Copyright © 2005 Pearson Education, Inc. Slide 7-1.

Slides:



Advertisements
Similar presentations
Section 6.3 ~ Probabilities With Large Numbers
Advertisements

Copyright © 2006 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Chapter 6: Probability : The Study of Randomness “We figured the odds as best we could, and then we rolled the dice.” US President Jimmy Carter June 10,
Section 7A: Fundamentals of Probability Section Objectives Define outcomes and event Construct a probability distribution Define subjective and empirical.
MM207 Statistics Welcome to the Unit 7 Seminar Prof. Charles Whiffen.
MAT 103 Probability In this chapter, we will study the topic of probability which is used in many different areas including insurance, science, marketing,
Warm up 1)We are drawing a single card from a standard deck of 52 find the probability of P(seven/nonface card) 2)Assume that we roll two dice and a total.
Probability.
Introduction to Probability and Risk
Probability and Expected Value 6.2 and 6.3. Expressing Probability The probability of an event is always between 0 and 1, inclusive. A fair coin is tossed.
Section 6.2 ~ Basics of Probability Introduction to Probability and Statistics Ms. Young.
Chapter 6 Probabilit y Vocabulary Probability – the proportion of times the outcome would occur in a very long series of repetitions (likelihood of an.
Copyright © 2015, 2011, 2008 Pearson Education, Inc. Chapter 7, Unit A, Slide 1 Probability: Living With The Odds 7.
Sets, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesProbability.
Sets, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesProbability.
5.1 Basic Probability Ideas
X of Z: MAJOR LEAGUE BASEBALL ATTENDANCE Rather than solving for z score first, we may be given a percentage, then we find the z score, then we find the.
Probability: Simple and Compound Independent and Dependent Experimental and Theoretical.
Copyright © Ed2Net Learning Inc.1. 2 Warm Up Use the Counting principle to find the total number of outcomes in each situation 1. Choosing a car from.
PROBABILITY. Counting methods can be used to find the number of possible ways to choose objects with and without regard to order. The Fundamental Counting.
Notes on PROBABILITY What is Probability? Probability is a number from 0 to 1 that tells you how likely something is to happen. Probability can be either.
Probability Section 7.1.
You probability wonder what we’re going to do next!
Copyright © 2015, 2011, 2008 Pearson Education, Inc. Chapter 7, Unit E, Slide 1 Probability: Living With The Odds 7.
Copyright © 2011 Pearson Education, Inc. Probability: Living with the Odds Discussion Paragraph 7B 1 web 59. Lottery Chances 60. HIV Probabilities 1 world.
Dependent and Independent Events. Events are said to be independent if the occurrence of one event has no effect on the occurrence of another. For example,
Seminar 7 MM150 Bashkim Zendeli. Chapter 7 PROBABILITY.
Warm-Up 1. Expand (x 2 – 4) 7 1. Find the 8 th term of (2x + 3) 10.
Copyright © 2011 Pearson Education, Inc. Probability: Living with the Odds.
MATH104 Ch. 11: Probability Theory part 3. Probability Assignment Assignment by intuition – based on intuition, experience, or judgment. Assignment by.
Chapter 7 Probability. 7.1 The Nature of Probability.
Copyright © 2011 Pearson Education, Inc. Probability: Living with the Odds Discussion Paragraph 7A 1 web 70. Blood Groups 71. Accidents 1 world 72. Probability.
Probability Section 7.1. What is probability? Probability discusses the likelihood or chance of something happening. For instance, -- the probability.
MM207 Statistics Welcome to the Unit 7 Seminar With Ms. Hannahs.
Dr. Fowler AFM Unit 7-8 Probability. Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall.
How likely is it that…..?. The Law of Large Numbers says that the more times you repeat an experiment the closer the relative frequency of an event will.
Chapter 12 Section 1 - Slide 1 Copyright © 2009 Pearson Education, Inc. AND.
Slide Copyright © 2009 Pearson Education, Inc. Chapter 7 Probability.
+ Chapter 5 Overview 5.1 Introducing Probability 5.2 Combining Events 5.3 Conditional Probability 5.4 Counting Methods 1.
Copyright © 2011 Pearson Education, Inc. Probability: Living with the Odds.
Unit 4 Section 3.1.
Copyright © 2015, 2011, 2008 Pearson Education, Inc. Chapter 7, Unit B, Slide 1 Probability: Living With The Odds 7.
© 2010 Pearson Education, Inc. All rights reserved Chapter 9 9 Probability.
Copyright © 2011 Pearson Education, Inc. Probability: Living with the Odds Discussion Paragraph 7B 1 web 59. Lottery Chances 60. HIV Probabilities 1 world.
Chance We will base on the frequency theory to study chances (or probability).
Copyright © 2009 Pearson Education, Inc. 6.3 Probabilities with Large Numbers LEARNING GOAL Understand the law of large numbers, use this law to understand.
Fundamentals of Probability
Probability and Sample Space…….
Essential Ideas for The Nature of Probability
Copyright © 2009 Pearson Education, Inc.
Chapter 4 Probability Concepts
From Randomness to Probability
Combining Probabilities
Combining Probabilities
6.2 Basics of Probability LEARNING GOAL
Probability: Living with the Odds
Probability: Living with the Odds
6.3 Probabilities with Large Numbers
Probability Probability underlies statistical inference - the drawing of conclusions from a sample of data. If samples are drawn at random, their characteristics.
The Law of Large Numbers
Probability: Living with the Odds
Probability: Living with the Odds
Chapter 14 – From Randomness to Probability
Section 6.2 Probability Models
Fundamentals of Probability
Probability: Living with the Odds
6.2 Basics of Probability LEARNING GOAL
Counting and Probability
5-8 Probability and Chance
Probability in Statistics
Presentation transcript:

Copyright © 2005 Pearson Education, Inc. Slide 7-1

Copyright © 2005 Pearson Education, Inc. Chapter 7

Copyright © 2005 Pearson Education, Inc. Slide 7-3 Probability Fundamentals 7-A P(2 heads with 2 tosses) = 1/4 {HH}/{HH, TT, HT, TH} P(no rain) = 1 – 40% = 60%

Copyright © 2005 Pearson Education, Inc. Slide 7-4 Expressing Probability 7-A The probability of an event, expressed as P(event), is alwaysbetween 0 and 1 (inclusive). A probability of 0 means the event is impossible and a probability of 1 means the event is certain Certain Likely Unlikely Chance Impossible 0 ≤ P(A) ≤ 1

Copyright © 2005 Pearson Education, Inc. Slide 7-5 Three Types of Probabilities 7-A empirical probabilitysubjective probability theoretical probability The chance of rolling a 4 is 1 out of 6.

Copyright © 2005 Pearson Education, Inc. Slide 7-6 Three Types of Probabilities 7-A empirical probabilitysubjective probability She’s a 92% free throw shooter for the season. theoretical probability

Copyright © 2005 Pearson Education, Inc. Slide 7-7 Three Types of Probabilities 7-A empirical probabilitysubjective probability There’s about a 70% chance she will go out on a date with me. theoretical probability

Copyright © 2005 Pearson Education, Inc. Slide 7-8 Outcomes Using Two Fair Dice 7-A Probabilities of rolling numbers 2 through 12

Copyright © 2005 Pearson Education, Inc. Slide 7-9 Outcomes and Events 7-A Question: Assuming equal chance of having a boy or girl at birth, what is the probability of having two girls and two boys in a family of four children? Answer: 37.5% Why?: There are 16 possible outcomes with 6 ways (events) to have two of each gender. 6/16 = 37.5%

Copyright © 2005 Pearson Education, Inc. Slide 7-10 Combining Probabilities 7-B Independent Events: P(A and B) = P(A) P(B) Example: P(rolling a 2 and drawing an Ace of Diamonds) Dependent Events:P(A and B) = P(A) P(B given A) Example: P(drawing a Queen and then drawing a 5) (assume we are not replacing the 1st card drawn) = 1/6 1/52 = 1/312 = 4/52 4/51 = 16/2652 = 4/663

Copyright © 2005 Pearson Education, Inc. Slide 7-11 More on Combining Probabilities 7-B Overlapping Events: P(A or B) = P(A) + P(B) – P(A and B) Example: What is the probability that in a standard shuffled deck of cards you will draw a queen or a club? = P(queen or club) = P(queen) + P(club) – P(queen and club) = 4/ /52 – 1/52 = 16/52 = 4/13 Non-overlapping Events: P(A or B) = P(A) + P(B) Example: Suppose you roll a single die. What is the probability of rolling either a 2 or a 3? P(2 or 3) = P(2) + P(3) = 1/6 + 1/6 = 2/6 = 1/3

Copyright © 2005 Pearson Education, Inc. Slide 7-12 More on Combining Probabilities 7-B Overlapping Events: P(A or B) = P(A) + P(B) – P(A and B) Non-overlapping Events: P(A or B) = P(A) + P(B)

Copyright © 2005 Pearson Education, Inc. Slide 7-13 At Least Once Rule 7-B P(at least one event A in n trials) = 1 – P(not event A in n trials) Example: What is the probability of having at least one girl in a family of four children? = 1 – P(not A) n

Copyright © 2005 Pearson Education, Inc. Slide 7-14 The Law of Averages 7-C If the process is repeated through many trials, the proportion of the trials in which event A occurs will be close to the probability P(A). The larger the number of trials the closer the proportion should be to P(A). (Sometimes this is referred to as the Law of Large Numbers.)

