Chapter 4 Elementary Probability Theory. What is Probability?  Probability is a numerical measure between 0 and 1 that describes the likelihood that.

Slides:



Advertisements
Similar presentations
Probability Unit 3.
Advertisements

Probability Simple Events
Presentation 5. Probability.
Randomness and Probability
Probability, Part III.
Section 5.1 and 5.2 Probability
Chapter 4 Probability and Probability Distributions
COUNTING AND PROBABILITY
© 2011 Pearson Education, Inc
From Randomness to Probability
Unit 4 Sections 4-1 & & 4-2: Sample Spaces and Probability  Probability – the chance of an event occurring.  Probability event – a chance process.
Chapter 3 Probability.
NIPRL Chapter 1. Probability Theory 1.1 Probabilities 1.2 Events 1.3 Combinations of Events 1.4 Conditional Probability 1.5 Probabilities of Event Intersections.
Elementary Probability Theory
Statistics for Managers Using Microsoft Excel, 5e © 2008 Pearson Prentice-Hall, Inc.Chap 4-1 Statistics for Managers Using Microsoft® Excel 5th Edition.
Applying the ideas: Probability
CHAPTER 5 PROBABILITY. CARDS & DICE BLACKRED CLUBSPADEDIAMONDHEARTTOTAL ACE11114 FACE CARD (K, Q, J) NUMBERED CARD (1-9) TOTAL13 52.
Chapter 15: Probability Rules!
Probability Chapter 3. § 3.1 Basic Concepts of Probability.
Statistics Chapter 3: Probability.
Chapter 4 Probability See.
Counting Principles (Permutations and Combinations )
Overview 5.1 Introducing Probability 5.2 Combining Events
Elementary Probability Theory
Chapter 1 Basics of Probability.
5.1 Basic Probability Ideas
Probability Denoted by P(Event) This method for calculating probabilities is only appropriate when the outcomes of the sample space are equally likely.
Conditional Probabilities Multiplication Rule Independence.
1  Event - any collection of results or outcomes from some procedure  Simple event - any outcome or event that cannot be broken down into simpler components.
Copyright © Cengage Learning. All rights reserved. Elementary Probability Theory 5.
1 Chapter 3 Probability 3-1 Fundamentals 3-2 Addition Rule 3-3 Multiplication Rule: Basics 3-4 Multiplication Rule: Complements and Conditional Probability.
AP Statistics Exam Review
Chapter 4 Correlation and Regression Understanding Basic Statistics Fifth Edition By Brase and Brase Prepared by Jon Booze.
Warm-Up 1. What is Benford’s Law?
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.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin A Survey of Probability Concepts Chapter 5.
Seminar 7 MM150 Bashkim Zendeli. Chapter 7 PROBABILITY.
Larson/Farber Ch. 3 Weather forecast Psychology Games Sports 3 Elementary Statistics Larson Farber Business Medicine Probability.
Chapter 7 Probability. 7.1 The Nature of Probability.
7th Probability You can do this! .
Copyright © 2005 Pearson Education, Inc. Slide 7-1.
Copyright © Cengage Learning. All rights reserved. Elementary Probability Theory 5.
From Randomness to Probability Chapter 14. Dealing with Random Phenomena A random phenomenon is a situation in which we know what outcomes could happen,
MM207 Statistics Welcome to the Unit 7 Seminar With Ms. Hannahs.
QR 32 Section #6 November 03, 2008 TA: Victoria Liublinska
Math 30-2 Probability & Odds. Acceptable Standards (50-79%)  The student can express odds for or odds against as a probability determine the probability.
Chapter 12 Section 1 - Slide 1 Copyright © 2009 Pearson Education, Inc. AND.
Chapter 4 Probability, Randomness, and Uncertainty.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.
+ Chapter 5 Overview 5.1 Introducing Probability 5.2 Combining Events 5.3 Conditional Probability 5.4 Counting Methods 1.
Week 21 Rules of Probability for all Corollary: The probability of the union of any two events A and B is Proof: … If then, Proof:
Unit 4 Section 3.1.
Chapter 6 - Probability Math 22 Introductory Statistics.
3-1 Copyright © 2014, 2011, and 2008 Pearson Education, Inc.
Probability. Definitions Probability: The chance of an event occurring. Probability Experiments: A process that leads to well- defined results called.
6.2 – Probability Models It is often important and necessary to provide a mathematical description or model for randomness.
AP Statistics From Randomness to Probability Chapter 14.
Section 5.1 and 5.2 Probability
Essential Ideas for The Nature of Probability
Elementary Probability Theory
Chapter 4 Probability Concepts
Chapter 6 6.1/6.2 Probability Probability is the branch of mathematics that describes the pattern of chance outcomes.
What is Probability? Quantification of uncertainty.
Chapter 4 Section 4.1 Statistics
Elementary Statistics
Probability Probability underlies statistical inference - the drawing of conclusions from a sample of data. If samples are drawn at random, their characteristics.
Chapter 3 Probability.
Chapter 4 Basic Probability.
Honors Statistics From Randomness to Probability
Presentation transcript:

Chapter 4 Elementary Probability Theory

What is Probability?  Probability is a numerical measure between 0 and 1 that describes the likelihood that an event will occur. Probabilities closer to 1 indicate that the event is more likely to occur. Probabilities closer to 0 indicated that the event is less likely to occur.

