Chapter 17: The Expected Value and Standard Error

Similar documents
Discrete Random Variables Day 1

There is no class tomorrow! Have a good weekend! Scores will be posted in Compass early Friday morning J

a) Getting 10 +/- 2 head in 20 tosses is the same probability as getting +/- heads in 320 tosses

Math 147 Lecture Notes: Lecture 21

Midterm 2 Practice Problems

LISTING THE WAYS. getting a total of 7 spots? possible ways for 2 dice to fall: then you win. But if you roll. 1 q 1 w 1 e 1 r 1 t 1 y

Expected Value, continued

Casino Lab AP Statistics

Statistics Laboratory 7

Stat 20: Intro to Probability and Statistics

Module 5: Probability and Randomness Practice exercises

Outcome X (1, 1) 2 (2, 1) 3 (3, 1) 4 (4, 1) 5 {(1, 1) (1, 2) (1, 3) (1, 4) (1, 5) (1, 6) (6, 1) (6, 2) (6, 3) (6, 4) (6, 5) (6, 6)}

Chapter 7 Homework Problems. 1. If a carefully made die is rolled once, it is reasonable to assign probability 1/6 to each of the six faces.

Ex 1: A coin is flipped. Heads, you win $1. Tails, you lose $1. What is the expected value of this game?

6. a) Determine the probability distribution. b) Determine the expected sum of two dice. c) Repeat parts a) and b) for the sum of

The Teachers Circle Mar. 20, 2012 HOW TO GAMBLE IF YOU MUST (I ll bet you $5 that if you give me $10, I ll give you $20.)

Make better decisions. Learn the rules of the game before you play.

Statistics 1040 Summer 2009 Exam III

Exam III Review Problems

STATION 1: ROULETTE. Name of Guesser Tally of Wins Tally of Losses # of Wins #1 #2

November 11, Chapter 8: Probability: The Mathematics of Chance

CSC/MTH 231 Discrete Structures II Spring, Homework 5

SIC BO ON THE MULTI TERMINALS

Section 6.1 #16. Question: What is the probability that a five-card poker hand contains a flush, that is, five cards of the same suit?

Moore, IPS 6e Chapter 05

Random Variables. A Random Variable is a rule that assigns a number to each outcome of an experiment.

Random Variables. Outcome X (1, 1) 2 (2, 1) 3 (3, 1) 4 (4, 1) 5. (6, 1) (6, 2) (6, 3) (6, 4) (6, 5) (6, 6) }

STAT 311 (Spring 2016) Worksheet W8W: Bernoulli, Binomial due: 3/21

Presentation by Toy Designers: Max Ashley

If a fair coin is tossed 10 times, what will we see? 24.61% 20.51% 20.51% 11.72% 11.72% 4.39% 4.39% 0.98% 0.98% 0.098% 0.098%

Math Steven Noble. November 24th. Steven Noble Math 3790

Math March 12, Test 2 Solutions

The game of poker. Gambling and probability. Poker probability: royal flush. Poker probability: four of a kind

Here are two situations involving chance:

or More Events Activities D2.1 Open and Shut Case D2.2 Fruit Machines D2.3 Birthdays Notes for Solutions (1 page)

1. Decide whether the possible resulting events are equally likely. Explain. Possible resulting events

1. Determine whether the following experiments are binomial.

Name: Practice Exam 3B. April 16, 2015

Lecture 21/Chapter 18 When Intuition Differs from Relative Frequency

Review Questions on Ch4 and Ch5

Exam #1. Good luck! Page 1 of 7

University of California, Berkeley, Statistics 20, Lecture 1. Michael Lugo, Fall Exam 2. November 3, 2010, 10:10 am - 11:00 am

Math 106 Lecture 3 Probability - Basic Terms Combinatorics and Probability - 1 Odds, Payoffs Rolling a die (virtually)

Math Exam 2 Review. NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5.

Math Exam 2 Review. NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5.

