 San Jose State University
381 Discrete Probability Distributions (The Binomial Distribution) QSCI 381 – Lecture 13 (Larson and Farber, Sect 4.2)
Lecture 7 Dustin Lueker.  Experiment ◦ Any activity from which an outcome, measurement, or other such result is obtained  Random (or Chance) Experiment.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 4 and 5 Probability and Discrete Random Variables.
Section 15.8 The Binomial Distribution. A binomial distribution is a discrete distribution defined by two parameters: The number of trials, n The probability.
The Binomial Distribution, Mean, and Standard Deviation IBHL/SL - Santowski.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 5-3 Binomial Probability Distributions.
Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics Statistics & Econometrics.
BINOMIAL DISTRIBUTION Success & Failures. Learning Goals I can use terminology such as probability distribution, random variable, relative frequency distribution,
HW adjustment Q70 solve only parts a, b, and d. Q76 is moved to the next homework and added-Q2 is moved to the next homework as well.
MTH 161: Introduction To Statistics
1 Bernoulli trial and binomial distribution Bernoulli trialBinomial distribution x (# H) 01 P(x)P(x)P(x)P(x)(1 – p)p ?
LECTURE 15 THURSDAY, 26 MARCH STA 291 Spring 2009.
Chapter 7 Lesson 7.5 Random Variables and Probability Distributions
October 15. In Chapter 6: 6.1 Binomial Random Variables 6.2 Calculating Binomial Probabilities 6.3 Cumulative Probabilities 6.4 Probability Calculators.
Bernoulli Trials Two Possible Outcomes –Success, with probability p –Failure, with probability q = 1  p Trials are independent.
BIA 2610 – Statistical Methods Chapter 5 – Discrete Probability Distributions.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Lecture Slides Elementary Statistics Eleventh Edition and the Triola.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Section 5-2 Random Variables.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 5 Discrete Random Variables.
LECTURE 14 THURSDAY, 12 March STA 291 Spring
King Saud University Women Students
Math b (Discrete) Random Variables, Binomial Distribution.
Chapter 5 Discrete Probability Distributions. Random Variable A numerical description of the result of an experiment.
LECTURE 19 THURSDAY, 29 OCTOBER STA 291 Fall
LECTURE 17 THURSDAY, 22 OCTOBER STA 291 Fall
The Binomial Distribution
4.2 Binomial Distributions
Statistics 3502/6304 Prof. Eric A. Suess Chapter 4.
Exam 2: Rules Section 2.1 Bring a cheat sheet. One page 2 sides. Bring a calculator. Bring your book to use the tables in the back.
Probability Distributions, Discrete Random Variables
1 Week NORMAL DISTRIBUTION BERNOULLI TRIALS BINOMIAL DISTRIBUTION EXPONENTIAL DISTRIBUTION UNIFORM DISTRIBUTION POISSON DISTRIBUTION.
6.2 BINOMIAL PROBABILITIES.  Features  Fixed number of trials (n)  Trials are independent and repeated under identical conditions  Each trial has.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by.
LECTURE 18 TUESDAY, 27 OCTOBER STA 291 Fall
Section 9-3 Probability. Probability of an Event if E is an event in a sample space, S, of equally likely outcomes, then the probability of the event.
LECTURE 20 TUESDAY, April 21 STA 291 Spring
Lecture 7 Dustin Lueker.  Experiment ◦ Any activity from which an outcome, measurement, or other such result is obtained  Random (or Chance) Experiment.
Welcome to MM305 Unit 3 Seminar Prof Greg Probability Concepts and Applications.
Chapter 6: Random Variables
LECTURE 16 TUESDAY, 20 OCTOBER STA 291 Fall
MATH 2311 Section 3.2. Bernoulli Trials A Bernoulli Trial is a random experiment with the following features: 1.The outcome can be classified as either.
Engineering Probability and Statistics - SE-205 -Chap 3 By S. O. Duffuaa.
Random Variables Lecture Lecturer : FATEN AL-HUSSAIN.
1. 2 At the end of the lesson, students will be able to (c)Understand the Binomial distribution B(n,p) (d) find the mean and variance of Binomial distribution.
Homework Questions. Binomial Theorem Binomial  Bi – means 2 – two outcomes  Win/lose, girl/boy, heads/tails  Binomial Experiments.
Slide 1 Copyright © 2004 Pearson Education, Inc. Chapter 5 Probability Distributions 5-1 Overview 5-2 Random Variables 5-3 Binomial Probability Distributions.
LECTURE 23 THURSDAY, 12 November
Engineering Probability and Statistics - SE-205 -Chap 3
Discrete Probability Distributions
3.4 The Binomial Distribution
STA 291 Spring 2008 Lecture 7 Dustin Lueker.
MATH 2311 Section 3.2.
LESSON 9: BINOMIAL DISTRIBUTION
Lecture 11: Binomial and Poisson Distributions
Elementary Statistics
Bernoulli Trials Two Possible Outcomes Trials are independent.
The Geometric Distributions
MATH 2311 Section 3.2.
Applied Statistical and Optimization Models
Presentation transcript:

LECTURE 12 TUESDAY, 10 MARCH STA 291 Spring

Homework Graded online homework is due Saturday (10/18) – watch for it to be posted today. Suggested problems from the textbook: 7.20, 7.30, 7.84, 7.92, 7.96, 7.106* 2

Expected Value of a Random Variable The Expected Value, or mean, of a random variable, X, is Mean = E(X)= Back to our previous example—what’s E(X)? X P(x)P(x)

Variance of a Random Variable Variance= Var(X) = Back to our previous example—what’s Var(X)? X P(x)P(x)

Bernoulli Trial Suppose we have a single random experiment X with two outcomes: “success” and “failure.” Typically, we denote “success” by the value 1 and “failure” by the value 0. It is also customary to label the corresponding probabilities as: P(success) = P(1) = p and P(failure) = P(0) = 1 – p = q Note: p + q = 1 5

Binomial Distribution I Suppose we perform several Bernoulli experiments and they are all independent of each other. Let’s say we do n of them. The value n is the number of trials. We will label these n Bernoulli random variables in this manner: X 1, X 2, …, X n As before, we will assume that the probability of success in a single trial is p, and that this probability of success doesn’t change from trial to trial. 6

Binomial Distribution II Now, we will build a new random variable X using all of these Bernoulli random variables: What are the possible outcomes of X? What is X counting? How can we find P( X = x )? 7

Binomial Distribution III We need a quick way to count the number of ways in which k successes can occur in n trials. Here’s the formula to find this value: Note: n C k is read as “n choose k.” 8

Binomial Distribution IV Now, we can write the formula for the binomial distribution: The probability of observing x successes in n independent trials is under the assumption that the probability of success in a single trial is p. 9

Using Binomial Probabilities Note: Unlike generic random variables where we would have to be given the probability distribution or calculate it from a frequency distribution, here we can calculate it from a mathematical formula. Helpful resources (besides your calculator): Excel: Table 1, pp. B-1 to B-5 in the back of your book EnterGives =BINOMDIST(4,10,0.2,FALSE) =BINOMDIST(4,10,0.2,TRUE)

Table 1, pp. B-1 to B-5 11

Binomial Probabilities We are choosing a random sample of n = 7 Lexington residents—our random variable, C = number of Centerpointe supporters in our sample. Suppose, p = P (Centerpointe support) ≈ 0.3. Find the following probabilities: a) P ( C = 2 ) b) P ( C < 2 ) c) P ( C ≤ 2 ) d) P ( C ≥ 2 ) e) P ( 1 ≤ C ≤ 4 ) What is the expected number of Centerpointe supporters,  C ? 12

Attendance Question #13 Write your name and section number on your index card. Today’s question: 13