Note:  P(A) = probability of event A; you read it as “P of A”.  P(A)=1, the event A is certain to occur  P(A)=0, the event A is certain to not occur  Binary number works like this…1 means it’s true, 0 means false.

See if you understand this statement:  “There are only 10 types of people in the world: those who understand binary, and those who don't”

Anyways…Probability Assignments

Examples  Intuition – NBA announcer claims that Kobe makes 84% of his free throws. Based on this, he will have a high chance of making his next free throw.  Relative frequency – Auto Fix claims that the probability of Toyota breaking down is.10 based on a sample of 500 Toyota of which 50 broke down.  Equally likely outcome - You figure that if you guess on a SAT test, the probability of getting it right is.20

Group Work  Create a situation for each of the probability assignments. (intuition, relative frequency, equally likely outcome)  Show me

Law of Large Numbers  In the long run, as the sample size increases, the relative frequencies of outcomes get closer to the theoretical (or actual) probability value  Example: The more numbers you ask, the more likelihood that P(getting a girl’s real number)=1

Law of Large Numbers examples:  The more numbers you ask, the more likelihood that P(getting a (hot) girl’s real number)=1  Then after collecting all the numbers, the more girls you ask out on a date, the more likelihood that P(getting a date)=1

Some other real life examples:  Casino (the more you play, the more you lose)  Insurance (the more people you insure, the less the likelihood the company have to pay for the insurance benefits)

Statistical Experiment  Statistical experiment or statistical observation can be thought of as any random activity that results in a definite outcome  An event is a collection of one or more outcomes of a statistical experiment or observation  Simple event is one particular outcome of a statistical experiment  The set of all simple events constitutes the sample space of an experiment

Example: Blue eyes vs Brown eyes (relating to biology)  Brown eyes’ genotype is Bb or BB  Blue eyes’ genotype is bb  If your Dad has Brown eyes (and his dad has blue eyes) and your Mom has blue eyes, what’s the probability that you have blue eyes?

Answer (using sample space) Bb bBbbb bBbbb Dad Mom

Group Work (use sample space):  You are running out of time in a true/false quiz. You only have 4 questions left! How should you guess?  P(all false)= P(3 false)=  P(all true)= P(2 false)=  P(1 true)= P(1 false)=  P(2 true)=  P(3 true)=

Answer  Your sample space should have 16 different combinations  P(all false)= 1/16 P(3 false)= 4/16  P(all true)= 1/16 P(2 false)= 6/16  P(1 true)= 4/16 P(1 false)= 4/16  P(2 true)= 6/16  P(3 true)= 4/16  You will probably choose 2 true and 2 false TTTTFTTTTFTTTTFT TTTFFFTTFTFTFTTF TFFTTFTFTTFFFFFT TFFFFTFFFFTFFFFF

Note:

Example:  P(getting A in Mr. Liu’s class)+P(not getting A in Mr. Liu’s class) =1  P(getting A in Mr. Liu’s class)=.15  What’s the P(not getting A in Mr. Liu’s class)?

Answer  P(not getting A in Mr. Liu’s class)=.85

Group Work  P(having a date on a Friday)=1/7  What’s the P(not having a date on a Friday)?

Answer  6/7

Homework Practice:  Pg 130 #1-6 (all), 7-13 (odd)

Compound Events

Consider these two situation  P(5 on 1 st die and 5 on 2 nd die)  P(ace on 1 st card and ace on 2 nd card)  What is the difference between these two situation?

The answer  In the first situation, the first result does not effect the outcome of the 2 nd result.  In the second situation, the first result does effect the outcome of the 2 nd result.

Independent  Two events are independent if the occurrence or nonoccurrence of one does not change the probability that the other will occur  What does it mean if two events are dependent?

Multiplication rule for independent events

What if the events are dependent?  Then we must take into account the changes in the probability of one event caused by the occurrence of the other event.

Sample Space A B A and B

General multiplication rule for any events

Conditional Probability Example:  Your friend has 2 children. You learned that she has a boy named Rick. What is the probability that Rick’s sibling is a boy?  Take a guess

Answer

Group Work  A machine produce parts that’s either good (90%), slightly defective (2%) or obliviously broken (8%). The parts gets through an automatic inspection machine that is able to find the oblivious broken parts and throw them away. What is the probability of the quality part that make it through and get shipped?

Answer

Relax!  Conditional Probability can be very intriguing and complicated. We won’t go into any more in depth…..or maybe….

Note: Very important to understand about probability is that are the events dependent or independent.