Compute P(X 4) = Chapter 8 Homework Problems Compiled by Joe Kahlig

1. A factory makes calculators. Over a long period, 2 % of them are found to be faulty. A random sample of 100 calculators is tested.

Math 4653, Section 001 Elementary Probability Fall Week 3 Worksheet

MAT Midterm Review

Lenarz Math 102 Practice Exam # 3 Name: 1. A 10-sided die is rolled 100 times with the following results:

* How many total outcomes are there if you are rolling two dice? (this is assuming that the dice are different, i.e. 1, 6 isn t the same as a 6, 1)

1) What is the total area under the curve? 1) 2) What is the mean of the distribution? 2)

An Adaptive-Learning Analysis of the Dice Game Hog Rounds

Suppose Y is a random variable with probability distribution function f(y). The mathematical expectation, or expected value, E(Y) is defined as:

Basic Probability & Statistics Exam 2 { Part I { Sections (Chapter 4, Chapter 5) March 19, 2009

Probabilities and Probability Distributions

Math 4610, Problems to be Worked in Class

HOMEWORK 3 Due: next class 2/3

North Seattle Community College Winter ELEMENTARY STATISTICS 2617 MATH Section 05, Practice Questions for Test 2 Chapter 3 and 4

5. Aprimenumberisanumberthatisdivisibleonlyby1anditself. Theprimenumbers less than 100 are listed below.

MATH CALCULUS & STATISTICS/BUSN - PRACTICE EXAM #2 - FALL DR. DAVID BRIDGE

c. If you roll the die six times what are your chances of getting at least one d. roll.

18.S34 (FALL, 2007) PROBLEMS ON PROBABILITY

#3. Let A, B and C be three sets. Draw a Venn Diagram and use shading to show the set: PLEASE REDRAW YOUR FINAL ANSWER AND CIRCLE IT!

, x {1, 2, k}, where k > 0. (a) Write down P(X = 2). (1) (b) Show that k = 3. (4) Find E(X). (2) (Total 7 marks)

Math : Probabilities

Due Friday February 17th before noon in the TA drop box, basement, AP&M. HOMEWORK 3 : HAND IN ONLY QUESTIONS: 2, 4, 8, 11, 13, 15, 21, 24, 27

Probability Essential Math 12 Mr. Morin

Rosen, Discrete Mathematics and Its Applications, 6th edition Extra Examples

Something to Think About

2. The value of the middle term in a ranked data set is called: A) the mean B) the standard deviation C) the mode D) the median

If a fair coin is tossed 10 times, what will we see? 24.61% 20.51% 20.51% 11.72% 11.72% 4.39% 4.39% 0.98% 0.98% 0.098% 0.098%

Probability Paradoxes

Chapter 8: Probability: The Mathematics of Chance

Name: Final Exam May 7, 2014

Guide. Odds. Understanding. The THE HOUSE ADVANTAGE

Mrs. Daniel- AP Stats Chapter 6 MC Practice

23 Applications of Probability to Combinatorics

Basic Probability Ideas. Experiment - a situation involving chance or probability that leads to results called outcomes.

Probability with Set Operations. MATH 107: Finite Mathematics University of Louisville. March 17, Complicated Probability, 17th century style

Name: Exam 01 (Midterm Part 2 take home, open everything)

Mini-Lecture 6.1 Discrete Random Variables

EE 126 Fall 2006 Midterm #1 Thursday October 6, 7 8:30pm DO NOT TURN THIS PAGE OVER UNTIL YOU ARE TOLD TO DO SO

4.3 Rules of Probability

BAYESIAN STATISTICAL CONCEPTS

Cycle Roulette The World s Best Roulette System By Mike Goodman

HARD 1 HARD 2. Split the numbers above into three groups of three numbers each, so that the product of the numbers in each group is equal.

