Cards, decks and hands
|
|
- Pierce Sullivan
- 5 years ago
- Views:
Transcription
1 Cards, decks and hands Blackjack The cards (in the mathematical sense) are the cards, aces have weight, cards from 2 to 9 have their point value as weight, 0s, jacks, queens and kings have weight 0, so d d 2 =... = d 9 =, d 0 = 4, d r = 0 for r> 0. (In this model we do not distinguish between suits.) Therefore the 2-variable hand-enumerator is H 9 x r y r x 0 y x y x 2 y x 3 y x 4 y x 5 y x 6 y x 7 y x 8 y x 9 y x 0 y 4 4 To find the number of hands of value 8 consisting of 4 cards, we need to extract the coefficient of x 8 y 4. SeriesCoefficient H, x, 0, 8, y, 0, 4 5 The money-changing problem Question: In how many ways can one pay a certain amount using the given coins? E.g., in how many way can one pay 37p using p, 2p, 5p, 0p coins using exactly 2 coins or using an arbitrary number of coins? (Each coin can be used arbitrarily many times.) Now the cards are the coins, and the weight of each coin is its value. So in this example, d d 2 d 5 = d 0 =, and d r 0 for other values of r. Therefore the 2-variable
2 2 Cards.nb hand-enumerator is H x y x 2 y x 5 y x 0 y x y x 2 y x 5 y x 0 y To find the number of ways one can pay 37p using exactly 2 coins, we need to extract the coefficient of x 37 y 2. SeriesCoefficient H, x, 0, 37, y, 0, 2 7 If we do not care about the number of coins used, we calculate the -variable hand-enumerator by substituting y= into H, and then extract the coefficient of x 37. SeriesCoefficient H. y, x, 0, BT payphones The payphones accept 5p, 0p, 20p and 50p coins, and to make a call you need to insert at least 60p using at most 4 coins. The cards are the coins and the weight of each coin is its value, just as in the previous example. Therefore d 5 d 0 d 20 = d 50 =, and d r 0 for other values of r. The 2- variable hand-enumerator is H x 5 y x 0 y x 20 y x 50 y x 5 y x 0 y x 20 y x 50 y The number of possible combinations of coins is the sum of the coefficients x n y k for n 60 and k 4. First we discard all the terms of degree 5 in y, then the terms of degree <60 in x.
3 Cards.nb 3 J Normal Series H, y, 0, 4 x 5 x 0 x 20 x 50 y x 0 x 5 x 20 x 25 x 30 x 40 x 55 x 60 x 70 x 00 y 2 x 5 x 20 x 25 2 x 30 x 35 x 40 x 45 x 50 2 x 60 x 65 x 70 x 75 x 80 x 90 x 05 x 0 x 20 x 50 y 3 x 20 x 25 x 30 2 x 35 2 x 40 x 45 2 x 50 x 55 x 60 2 x 65 2 x 70 x 75 3 x 80 x 85 x 90 x 95 x 00 2 x 0 x 5 x 20 x 25 x 30 x 40 x 55 x 60 x 70 x 200 y 4 K J Normal Series J, x, 0, 59 Expand x 60 y 2 x 70 y 2 x 00 y 2 2 x 60 y 3 x 65 y 3 x 70 y 3 x 75 y 3 x 80 y 3 x 90 y 3 x 05 y 3 x 0 y 3 x 20 y 3 x 50 y 3 x 60 y 4 2 x 65 y 4 2 x 70 y 4 x 75 y 4 3 x 80 y 4 x 85 y 4 x 90 y 4 x 95 y 4 x 00 y 4 2 x 0 y 4 x 5 y 4 x 20 y 4 x 25 y 4 x 30 y 4 x 40 y 4 x 55 y 4 x 60 y 4 x 70 y 4 x 200 y 4 The answer is the sum of the coefficients of the above polynomial, which we can obtain easily by substituting x=y= into it. K. x, y 38 Partitions Let p(n) be the number of ways in which a non-negative integer can be written as the sum of positive integers, without regard to the order of the terms. E.g., 4=3+=2+2=2++=+++, so p(4)=5. This can be considered as a special case of the money changing problem when we have one coin for each positive integer, so d r for each positive integer r. As we have not specified the number of terms, we write down the -variable handenumerator. H r x r QPochhammer x, x To find the number of partitions of any particular number
4 4 Cards.nb n, we need to extract the coefficient of x n from the Maclaurin series of this function. For example, if n=200, SeriesCoefficient H, x, 0,
5 Cards.nb 5 Restricted problems Blackjack The previous model assumed that each card may be used arbitrarily many times which corresponds to playing with infinitely many decks. The right model for calculating the optimal strategy for a single deck of card is to distinguish the suits and to allow each card to be used at most once, so d d 2 =... = d 9 = 4, d 0 = 6, d r = 0 for r> 0, and the restriction set is W={0,}. Therefore the 2-variable hand - enumerator is H 9 x r y 4 x 0 y 6 r x y 4 x 2 y 4 x 3 y 4 x 4 y 4 x 5 y 4 x 6 y 4 x 7 y 4 x 8 y 4 x 9 y 4 x 0 y 6 To find the number of hands worth 8 points consisting of 4 cards, we again need to extract the coefficient of x 8 y 4. SeriesCoefficient H, x, 0, 8, y, 0, The money-changing problem Let us consider the problem of paying 37p using p, 2p, 5p, 0p coins using exactly 2 coins or using an arbitrary number of coins with the additional restriction that at most 5 of each coin may be used. d d 2 d 5 = d 0 =, and d r 0 for other values of r as
