STAT 302 Introduction to Probability Learning Outcomes. Textbook: A First Course in Probability by Sheldon Ross, 8 th ed.
|
|
- Warren Davidson
- 5 years ago
- Views:
Transcription
1 STAT 302 Introduction to Probability Learning Outcomes Textbook: A First Course in Probability by Sheldon Ross, 8 th ed. Chapter 1: Combinatorial Analysis Demonstrate the ability to solve combinatorial problems o Use the basic principle of counting to obtain the total number of possible outcomes in a random experiment o Differentiate between permutations and combinations in a particular context o Explain if the order of outcomes matters in the context of the counting problem o Apply rules on permutations and combinations in solving counting problems Chapter 2: Axioms of Probability Demonstrate an understanding of basic probability concepts o Recognize the random experiment of interest in a given scenario o List all possible outcomes in the sample space of a random experiment o Recall the definitions of an event, the complement of an event, unions and intersections of events o Recall the meaning of a null set, and that of an event being contained in another event o Use Venn Diagrams to depict single events, complementary events, unions and intersections of a collection of events o Explain whether a collection of events are mutually exclusive o Apply the Commutative, Associative, Distributive and DeMorgan s laws to unions and intersections of events o Recall the probability of an event is a long-run relative frequency of occurrences of the event in repetitions of a random experiment o Recall the axioms of probability of an event o Define appropriate events in solving probability problems o Recognize if outcomes in a sample space are equally likely o Determine the probability of an event where the outcomes in a sample space are equally likely o Apply probability rules (complement rule, addition/general addition rule and others) in solving probability problems Chapter 3: Conditional Probability and Independence Apply the concepts of conditional probabilities and independence in computing probabilities of interest o Recall the definition and properties of conditional probabilities
2 o Compute conditional probabilities where probabilities of events in the conditional probability formula are given o Derive Bayes formula o Apply Bayes formula in solving probability problems o Recall the definition of the odds of an event o Interpret the value of the odds in terms of the relative probability of an event and its complement o Recall what is meant by independent events o Apply the definition of independence to determine whether two events or a collection of events are independent o Apply the definition of conditional independence to determine whether two events are conditionally independent Chapter 4: Discrete Random Variables Demonstrate an understanding of the basic concepts of discrete random variables and a number of common discrete s o Recall that a random variable is a function that maps outcomes in a sample space to a numerical quantity o Identify the random variable(s) of interest in a given scenario o Tell whether a random variable is discrete or not o Recall the definition and properties of the probability mass function of a discrete random variable o Recall the definition and properties of the cumulative function of a discrete random variable o Obtain the probability mass function and cumulative function for a discrete random variable of interest o Calculate probabilities associated with a discrete random variable o Calculate the expected value of a discrete random variable and that of a real function of a discrete variable o Interpret the expected value of a discrete random variable o Calculate the variance and standard deviation of a discrete random variable o Apply general properties of expectation and variance operators o Recall the definitions of a Bernoulli trial and a Binomial experiment o Recall the properties of a Poisson process o Recognize the Poisson random variable associated with a Poisson process o Recognize cases where the following s could be an applied model: Bernoulli, Binomial, Geometric, Negative Binomial, Poisson and Hypergeometric o Identify the parameters for the following s: Bernoulli, Binomial, Geometric, Negative Binomial, Poisson and Hypergeometric
3 o Calculate probabilities, the mean and variance of the following random variables: Bernoulli, Binomial, Geometric, Negative Binomial, Poisson and Hypergeometric o Approximate Binomial probabilities using a Poisson where appropriate Chapter 5: Continuous Random Variables Demonstrate an understanding of the basic concepts of continuous random variables and a number of common continuous s o Identify the random variable(s) of interest in a given scenario o Differentiate between discrete and continuous random variables o Recall the properties of the probability density function and cumulative function of a continuous random variable o Calculate probabilities of a continuous random variable from a given probability density function o Recognize that the probability that a continuous random variable whose value falls in a certain region is given by the area under the probability density function over that region o Recall the relationship between the probability density function and the cumulative function of a continuous random variable o Obtain the cumulative function from a probability density function for a continuous random variable, and vice versa o Calculate the expected value of a continuous random variable and that of a real function of a continuous variable o Calculate the variance and standard deviation of a continuous random variable o Apply general properties of expectation and variance operators o Recognize cases where the following s could be an applied model: Uniform, Normal, Exponential, Gamma and Beta o Identify the parameters for the following s: Uniform, Normal, Exponential, Gamma and Beta o Describe how the probability density function changes with the parameter(s) for the following s: Uniform, Normal, Exponential, Gamma and Beta o Calculate probabilities, the mean and variance of the following random variables: Uniform, Normal, Exponential, Gamma and Beta o Recall the properties of a Normal, and those of the standard Normal o Obtain probabilities related to Normal random variables using the standard Normal table o Approximate Binomial probabilities using a Normal where appropriate o Apply continuity correction when approximating Binomial probabilities using a Normal