Copyright © 2005 Pearson Education, Inc. Slide 7-15 Expected Value 7-C Consider two events, each with its own value and probability. Expected Value = (event 1 value) (event 1 probability) + (event 2 value) (event 2 probability) Example: Suppose an automobile insurance company sells an insurance policy with an annual premium of $200. Based on data from past claims, the company has calculated the following probabilities: An average of 1 in 50 policyholders will file a claim of $2,000 An average of 1 in 20 policyholders will file a claim of $1,000 An average of 1 in 10 policyholders will file a claim of $500 Assuming that the policyholder could file any of the claims above, what is the expected value to the company for each policy sold?

Copyright © 2005 Pearson Education, Inc. Slide 7-16 Expected Value 7-C Let the $200 premium be positive (income) with a probability of 1 since there will be no policy without receipt of the premium. The insurance claims will be negatives (expenses). The expected value is = $200(1) + -$2000(1/50) + -$1000(1/20) + -$500(1/10) = $60 (Suggesting that if the company sold many, many policies, on average, the return per policy is a positive $60.) Consider two events, each with its own value and probability. Expected Value = (event 1 value) (event 1 probability) + (event 2 value) (event 2 probability)

Copyright © 2005 Pearson Education, Inc. Slide 7-17 The Gambler’s Fallacy 7-C The gambler's fallacy is the mistaken belief that a streak of bad luck makes a person "due" for a streak of good luck. Example (continued losses): Suppose you are playing the coin toss game, in which you win $1 for heads and lose $1 for tails. After 100 tosses you are $10 in the hole because you flip perhaps 45 heads and 55 tails. The empirical probability is.45 for heads.... So you keep playing the game: With 1,000 tosses perhaps you get 490 heads and 510 tails. The empirical ratio is now.49 but you are $20 in the hole at this point. Because you know the Law of Averages helps us to know that the eventual theoretical probability is.50, you decide to play the game just a little more: After 10,000 tosses perhaps you flip 4,985 heads and 5,015 tails. The empirical ratio is now.4985 but you are $30 in the hole even though the ratio is approaching the hypothetical 50%.

Copyright © 2005 Pearson Education, Inc. Slide 7-18 Life Expectancy at Birth 7-D

Copyright © 2005 Pearson Education, Inc. Slide 7-19 Arrangements with Repetition 7-E If we make r selections from a group of n choices, a total of n r different arrangements are possible.

Copyright © 2005 Pearson Education, Inc. Slide 7-20 The Permutations Formula 7-E If we make r selections from a group of n choices, the number of permutations (arrangements in which order matters) is

Copyright © 2005 Pearson Education, Inc. Slide 7-21 The Permutations Formula 7-E If we make r selections from a group of n choices, the number of permutations (arrangements in which order matters) is Question: If an international track event has 8 athletes participating and three medals (gold, silver and bronze) are to be awarded, how many different orderings of the top three athletes are possible? Answer: 336 Why?

Copyright © 2005 Pearson Education, Inc. Slide 7-22 The Combinations Formula 7-E If we make r selections from a group of n items, the number of combinations (arrangements in which order does not matter) is Question: If a committee of 3 people are needed out of 8 possible candidates and there is not any distinction between committee members, how many possible committees would there be? Answer: 56 Why?

Copyright © 2005 Pearson Education, Inc. Slide 7-23 Birthday Coincidence 7-E What are the probabilities that someone in a room of x people will have a birthday in common with someone else in that room? 1 0 y = Probabilities x = People in Room

Copyright © 2005 Pearson Education, Inc. Slide 7-24 Birthday Coincidence 7-E What are the probabilities that someone in a room of x people will have a birthday in common with someone else in that room? 1 0 y = Probabilities x = People in Room

Copyright © 2005 Pearson Education, Inc. Slide 7-25 Birthday Coincidence 7-E What are the probabilities that someone in a room of x people will have a birthday in common with someone else in that room? 1 0 y = Probabilities x = People in Room

Copyright © 2005 Pearson Education, Inc. Slide 7-26 Birthday Coincidence 7-E What are the probabilities that someone in a room of x people will have a birthday in common with someone else in that room? 1 0 y = Probabilities x = People in Room

Copyright © 2005 Pearson Education, Inc. Slide 7-27 Birthday Coincidence 7-E What are the probabilities that someone in a room of x people will have a birthday in common with someone else in that room? 1 0 y = Probabilities x = People in Room

Copyright © 2005 Pearson Education, Inc. Slide 7-28 Birthday Coincidence 7-E What are the probabilities that someone in a room of x people will have a birthday in common with someone else in that room? 1 0 y = Probabilities x = People in Room How many people in the room would be required for 100% certainty?