6 6 Cards.nb before and W={0,,2,3,4,5}. Therefore the 2-variable hand-enumerator is H x 6 y 6 x 2 y 6 x 30 y 6 x 60 y 6 x y x 2 y x 5 y x 0 y x 6 y 6 x 2 y 6 x 30 y 6 x 60 y 6 x y x 2 y x 5 y x 0 y To find the number of ways one can pay 37p using exactly 2 coins with the restriction, we need to extract the coefficient of x 37 y 2. SeriesCoefficient H, x, 0, 37, y, 0, 2 2 If we do not care about the number of coins used, we calculate the -variable hand-enumerator by substituting y= into H and then extract the coefficient of x 37. SeriesCoefficient H. y, x, 0, 37 2
Section 4.2: Mathematical Induction 1
Section 4.: Mathematical Induction 1 Over the next couple of sections, we shall consider a method of proof called mathematical induction. Induction is fairly complicated, but a very useful proof technique,
More informationIntroduction and basic definitions
Chapter 1 Introduction and basic definitions 1.1 Sample space, events, elementary probability Exercise 1.1 Prove that P( ) = 0. Solution of Exercise 1.1 : Events S (where S is the sample space) and are
More informationCiphering MU ALPHA THETA STATE 2008 ROUND
Ciphering MU ALPHA THETA STATE 2008 ROUND SCHOOL NAME ID CODE Circle one of the following Mu Alpha Theta Euclidean Round 1 What is the distance between the points (1, -6) and (5, -3)? Simplify: 5 + 5 5
More information1. Discrete Distributions
Virtual Laboratories > 2. Distributions > 1 2 3 4 5 6 7 8 1. Discrete Distributions Basic Theory As usual, we start with a random experiment with probability measure P on an underlying sample space Ω.
More informationIntroduction to Random Variables
Introduction to Random Variables Readings: Pruim 2.1.3, 2.3.1, 2.5 Learning Objectives: 1. Be able to define a random variable and its probability distribution 2. Be able to determine probabilities associated
More informationLecture 2: Mutually Orthogonal Latin Squares and Finite Fields
Latin Squares Instructor: Padraic Bartlett Lecture 2: Mutually Orthogonal Latin Squares and Finite Fields Week 2 Mathcamp 2012 Before we start this lecture, try solving the following problem: Question
More informationIf S = {O 1, O 2,, O n }, where O i is the i th elementary outcome, and p i is the probability of the i th elementary outcome, then
1.1 Probabilities Def n: A random experiment is a process that, when performed, results in one and only one of many observations (or outcomes). The sample space S is the set of all elementary outcomes
More informationModule 1. Probability
Module 1 Probability 1. Introduction In our daily life we come across many processes whose nature cannot be predicted in advance. Such processes are referred to as random processes. The only way to derive
More informationELEG 3143 Probability & Stochastic Process Ch. 1 Probability
Department of Electrical Engineering University of Arkansas ELEG 3143 Probability & Stochastic Process Ch. 1 Probability Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Applications Elementary Set Theory Random
More information1. Consider a random independent sample of size 712 from a distribution with the following pdf. c 1+x. f(x) =
1. Consider a random independent sample of size 712 from a distribution with the following pdf f(x) = c 1+x 0
More informationJuly 21 Math 2254 sec 001 Summer 2015
July 21 Math 2254 sec 001 Summer 2015 Section 8.8: Power Series Theorem: Let a n (x c) n have positive radius of convergence R, and let the function f be defined by this power series f (x) = a n (x c)
More informationAlgebra 2 PBA PARCC Packet
Algebra PBA PARCC Packet NAME: This packet contains sample questions and refresher worksheets. This packet must be completed and returned to room 111 before the Algebra PARCC test on Wednesday March 4
More informationChapter 3 : Conditional Probability and Independence
STAT/MATH 394 A - PROBABILITY I UW Autumn Quarter 2016 Néhémy Lim Chapter 3 : Conditional Probability and Independence 1 Conditional Probabilities How should we modify the probability of an event when
More informationSome Concepts in Probability and Information Theory
PHYS 476Q: An Introduction to Entanglement Theory (Spring 2018) Eric Chitambar Some Concepts in Probability and Information Theory We begin this course with a condensed survey of basic concepts in probability
More informationPRECALCULUS SEM. 1 REVIEW ( ) (additional copies available online!) Use the given functions to find solutions to problems 1 6.