Student activity sheet Gambling in Australia quick quiz

Week in Review #5 ( , 3.1)

OUTSIDE IOWA, CALL

Probability: Anticipating Patterns

The student will explain and evaluate the financial impact and consequences of gambling.

Math 10 Homework 2 ANSWER KEY. Name: Lecturer: Instructions

MATH 1115, Mathematics for Commerce WINTER 2011 Toby Kenney Homework Sheet 6 Model Solutions

Probability MAT230. Fall Discrete Mathematics. MAT230 (Discrete Math) Probability Fall / 37

KS specimen papers

Chapter 11. Sampling Distributions. BPS - 5th Ed. Chapter 11 1

A Mathematical Analysis of Oregon Lottery Keno

Introduction to Auction Theory: Or How it Sometimes

Expected Value(Due by EOC Nov. 1)

Transcription:

Chapter 17: The Expected Value and Standard Error Think about drawing 25 times, with replacement, from the box: 0 2 3 4 6 Here s one set of 25 draws: 6 0 4 3 0 2 2 2 0 0 3 2 4 2 2 6 0 6 3 6 3 4 0 6 0, sum = 66 Here s another: 0 3 2 6 2 2 4 4 0 2 2 6 6 2 2 3 0 3 0 0 3 2 0 4 3, sum = 61 and another: 4 0 3 0 4 4 4 4 4 0 0 6 2 3 6 2 0 2 2 0 0 0 0 6 6, sum = 62

What can we say about the sum of 25 draws from the box? The expected value for the sum of the draws is EVsum = (number of draws) (ave box ) The standard error for the sum of the draws is SEsum = ( number of draws) (SD box ) ave box and SD box are the average and the SD of the tickets in the box.

Example: The sum of 25 draws from the box: 0 2 3 4 6 ave box = 3 SD box = 2 CALCULATOR! so EVsum = (number of draws) (ave box ) = (25)(3) = 75 and SEsum = number of draws (SD box ) = ( 25 ) (2) = 10

What do the EVsum and SEsum tell us? Suppose we take many, many samples of size 25 and look at the sum of the draws: 0 2 4 4 3 4 4 0 4 4 4 3 4 2 6 3 2 2 3 6 4 2 4 6 4 sum = 84 4 0 6 0 2 6 4 2 0 4 0 2 4 4 3 0 6 0 6 6 4 4 4 4 3 sum = 78 6 2 6 3 3 3 4 6 4 0 0 3 3 0 2 4 2 2 6 3 0 3 0 4 2 sum = 71 3 0 2 4 4 0 0 4 4 0 2 3 2 2 3 0 6 2 3 3 2 6 4 3 4 sum = 66 2 2 6 4 2 2 3 3 0 3 3 2 4 3 4 0 3 3 3 4 0 2 0 2 2 sum = 62. 6 6 0 0 2 4 4 4 4 4 4 0 6 0 6 0 0 6 6 3 6 4 6 0 4 sum = 85 2 0 3 0 6 3 3 0 4 2 0 4 4 4 6 6 6 4 4 4 6 2 4 6 3 sum = 86 6 6 6 4 0 2 4 4 3 4 4 3 0 2 2 3 6 6 0 0 3 4 3 0 4 sum = 79 3 0 0 0 3 3 2 6 3 3 2 6 6 3 4 3 6 2 4 4 0 6 6 6 0 sum = 81 3 3 6 6 2 0 2 0 0 6 4 6 6 2 6 3 0 3 3 0 4 6 0 3 6 sum = 80 Many samples of 25 draws

The first 10 sums: 84 78 71 66 62 60 77 79 76 80, ave = 73.3, SD = 7.7 10 sums have ave = 73.3, SD = 7.7 100 sums have ave = 74.5, SD of 8.9. 1000 sums have ave = 75.0, SD of 9.7. 10000 sums have ave = 75.0, SD of 10.0. EVsum tells us the average of the SUM OF THE DRAWS SEsum tells us the SD of the SUM OF THE DRAWS. If we repeat the 25 draws MANY times, we expect the SUM OF THE DRAWS to be around EVsum, with an SD of SEsum.