4 o Recall that the time between two consecutive events that occur according to a Poisson process follows the Exponential o Explain the memoryless property of a continuous random variable, with the Exponential random variable as an example o Recall the relationship between the Exponential and the Gamma o Derive the cumulative function and the probability density function of a real function of a given continuous random variable Chapter 6: Jointly Distributed Random Variables Describe the joint and conditional s related to two or more discrete or continuous random variables o Recall the following definitions relating to two discrete random variables: joint probability mass function, marginal probability mass function and joint cumulative function o Recall the properties of the joint probability mass function of discrete random variables o Derive the joint probability mass function of two discrete random variables of interest in a given scenario o Obtain the marginal probability mass function and the joint cumulative function from the joint probability mass function of two discrete random variables o Recognize if two random variables are jointly continuous o Recall the properties of the joint probability density function of continuous random variables o Recall the definitions of the following of two continuous random variables: marginal probability density function and joint cumulative function o Recall the relationship between the joint probability density function and the joint cumulative function of two continuous random variables o Obtain the marginal probability density function and the joint cumulative function from the joint probability density function of two continuous random variables o Represent graphically the region on the x-y plane over which two jointly continuous random variables are defined o Set up appropriate bounds on the x-y plane and hence limits of integration for finding a probability of interest of two jointly continuous random variables o Identify the discrete or continuous random variables of interest in a given scenario o Compute probabilities related to two discrete or continuous random variables o Explain whether two random variables are independent o Derive the cumulative function and probability mass/density function of the sum of two independent random variables o Recall that the sum of two independent Gamma random variables with the same shape parameter also follows the Gamma
5 o Recall that Normality is preserved under linear combinations o Recall that the sum of independent Poisson random variables also follows the Poisson o Recall the definitions of the following for discrete random variables: conditional probability mass function and conditional cumulative function o Calculate conditional probability mass functions and conditional cumulative functions for bivariate discrete random variables o Determine if two discrete random variables are independent by using conditional probability mass function o Recall the definitions of the following for continuous random variables: conditional probability density function and conditional cumulative function o Calculate conditional probability density functions and conditional cumulative functions for bivariate continuous random variables o Determine if two continuous random variables are independent by using conditional probability density function o Calculate conditional probabilities of one variable given values of another variable by making use of conditional probability mass function or conditional probability density function Chapter 7: Properties of Expectation Apply properties of expectation to describe relationships between two random variables, and to compute probabilities, expectation and variance by conditioning. o Calculate the expectation of a real function of two random variables o Calculate the covariance and correlation between two random variables o Interpret the covariance and correlation between two random variables o Recall the properties of correlation between two random variables o Calculate the variance of a linear combination of random variables o Recall the properties of expectation for independent random variables o Calculate conditional expectations and conditional variances o Calculate the expectation of a random variable and that of a real function of a random variable using conditional expectation o Calculate the variance of a random variable using conditional expectation o Calculate the probability of an event by taking the conditional expectation of an indicator function of the event o Recognize situations where conditional expectation can be used to find expectations, variances and probabilities of interest o Recall the definition of the moment generating function of a random variable o Obtain the moment generating function of a random variable o Recall that the moment generating function provides a unique characterization of the of a random variable
6 o Identify the of a random variable and its parameters by comparing its moment generating function with those of common discrete and continuous s o Recall the definition of the nth moment of a random variable o Calculate the nth moment of a random variable by using moment generating function o Recall that for two independent random variables, the moment generating function of their sum is the product of their individual moment generating functions Chapter 8: Limit Theorems Apply Chebyshev s Inequality and the Central Limit Theorem in describing the of and calculating probabilities concerning general random variables, averages and sums of random variables o Recall the Markov s Inequality o Use the Markov s Inequality to prove the Chebyshev s Inequality o Apply the Chebyshev s Inequality to obtain a bound for the probability concerning a random variable given its mean and variance o Use the Chebyshev s Inequality to prove the Weak Law of Large Numbers o Recall the Weak Law of Large Numbers that states the convergence in probability of the average of random variables to their expected mean. o Apply the Central Limit Theorem to problems involving sums and averages of independent random variables from arbitrary s
Learning Objectives for Stat 225
Learning Objectives for Stat 225 08/20/12 Introduction to Probability: Get some general ideas about probability, and learn how to use sample space to compute the probability of a specific event. Set Theory:
More informationTABLE OF CONTENTS CHAPTER 1 COMBINATORIAL PROBABILITY 1
TABLE OF CONTENTS CHAPTER 1 COMBINATORIAL PROBABILITY 1 1.1 The Probability Model...1 1.2 Finite Discrete Models with Equally Likely Outcomes...5 1.2.1 Tree Diagrams...6 1.2.2 The Multiplication Principle...8
More informationPart IA Probability. Definitions. Based on lectures by R. Weber Notes taken by Dexter Chua. Lent 2015
Part IA Probability Definitions Based on lectures by R. Weber Notes taken by Dexter Chua Lent 2015 These notes are not endorsed by the lecturers, and I have modified them (often significantly) after lectures.