PRECALCULUS SEM. 1 REVIEW (2011 2012) (additional copies available online!) Name: Period: Unit 1: Functions *** No Calculators!!**** Use the given functions to find solutions to problems 1 6. f (x) = x
More informationIntroduction to Induction (LAMC, 10/14/07)
Introduction to Induction (LAMC, 10/14/07) Olga Radko October 1, 007 1 Definitions The Method of Mathematical Induction (MMI) is usually stated as one of the axioms of the natural numbers (so-called Peano
More informationProbability, Conditional Probability and Bayes Rule IE231 - Lecture Notes 3 Mar 6, 2018
Probability, Conditional Probability and Bayes Rule IE31 - Lecture Notes 3 Mar 6, 018 #Introduction Let s recall some probability concepts. Probability is the quantification of uncertainty. For instance
More informationSolution Set for Homework #1
CS 683 Spring 07 Learning, Games, and Electronic Markets Solution Set for Homework #1 1. Suppose x and y are real numbers and x > y. Prove that e x > ex e y x y > e y. Solution: Let f(s = e s. By the mean
More informationBinomial Probability. Permutations and Combinations. Review. History Note. Discuss Quizzes/Answer Questions. 9.0 Lesson Plan
9.0 Lesson Plan Discuss Quizzes/Answer Questions History Note Review Permutations and Combinations Binomial Probability 1 9.1 History Note Pascal and Fermat laid out the basic rules of probability in a
More informationSTAT 516 Answers Homework 2 January 23, 2008 Solutions by Mark Daniel Ward PROBLEMS. = {(a 1, a 2,...) : a i < 6 for all i}
STAT 56 Answers Homework 2 January 23, 2008 Solutions by Mark Daniel Ward PROBLEMS 2. We note that E n consists of rolls that end in 6, namely, experiments of the form (a, a 2,...,a n, 6 for n and a i
More informationMATH39001 Generating functions. 1 Ordinary power series generating functions
MATH3900 Generating functions The reference for this part of the course is generatingfunctionology by Herbert Wilf. The 2nd edition is downloadable free from http://www.math.upenn. edu/~wilf/downldgf.html,
More informationMarch 5, Solution: D. The event happens precisely when the number 2 is one of the primes selected. This occurs with probability ( (
March 5, 2007 1. We randomly select 4 prime numbers without replacement from the first 10 prime numbers. What is the probability that the sum of the four selected numbers is odd? (A) 0.21 (B) 0.30 (C)
More informationChapter 7: Section 7-1 Probability Theory and Counting Principles
Chapter 7: Section 7-1 Probability Theory and Counting Principles D. S. Malik Creighton University, Omaha, NE D. S. Malik Creighton University, Omaha, NE Chapter () 7: Section 7-1 Probability Theory and
More informationAlgebra 1 End of Course Review
1 Fractions, decimals, and integers are not examples of whole numbers, rational numbers, and natural numbers. Numbers divisible by 2 are even numbers. All others are odd numbers. The absolute value of
More informationPROBABILITY VITTORIA SILVESTRI
PROBABILITY VITTORIA SILVESTRI Contents Preface. Introduction 2 2. Combinatorial analysis 5 3. Stirling s formula 8 4. Properties of Probability measures Preface These lecture notes are for the course
More informationSect Exponents: Multiplying and Dividing Common Bases
154 Sect 5.1 - Exponents: Multiplying and Dividing Common Bases Concept #1 Review of Exponential Notation In the exponential expression 4 5, 4 is called the base and 5 is called the exponent. This says
More informationLecture 2: Probability. Readings: Sections Statistical Inference: drawing conclusions about the population based on a sample
Lecture 2: Probability Readings: Sections 5.1-5.3 1 Introduction Statistical Inference: drawing conclusions about the population based on a sample Parameter: a number that describes the population a fixed