Group Work  Suppose you are going to throw 2 fair dice. What is the probability of getting a 3 on each die?  A) Is this situation independent or dependent?  B) Create all the sample space (all the potential outcomes)  C) What is the probability?

Answer  A) Independent because one event does not affect the second event  B) You should have 36 total outcomes  C) 1/36

Group Work  I took a die away. Now you only have ONE die! Again you toss the die twice. What is the probability of getting a 1 on the first and 4 on the second try?

Answer  It is still an independent event!  1/36

Note:  The last two examples are considered multiplication rule, independent events.

Group Work  Mr. Liu has a 80% probability of teaching statistics next year. Mr. Riley has a 15% probability of teaching statistics next year. What is the probability that both Mr. Liu and Mr. Riley teach statistics next year?

Answer .8*.15=.12 or 12% probability

Now comes the dependent events  Suppose you have 100 Iphones. The defective rate of iphone is 10%. What is the probability that you choose two iphones and both are defective?

Answer

Group work  What is the probability of getting tail and getting a 3 on a die and getting an ace in a deck of cards?

Answer

Addition Rules  You use addition when you want to consider the possibility of one event OR another occurring

Example:

Group Work: And or Or?  1) Satisfying the humanities requirement by taking a course in the history of Japan or by taking a course in classical literature  2) Buying new tires and aligning the tires  3) Getting an A in math but also in biology  4) Having at least one of these pets: cat, dog, bird, rabbit

Answer  1) or  2) and  3) and  4) or

Note:  Two events are mutually exclusive or disjoint if they cannot occur together. In particular, events A and B are mutually exclusive if P(A and B)=0

Addition rule for mutually exclusive events A and B  P(A or B)=P(A)+P(B)  General Rule for any events A and B  P(A or B)=P(A)+P(B)-P(A and B)  Remember in mutually exclusive events P(A and B)=0

Group Example: Employee type Democrat (D) Republican (R) Independent (I) Row Total Executive (E) Production Worker (PW) Column Total grand total

Answer

Homework Practice:  Pg 146 #1,2,5,7,9,10,14,19

Conditional Probability extension  Bayes’s theorem: It uses conditional probabilities to adjust calculations so that we can accommodate new relevant information.  The special case where event B is partitioned into only two mutually exclusive events.

Formula

Example (Real Life Extension): How accurate is “one” pregnancy test?  Supposedly pregnancy test strip claims it is 99% accurate.  false-positive and false-negative NOT PregnantPregnant!!!! Pregnancy Test Negative True NegativeFalse Negative Pregnancy Test Positive False Positive True Positive!!

Procedure

Trees and Counting Techniques

Tree diagram:  A tree diagram shows all the possible outcomes of an event.  All possible outcomes of an event are shown by a tree diagram.

Example using tree diagrams:  If a coin and a dice are tossed simultaneously, what is the probability of getting tail and even number?

Answer  1/4

Group Work using tree diagram:  You are on a sports team. What is the probability that out of three games, you win two of them?

Tree Diagram with Probability:  You have 7 balls, 4 are blue and 3 are green. What is the probability that when you pick the balls, you get green on 1 st and blue one 2 nd ?

Shown in class

Group Work:  You make free throws 85% of the time. What is the probability of making at least one out of the three?

 P(make 1 out of 3)=99.66% of the time

Factorials  ! Means factorial  0!=1  1!=1  n!=(n)(n-1)(n-2)(n-3)….

What is 6! ?  6!=6*5*4*3*2*1=720

Combination vs Permutation

Combination:  Order does not matter! It is not important  If you have 3,1,2, it is the same as 1,3,2 because they all have 1,2,3  Different Arrangement of things  Combination is choosing

Permutation:  Order does matter! It is important  Note: Permutation is position

Combination vs Permutation continue:  Both of them break down into two different category:  Combination with repetition (ex: ice cream scoops)  Combination without repetition (ex: lottery)  Permutation with repetition (ex: lock in locker room)  Permutation without repetition (ex: marathon race)

Reading activity:  -permutations.html -permutations.html  Read the different examples

Whew that was a lot of reading…now do some examples.  You have 8 people, what are the number of possible ordered seating arrangement for 5 chairs

Answer

Group Work: Gamestop has 25 new games this month and you decided to buy 5 of them. How many different arrange of game you can have?

Answer

Group Work: MEGA Million  What is the chance of winning the jackpot for MEGA Million?  You have 5 slots + 1 slot for MEGA number  The first 5 slots are numbers between 1-75, mega number is number between 1-15

Answer

Group Work: Powerball lottery  What is the chance of winning the Jackpot for Powerball?  You have 5 slots+1 slot for Power number  The first 5 slots are numbers between 1-56, powerball slot is number between 1-35

Answer  1 in 175,223,510

Interesting fact  Even though it’s harder to win the Jackpot, for overall winning chance, you have more chance for MEGA million than Powerball

Homework Practice  Pg 160 #1-27 every other odd