More informationb. ( ) ( ) ( ) ( ) ( ) 5. Independence: Two events (A & B) are independent if one of the conditions listed below is satisfied; ( ) ( ) ( )
1. Set a. b. 2. Definitions a. Random Experiment: An experiment that can result in different outcomes, even though it is performed under the same conditions and in the same manner. b. Sample Space: This
More informationInstitute of Actuaries of India
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2018 Examinations Subject CT3 Probability and Mathematical Statistics Core Technical Syllabus 1 June 2017 Aim The
More informationStatistical Methods in HYDROLOGY CHARLES T. HAAN. The Iowa State University Press / Ames
Statistical Methods in HYDROLOGY CHARLES T. HAAN The Iowa State University Press / Ames Univariate BASIC Table of Contents PREFACE xiii ACKNOWLEDGEMENTS xv 1 INTRODUCTION 1 2 PROBABILITY AND PROBABILITY
More informationSubject CS1 Actuarial Statistics 1 Core Principles
Institute of Actuaries of India Subject CS1 Actuarial Statistics 1 Core Principles For 2019 Examinations Aim The aim of the Actuarial Statistics 1 subject is to provide a grounding in mathematical and
More informationContents 1. Contents
Contents 1 Contents 6 Distributions of Functions of Random Variables 2 6.1 Transformation of Discrete r.v.s............. 3 6.2 Method of Distribution Functions............. 6 6.3 Method of Transformations................
More informationSTATISTICS ANCILLARY SYLLABUS. (W.E.F. the session ) Semester Paper Code Marks Credits Topic
STATISTICS ANCILLARY SYLLABUS (W.E.F. the session 2014-15) Semester Paper Code Marks Credits Topic 1 ST21012T 70 4 Descriptive Statistics 1 & Probability Theory 1 ST21012P 30 1 Practical- Using Minitab
More informationAxioms of Probability
Sample Space (denoted by S) The set of all possible outcomes of a random experiment is called the Sample Space of the experiment, and is denoted by S. Example 1.10 If the experiment consists of tossing
More informationReview of Probabilities and Basic Statistics
Alex Smola Barnabas Poczos TA: Ina Fiterau 4 th year PhD student MLD Review of Probabilities and Basic Statistics 10-701 Recitations 1/25/2013 Recitation 1: Statistics Intro 1 Overview Introduction to
More informationPart IA Probability. Theorems. Based on lectures by R. Weber Notes taken by Dexter Chua. Lent 2015
Part IA Probability Theorems Based on lectures by R. Weber Notes taken by Dexter Chua Lent 2015 These notes are not endorsed by the lecturers, and I have modified them (often significantly) after lectures.