More informationAnalysis of Algorithms [Reading: CLRS 2.2, 3] Laura Toma, csci2200, Bowdoin College
Analysis of Algorithms [Reading: CLRS 2.2, 3] Laura Toma, csci2200, Bowdoin College Why analysis? We want to predict how the algorithm will behave (e.g. running time) on arbitrary inputs, and how it will
More informationCBSE Class X Mathematics Sample Paper 04
CBSE Class X Mathematics Sample Paper 04 Time Allowed: 3 Hours Max Marks: 80 General Instructions: i All questions are compulsory ii The question paper consists of 30 questions divided into four sections
More informationPROBABILITY. Contents Preface 1 1. Introduction 2 2. Combinatorial analysis 5 3. Stirling s formula 8. Preface
PROBABILITY VITTORIA SILVESTRI Contents Preface. Introduction. Combinatorial analysis 5 3. Stirling s formula 8 Preface These lecture notes are for the course Probability IA, given in Lent 09 at the University
More informationECEN 5612, Fall 2007 Noise and Random Processes Prof. Timothy X Brown NAME: CUID:
Midterm ECE ECEN 562, Fall 2007 Noise and Random Processes Prof. Timothy X Brown October 23 CU Boulder NAME: CUID: You have 20 minutes to complete this test. Closed books and notes. No calculators. If
More informationCOMP 120. For any doubts in the following, contact Agam, Room. 023
COMP 120 Computer Organization Spring 2006 For any doubts in the following, contact Agam, Room. 023 Problem Set #1 Solution Problem 1. Miss Information [A] First card ca n be any one of 52 possibilities.
More information2. Counting and Probability
2. Counting and Probability 2.1.1 Factorials 2.1.2 Combinatorics 2.2.1 Probability Theory 2.2.2 Probability Examples 2.1.1 Factorials Combinatorics Combinatorics is the mathematics of counting. It can
More information1. 10 apples for $6 is $0.60 per apple. 20 apples for $10 is $0.50 per apple. 25 $ $0.50 = 25 $0.10 = $2.50
Fall 205. 0 apples for $6 is $0.60 per apple. 20 apples for $0 is $0.50 per apple. So the answer is B. $2.50 25 $0.60 25 $0.50 = 25 $0.0 = $2.50 2. Let s begin by finding the slopes of the two lines. ax
More informationSimplifying Radical Expressions
Simplifying Radical Expressions Product Property of Radicals For any real numbers a and b, and any integer n, n>1, 1. If n is even, then When a and b are both nonnegative. n ab n a n b 2. If n is odd,
More informationA Latin Square of order n is an n n array of n symbols where each symbol occurs once in each row and column. For example,
1 Latin Squares A Latin Square of order n is an n n array of n symbols where each symbol occurs once in each row and column. For example, A B C D E B C A E D C D E A B D E B C A E A D B C is a Latin square
More informationAnswer Key. Solve each equation x - 9 = (n + 2) = b - 6 = -3b + 48
Solve each equation. 1. -3x - 9 = -27 2. 25 + 2(n + 2) = 30 3. -9b - 6 = -3b + 48 x = 6 n = 1 / 2 b = -9 4. 5 - (m - 4) = 2m + 3(m - 1) 5. -24-10k = -8(k + 4) - 2k 6. f - (-19) = 11f + 23-20f m = 2 no
More informationRandom Variables and Events
Random Variables and Events Data Science: Jordan Boyd-Graber University of Maryland SLIDES ADAPTED FROM DAVE BLEI AND LAUREN HANNAH Data Science: Jordan Boyd-Graber UMD Random Variables and Events 1 /
More informationArithmetic properties of lacunary sums of binomial coefficients
Arithmetic properties of lacunary sums of binomial coefficients Tamás Mathematics Department Occidental College 29th Journées Arithmétiques JA2015, July 6-10, 2015 Arithmetic properties of lacunary sums
More informationChapter 2.5 Random Variables and Probability The Modern View (cont.)