Example 1. Consider the sum of 100 draws from the box 1 2 3 4 5 6 What is the expected value of the sum? Its SE?

Example 2. (See Chapter 16, Example 5) You play a game in which you roll a die 10 times and get paid the amount shown on the die (each time). How much do you expect to win? Give or take how much?

Example 3. (See Chapter 16, Example 6) You play a game in which you roll a die 10 times. Each time a 6 occurs, you win $10, otherwise you lose $1. How much do you expect to win? Give or take how much?

Example 4. (See Chapter 16, Example 8) A multiplechoice test has 20 questions, each with 4 possible choices. Each correct answer is worth 5 points, and for each incorrect answer you lose 2 points. If you guess all the answers, what do you expect your test score to be? Give or take how much?

Example 5. A true/false test has 20 questions. Each correct answer scores 5 points; each wrong answer scores 0 points. If you guess all the answers, what is your expected score? Give-or-take how much?

Example 6. Bet $1 on 17 100 times in roulette. How much do you expect to win? Give-or-take how much?

Example 7. Bet $100 on 17 once in roulette. What are the expected value and the SE for the amount you win?

Example 8. Bet $1 on red 100 times in roulette. How much money do you expect to win? Give-or-take how much?

Example 9. A child plays a game of chance in which she has a 30% chance of scoring 5 points, a 50% chance of scoring 3 points and a 20% chance of scoring 1 point. If she plays the game 20 times, what is her expected score? Give or take how much?

Classifying and Counting If we want to count how many times something happens, the box has 0 s and 1 s. The 1 s represent the thing we are counting, and the 0 s represent everything else. Here is the box for rolling a die and counting the total number of spots we get: 1 2 3 4 5 6 Here is the box for rolling a die and counting how many 6 s we get: 0 0 0 0 0 1

Example 10. Bet $1 on red 100 times in roulette. How many times do you expect to win? Give-or-take how many?

The Normal Curve For a large number of draws, the sum of the draws will follow the normal curve with average EVsum and standard deviation SEsum. In particular, 68% of the time the sum of the draws will be between EVsum SEsum and EVsum + SEsum. 95% of the time the sum of the draws will be between EVsum 2 (SEsum) and EVsum + 2 (SEsum). We can use the normal curve to find the chance that the sum of the draws is in a region of interest. We use EVsum and SEsum to get standard units.

Note: It does not matter what is in the box. The histogram for the tickets in the box does not have to follow the normal curve. The sum of the draws will follow the normal curve, even if the tickets in the box are 0 s and 1 s! In fact, we don t even need to know what is in the box, we just need to know the average and the SD of the box.

Example 11. Roll a die 100 times and count the total number of spots. What is the chance the total number of spots is more than 367?

Example 12. Bet $1 on red 100 times in roulette. What is the chance you win more than $10? What is the chance you lose more than $10? What is the chance you come out ahead?

Example 13. A multiple-choice test has 20 questions, each with 4 choices. Each correct answer scores 5 points; each wrong answer makes you lose 2 points. If you guess all the answers, what is the chance you score more than 20 points?

Sampling without replacement from a LARGE population is just like sampling with replacement. Example 14. A large crop of apples has an average weight of 4.3 oz with an SD of 1.5 oz. I choose 100 apples at random. What s the chance the total weight is less than 25 pounds?

Example 15. Suppose 10% of people in a large population are underweight. If we take a random sample of 1000 people from this population, what is the chance that more than 103 will be underweight?

Example 16. For HANES women, 19-24 a histogram of the amount of beef eaten over a 3-day period has an average of 22 grams and an SD of 41 grams. Suppose I choose 200 of these women, at random, and look at the total amount of beef eaten. What s the chance this total exceeds 5000g? Does it matter that the histogram is not like the normal curve? Why?