More informationFundamentals of Applied Probability and Random Processes
Fundamentals of Applied Probability and Random Processes,nd 2 na Edition Oliver C. Ibe University of Massachusetts, LoweLL, Massachusetts ip^ W >!^ AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS
More informationStat 5101 Lecture Notes
Stat 5101 Lecture Notes Charles J. Geyer Copyright 1998, 1999, 2000, 2001 by Charles J. Geyer May 7, 2001 ii Stat 5101 (Geyer) Course Notes Contents 1 Random Variables and Change of Variables 1 1.1 Random
More informationCommunication Theory II
Communication Theory II Lecture 5: Review on Probability Theory Ahmed Elnakib, PhD Assistant Professor, Mansoura University, Egypt Febraury 22 th, 2015 1 Lecture Outlines o Review on probability theory
More informationRandom Variables. Definition: A random variable (r.v.) X on the probability space (Ω, F, P) is a mapping
Random Variables Example: We roll a fair die 6 times. Suppose we are interested in the number of 5 s in the 6 rolls. Let X = number of 5 s. Then X could be 0, 1, 2, 3, 4, 5, 6. X = 0 corresponds to the
More informationSTAT Chapter 3: Probability
Basic Definitions STAT 515 --- Chapter 3: Probability Experiment: A process which leads to a single outcome (called a sample point) that cannot be predicted with certainty. Sample Space (of an experiment):
More information2. TRIGONOMETRY 3. COORDINATEGEOMETRY: TWO DIMENSIONS
1 TEACHERS RECRUITMENT BOARD, TRIPURA (TRBT) EDUCATION (SCHOOL) DEPARTMENT, GOVT. OF TRIPURA SYLLABUS: MATHEMATICS (MCQs OF 150 MARKS) SELECTION TEST FOR POST GRADUATE TEACHER(STPGT): 2016 1. ALGEBRA Sets:
More informationCopyright c 2006 Jason Underdown Some rights reserved. choose notation. n distinct items divided into r distinct groups.
Copyright & License Copyright c 2006 Jason Underdown Some rights reserved. choose notation binomial theorem n distinct items divided into r distinct groups Axioms Proposition axioms of probability probability
More informationSecondary Honors Algebra II Objectives
Secondary Honors Algebra II Objectives Chapter 1 Equations and Inequalities Students will learn to evaluate and simplify numerical and algebraic expressions, to solve linear and absolute value equations
More informationCOPYRIGHTED MATERIAL CONTENTS. Preface Preface to the First Edition
Preface Preface to the First Edition xi xiii 1 Basic Probability Theory 1 1.1 Introduction 1 1.2 Sample Spaces and Events 3 1.3 The Axioms of Probability 7 1.4 Finite Sample Spaces and Combinatorics 15
More informationBasics on Probability. Jingrui He 09/11/2007
Basics on Probability Jingrui He 09/11/2007 Coin Flips You flip a coin Head with probability 0.5 You flip 100 coins How many heads would you expect Coin Flips cont. You flip a coin Head with probability
More informationThe Union and Intersection for Different Configurations of Two Events Mutually Exclusive vs Independency of Events
Section 1: Introductory Probability Basic Probability Facts Probabilities of Simple Events Overview of Set Language Venn Diagrams Probabilities of Compound Events Choices of Events The Addition Rule Combinations
More informationHANDBOOK OF APPLICABLE MATHEMATICS
HANDBOOK OF APPLICABLE MATHEMATICS Chief Editor: Walter Ledermann Volume II: Probability Emlyn Lloyd University oflancaster A Wiley-Interscience Publication JOHN WILEY & SONS Chichester - New York - Brisbane
More informationMath 3338: Probability (Fall 2006)
Math 3338: Probability (Fall 2006) Jiwen He Section Number: 10853 http://math.uh.edu/ jiwenhe/math3338fall06.html Probability p.1/8 Chapter Two: Probability (I) Probability p.2/8 2.1 Sample Spaces and
More informationECE 353 Probability and Random Signals - Practice Questions
ECE 353 Probability and Random Signals - Practice Questions Winter 2018 Xiao Fu School of Electrical Engineering and Computer Science Oregon State Univeristy Note: Use this questions as supplementary materials
More informationMATH 556: PROBABILITY PRIMER
MATH 6: PROBABILITY PRIMER 1 DEFINITIONS, TERMINOLOGY, NOTATION 1.1 EVENTS AND THE SAMPLE SPACE Definition 1.1 An experiment is a one-off or repeatable process or procedure for which (a there is a well-defined
More informationSet Notation and Axioms of Probability NOT NOT X = X = X'' = X
Set Notation and Axioms of Probability Memory Hints: Intersection I AND I looks like A for And Union U OR + U looks like U for Union Complement NOT X = X = X' NOT NOT X = X = X'' = X Commutative Law A
More information(y 1, y 2 ) = 12 y3 1e y 1 y 2 /2, y 1 > 0, y 2 > 0 0, otherwise.