Chapter 2.5 Random Variables and Probability The Modern View (cont.) I. Statistical Independence A crucially important idea in probability and statistics is the concept of statistical independence. Suppose
More informationMath Bootcamp 2012 Miscellaneous
Math Bootcamp 202 Miscellaneous Factorial, combination and permutation The factorial of a positive integer n denoted by n!, is the product of all positive integers less than or equal to n. Define 0! =.
More informationLearning Target #1: I am learning to compare tables, equations, and graphs to model and solve linear & nonlinear situations.
8 th Grade Honors Name: Chapter 2 Examples of Rigor Learning Target #: I am learning to compare tables, equations, and graphs to model and solve linear & nonlinear situations. Success Criteria I know I
More informationReview: Power series define functions. Functions define power series. Taylor series of a function. Taylor polynomials of a function.
Taylor Series (Sect. 10.8) Review: Power series define functions. Functions define power series. Taylor series of a function. Taylor polynomials of a function. Review: Power series define functions Remarks:
More informationProblem # Number of points 1 /20 2 /20 3 /20 4 /20 5 /20 6 /20 7 /20 8 /20 Total /150
Name Student ID # Instructor: SOLUTION Sergey Kirshner STAT 516 Fall 09 Practice Midterm #1 January 31, 2010 You are not allowed to use books or notes. Non-programmable non-graphic calculators are permitted.
More informationDiscrete Mathematics 2007: Lecture 5 Infinite sets
Discrete Mathematics 2007: Lecture 5 Infinite sets Debrup Chakraborty 1 Countability The natural numbers originally arose from counting elements in sets. There are two very different possible sizes for
More informationDiscrete Structures Homework 1
Discrete Structures Homework 1 Due: June 15. Section 1.1 16 Determine whether these biconditionals are true or false. a) 2 + 2 = 4 if and only if 1 + 1 = 2 b) 1 + 1 = 2 if and only if 2 + 3 = 4 c) 1 +
More informationMADISON ACADEMY ALGEBRA WITH FINANCE PACING GUIDE
(ACT included) [N-RN] Extend the properties of exponents to rational exponents.. 1st 9 Weeks Brooks/Cole Cengage Learning Chapter 1 all sections, Operations with Integers, Operations with Rational Numbers,
More informationProbability: Sets, Sample Spaces, Events
Probability: Sets, Sample Spaces, Events Engineering Statistics Section 2.1 Josh Engwer TTU 01 February 2016 Josh Engwer (TTU) Probability: Sets, Sample Spaces, Events 01 February 2016 1 / 29 The Need
More informationMATH475 SAMPLE EXAMS.
MATH75 SAMPLE EXAMS Exam How many ways are there to distribute 8 different toys and 8 identical candy to children a without restrictions; b if first child should get exactly toys; c if the first child
More informationAnswers to the CSCE 551 Final Exam, April 30, 2008
Answers to the CSCE 55 Final Exam, April 3, 28. (5 points) Use the Pumping Lemma to show that the language L = {x {, } the number of s and s in x differ (in either direction) by at most 28} is not regular.
More informationMathematical Writing and Methods of Proof
Mathematical Writing and Methods of Proof January 6, 2015 The bulk of the work for this course will consist of homework problems to be handed in for grading. I cannot emphasize enough that I view homework
More informationEcon 113. Lecture Module 2
Econ 113 Lecture Module 2 Contents 1. Experiments and definitions 2. Events and probabilities 3. Assigning probabilities 4. Probability of complements 5. Conditional probability 6. Statistical independence
More informationMath 105A HW 1 Solutions
Sect. 1.1.3: # 2, 3 (Page 7-8 Math 105A HW 1 Solutions 2(a ( Statement: Each positive integers has a unique prime factorization. n N: n = 1 or ( R N, p 1,..., p R P such that n = p 1 p R and ( n, R, S
More informationMathematics 324 Riemann Zeta Function August 5, 2005
Mathematics 324 Riemann Zeta Function August 5, 25 In this note we give an introduction to the Riemann zeta function, which connects the ideas of real analysis with the arithmetic of the integers. Define
More informationNumber Theory and Counting Method. Divisors -Least common divisor -Greatest common multiple
Number Theory and Counting Method Divisors -Least common divisor -Greatest common multiple Divisors Definition n and d are integers d 0 d divides n if there exists q satisfying n = dq q the quotient, d
More informationA Event has occurred
Statistics and probability: 1-1 1. Probability Event: a possible outcome or set of possible outcomes of an experiment or observation. Typically denoted by a capital letter: A, B etc. E.g. The result of
More informationSTOR Lecture 4. Axioms of Probability - II
STOR 435.001 Lecture 4 Axioms of Probability - II Jan Hannig UNC Chapel Hill 1 / 23 How can we introduce and think of probabilities of events? Natural to think: repeat the experiment n times under same
More informationName: Exam 2 Solutions. March 13, 2017
Department of Mathematics University of Notre Dame Math 00 Finite Math Spring 07 Name: Instructors: Conant/Galvin Exam Solutions March, 07 This exam is in two parts on pages and contains problems worth
More informationLecture 4: Counting, Pigeonhole Principle, Permutations, Combinations Lecturer: Lale Özkahya
BBM 205 Discrete Mathematics Hacettepe University http://web.cs.hacettepe.edu.tr/ bbm205 Lecture 4: Counting, Pigeonhole Principle, Permutations, Combinations Lecturer: Lale Özkahya Resources: Kenneth
More informationMCS 256 Discrete Calculus and Probability Exam 5: Final Examination 22 May 2007
MCS 256 Discrete Calculus and Probability SOLUTIONS Exam 5: Final Examination 22 May 2007 Instructions: This is a closed-book examination. You may, however, use one 8.5 -by-11 page of notes, your note
More informationNORTHWESTERN UNIVERSITY Thrusday, Oct 6th, 2011 ANSWERS FALL 2011 NU PUTNAM SELECTION TEST
Problem A1. Let a 1, a 2,..., a n be n not necessarily distinct integers. exist a subset of these numbers whose sum is divisible by n. Prove that there - Answer: Consider the numbers s 1 = a 1, s 2 = a
More informationProperties of Probability
Econ 325 Notes on Probability 1 By Hiro Kasahara Properties of Probability In statistics, we consider random experiments, experiments for which the outcome is random, i.e., cannot be predicted with certainty.
More informationA SUMMARY OF RECURSION SOLVING TECHNIQUES
A SUMMARY OF RECURSION SOLVING TECHNIQUES KIMMO ERIKSSON, KTH These notes are meant to be a complement to the material on recursion solving techniques in the textbook Discrete Mathematics by Biggs. In
More information6 Lecture 6b: the Euler Maclaurin formula
Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Fall 217 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 217 March 26, 218 6 Lecture 6b: the Euler Maclaurin formula
More informationSlide 1 Math 1520, Lecture 21
Slide 1 Math 1520, Lecture 21 This lecture is concerned with a posteriori probability, which is the probability that a previous event had occurred given the outcome of a later event. Slide 2 Conditional
More informationMATH 151, FINAL EXAM Winter Quarter, 21 March, 2014
Time: 3 hours, 8:3-11:3 Instructions: MATH 151, FINAL EXAM Winter Quarter, 21 March, 214 (1) Write your name in blue-book provided and sign that you agree to abide by the honor code. (2) The exam consists
More informationModule 9 : Infinite Series, Tests of Convergence, Absolute and Conditional Convergence, Taylor and Maclaurin Series
Module 9 : Infinite Series, Tests of Convergence, Absolute and Conditional Convergence, Taylor and Maclaurin Series Lecture 27 : Series of functions [Section 271] Objectives In this section you will learn
More informationSection F Ratio and proportion
Section F Ratio and proportion Ratio is a way of comparing two or more groups. For example, if something is split in a ratio 3 : 5 there are three parts of the first thing to every five parts of the second
More informationInfinite Series. Copyright Cengage Learning. All rights reserved.
Infinite Series Copyright Cengage Learning. All rights reserved. Taylor and Maclaurin Series Copyright Cengage Learning. All rights reserved. Objectives Find a Taylor or Maclaurin series for a function.
More informationSTP 226 ELEMENTARY STATISTICS
STP 226 ELEMENTARY STATISTICS CHAPTER 5 Probability Theory - science of uncertainty 5.1 Probability Basics Equal-Likelihood Model Suppose an experiment has N possible outcomes, all equally likely. Then
More informationUndecidability. Andreas Klappenecker. [based on slides by Prof. Welch]
Undecidability Andreas Klappenecker [based on slides by Prof. Welch] 1 Sources Theory of Computing, A Gentle Introduction, by E. Kinber and C. Smith, Prentice-Hall, 2001 Automata Theory, Languages and
More informationQuestion 1. Find the coordinates of the y-intercept for. f) None of the above. Question 2. Find the slope of the line:
of 4 4/4/017 8:44 AM Question 1 Find the coordinates of the y-intercept for. Question Find the slope of the line: of 4 4/4/017 8:44 AM Question 3 Solve the following equation for x : Question 4 Paul has
More informationChapter 11. Taylor Series. Josef Leydold Mathematical Methods WS 2018/19 11 Taylor Series 1 / 27
Chapter 11 Taylor Series Josef Leydold Mathematical Methods WS 2018/19 11 Taylor Series 1 / 27 First-Order Approximation We want to approximate function f by some simple function. Best possible approximation
More informationD - E - F - G (1967 Jr.) Given that then find the number of real solutions ( ) of this equation.
D - E - F - G - 18 1. (1975 Jr.) Given and. Two circles, with centres and, touch each other and also the sides of the rectangle at and. If the radius of the smaller circle is 2, then find the radius of
More informationMath 4: Advanced Algebra Ms. Sheppard-Brick B Quiz Review Learning Targets
5B Quiz Review Learning Targets 4.6 5.9 Key Facts We learned two ways to solve a system of equations using algebra: o The substitution method! Pick one equation and solve for either x or y! Take that result
More informationIntermediate Algebra Semester Summary Exercises. 1 Ah C. b = h
. Solve: 3x + 8 = 3 + 8x + 3x A. x = B. x = 4 C. x = 8 8 D. x =. Solve: w 3 w 5 6 8 A. w = 4 B. w = C. w = 4 D. w = 60 3. Solve: 3(x ) + 4 = 4(x + ) A. x = 7 B. x = 5 C. x = D. x = 4. The perimeter of
More informationMath1a Set 1 Solutions
Math1a Set 1 Solutions October 15, 2018 Problem 1. (a) For all x, y, z Z we have (i) x x since x x = 0 is a multiple of 7. (ii) If x y then there is a k Z such that x y = 7k. So, y x = (x y) = 7k is also
More informationA RESULT ON RAMANUJAN-LIKE CONGRUENCE PROPERTIES OF THE RESTRICTED PARTITION FUNCTION p(n, m) ACROSS BOTH VARIABLES
#A63 INTEGERS 1 (01) A RESULT ON RAMANUJAN-LIKE CONGRUENCE PROPERTIES OF THE RESTRICTED PARTITION FUNCTION p(n, m) ACROSS BOTH VARIABLES Brandt Kronholm Department of Mathematics, Whittier College, Whittier,
More information(4.2) Equivalence Relations. 151 Math Exercises. Malek Zein AL-Abidin. King Saud University College of Science Department of Mathematics
King Saud University College of Science Department of Mathematics 151 Math Exercises (4.2) Equivalence Relations Malek Zein AL-Abidin 1440 ه 2018 Equivalence Relations DEFINITION 1 A relation on a set
More informationStatistics 100 Exam 2 March 8, 2017
STAT 100 EXAM 2 Spring 2017 (This page is worth 1 point. Graded on writing your name and net id clearly and circling section.) PRINT NAME (Last name) (First name) net ID CIRCLE SECTION please! L1 (MWF
More informationRandom processes. Lecture 17: Probability, Part 1. Probability. Law of large numbers
Random processes Lecture 17: Probability, Part 1 Statistics 10 Colin Rundel March 26, 2012 A random process is a situation in which we know what outcomes could happen, but we don t know which particular
More informationChapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations
Chapter 1: Systems of linear equations and matrices Section 1.1: Introduction to systems of linear equations Definition: A linear equation in n variables can be expressed in the form a 1 x 1 + a 2 x 2
More informationName: 180A MIDTERM 2. (x + n)/2
1. Recall the (somewhat strange) person from the first midterm who repeatedly flips a fair coin, taking a step forward when it lands head up and taking a step back when it lands tail up. Suppose this person
More informationCMPSCI 240: Reasoning about Uncertainty
CMPSCI 240: Reasoning about Uncertainty Lecture 2: Sets and Events Andrew McGregor University of Massachusetts Last Compiled: January 27, 2017 Outline 1 Recap 2 Experiments and Events 3 Probabilistic Models
More informationLecture 2. LINEAR DIFFERENTIAL SYSTEMS p.1/22
LINEAR DIFFERENTIAL SYSTEMS Harry Trentelman University of Groningen, The Netherlands Minicourse ECC 2003 Cambridge, UK, September 2, 2003 LINEAR DIFFERENTIAL SYSTEMS p1/22 Part 1: Generalities LINEAR
More informationICT12 8. Linear codes. The Gilbert-Varshamov lower bound and the MacWilliams identities SXD
1 ICT12 8. Linear codes. The Gilbert-Varshamov lower bound and the MacWilliams identities 19.10.2012 SXD 8.1. The Gilbert Varshamov existence condition 8.2. The MacWilliams identities 2 8.1. The Gilbert
More informationLesson 3: Using Linear Combinations to Solve a System of Equations
Lesson 3: Using Linear Combinations to Solve a System of Equations Steps for Using Linear Combinations to Solve a System of Equations 1. 2. 3. 4. 5. Example 1 Solve the following system using the linear
More informationCounting Methods. CSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo
Counting Methods CSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo c Xin He (University at Buffalo) CSE 191 Discrete Structures 1 / 48 Need for Counting The problem of counting
More informationA Bayesian Approach to Phylogenetics
A Bayesian Approach to Phylogenetics Niklas Wahlberg Based largely on slides by Paul Lewis (www.eeb.uconn.edu) An Introduction to Bayesian Phylogenetics Bayesian inference in general Markov chain Monte
More informationLecture 11 - Basic Number Theory.
Lecture 11 - Basic Number Theory. Boaz Barak October 20, 2005 Divisibility and primes Unless mentioned otherwise throughout this lecture all numbers are non-negative integers. We say that a divides b,
More informationSection 9.7 and 9.10: Taylor Polynomials and Approximations/Taylor and Maclaurin Series
Section 9.7 and 9.10: Taylor Polynomials and Approximations/Taylor and Maclaurin Series Power Series for Functions We can create a Power Series (or polynomial series) that can approximate a function around
More informationUCS Algebra II Semester 2 Exam Review- Multiple Choice
2015-2016 UCS lgebra II Semester 2 Exam Review- Multiple Choice With nswers!! 1 Which values of x satisfy: x 3 + 5 = x? x = 4 only C x = 4 and x = 7 B* x = 7 only x = 4 and x = -7 2 Find the x-intercept
More informationMotivation. Stat Camp for the MBA Program. Probability. Experiments and Outcomes. Daniel Solow 5/10/2017
Stat Camp for the MBA Program Daniel Solow Lecture 2 Probability Motivation You often need to make decisions under uncertainty, that is, facing an unknown future. Examples: How many computers should I
More informatione x = 1 + x + x2 2! + x3 If the function f(x) can be written as a power series on an interval I, then the power series is of the form
Taylor Series Given a function f(x), we would like to be able to find a power series that represents the function. For example, in the last section we noted that we can represent e x by the power series
More informationAnalysis II: Basic knowledge of real analysis: Part V, Power Series, Differentiation, and Taylor Series
.... Analysis II: Basic knowledge of real analysis: Part V, Power Series, Differentiation, and Taylor Series Kenichi Maruno Department of Mathematics, The University of Texas - Pan American March 4, 20
More informationWriting proofs for MATH 51H Section 2: Set theory, proofs of existential statements, proofs of uniqueness statements, proof by cases
Writing proofs for MATH 51H Section 2: Set theory, proofs of existential statements, proofs of uniqueness statements, proof by cases September 22, 2018 Recall from last week that the purpose of a proof
More informationStatistics for Managers Using Microsoft Excel (3 rd Edition)
Statistics for Managers Using Microsoft Excel (3 rd Edition) Chapter 4 Basic Probability and Discrete Probability Distributions 2002 Prentice-Hall, Inc. Chap 4-1 Chapter Topics Basic probability concepts
More informationChapter 8 Sequences, Series, and Probability
Chapter 8 Sequences, Series, and Probability Overview 8.1 Sequences and Series 8.2 Arithmetic Sequences and Partial Sums 8.3 Geometric Sequences and Partial Sums 8.5 The Binomial Theorem 8.6 Counting Principles
More informationClimbing an Infinite Ladder
Section 5.1 Section Summary Mathematical Induction Examples of Proof by Mathematical Induction Mistaken Proofs by Mathematical Induction Guidelines for Proofs by Mathematical Induction Climbing an Infinite
More informationDepartment of Mathematics Comprehensive Examination Option I 2016 Spring. Algebra
Comprehensive Examination Option I Algebra 1. Let G = {τ ab : R R a, b R and a 0} be the group under the usual function composition, where τ ab (x) = ax + b, x R. Let R be the group of all nonzero real
More information