54 We are given the marginal pdfs of Y and Y You should note that Y gamma(4, Y exponential( E(Y = 4, V (Y = 4, E(Y =, and V (Y = 4 (a With U = Y Y, we have E(U = E(Y Y = E(Y E(Y = 4 = (b Because Y and
More informationContinuous Probability Spaces
Continuous Probability Spaces Ω is not countable. Outcomes can be any real number or part of an interval of R, e.g. heights, weights and lifetimes. Can not assign probabilities to each outcome and add
More informationSTATISTICS ( CODE NO. 08 ) PAPER I PART - I
STATISTICS ( CODE NO. 08 ) PAPER I PART - I 1. Descriptive Statistics Types of data - Concepts of a Statistical population and sample from a population ; qualitative and quantitative data ; nominal and
More informationProbability 1 (MATH 11300) lecture slides
Probability 1 (MATH 11300) lecture slides Márton Balázs School of Mathematics University of Bristol Autumn, 2015 December 16, 2015 To know... http://www.maths.bris.ac.uk/ mb13434/prob1/ m.balazs@bristol.ac.uk
More informationSTAT 414: Introduction to Probability Theory
STAT 414: Introduction to Probability Theory Spring 2016; Homework Assignments Latest updated on April 29, 2016 HW1 (Due on Jan. 21) Chapter 1 Problems 1, 8, 9, 10, 11, 18, 19, 26, 28, 30 Theoretical Exercises
More informationCS37300 Class Notes. Jennifer Neville, Sebastian Moreno, Bruno Ribeiro
CS37300 Class Notes Jennifer Neville, Sebastian Moreno, Bruno Ribeiro 2 Background on Probability and Statistics These are basic definitions, concepts, and equations that should have been covered in your
More informationWhy study probability? Set theory. ECE 6010 Lecture 1 Introduction; Review of Random Variables
ECE 6010 Lecture 1 Introduction; Review of Random Variables Readings from G&S: Chapter 1. Section 2.1, Section 2.3, Section 2.4, Section 3.1, Section 3.2, Section 3.5, Section 4.1, Section 4.2, Section
More informationElements of probability theory
The role of probability theory in statistics We collect data so as to provide evidentiary support for answers we give to our many questions about the world (and in our particular case, about the business
More informationDecision making and problem solving Lecture 1. Review of basic probability Monte Carlo simulation
Decision making and problem solving Lecture 1 Review of basic probability Monte Carlo simulation Why probabilities? Most decisions involve uncertainties Probability theory provides a rigorous framework
More informationReview. DS GA 1002 Statistical and Mathematical Models. Carlos Fernandez-Granda
Review DS GA 1002 Statistical and Mathematical Models http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall16 Carlos Fernandez-Granda Probability and statistics Probability: Framework for dealing with
More informationSTAT 418: Probability and Stochastic Processes
STAT 418: Probability and Stochastic Processes Spring 2016; Homework Assignments Latest updated on April 29, 2016 HW1 (Due on Jan. 21) Chapter 1 Problems 1, 8, 9, 10, 11, 18, 19, 26, 28, 30 Theoretical
More informationThings to remember when learning probability distributions:
SPECIAL DISTRIBUTIONS Some distributions are special because they are useful They include: Poisson, exponential, Normal (Gaussian), Gamma, geometric, negative binomial, Binomial and hypergeometric distributions
More informationProbability and Statistics
Kristel Van Steen, PhD 2 Montefiore Institute - Systems and Modeling GIGA - Bioinformatics ULg kristel.vansteen@ulg.ac.be Chapter 3: Parametric families of univariate distributions CHAPTER 3: PARAMETRIC
More informationActuarial Science Exam 1/P
Actuarial Science Exam /P Ville A. Satopää December 5, 2009 Contents Review of Algebra and Calculus 2 2 Basic Probability Concepts 3 3 Conditional Probability and Independence 4 4 Combinatorial Principles,
More informationRandomized Algorithms
Randomized Algorithms Prof. Tapio Elomaa tapio.elomaa@tut.fi Course Basics A new 4 credit unit course Part of Theoretical Computer Science courses at the Department of Mathematics There will be 4 hours
More informationFunctions, Graphs, Equations and Inequalities
CAEM DPP Learning Outcomes per Module Module Functions, Graphs, Equations and Inequalities Learning Outcomes 1. Functions, inverse functions and composite functions 1.1. concepts of function, domain and
More informationSTAT 7032 Probability. Wlodek Bryc
STAT 7032 Probability Wlodek Bryc Revised for Spring 2019 Printed: January 14, 2019 File: Grad-Prob-2019.TEX Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221 E-mail address:
More informationQuantitative Methods Chapter 0: Review of Basic Concepts 0.1 Business Applications (II) 0.2 Business Applications (III)
Quantitative Methods Chapter 0: Review of Basic Concepts 0.1 Business Applications (II) 0.1.1 Simple Interest 0.2 Business Applications (III) 0.2.1 Expenses Involved in Buying a Car 0.2.2 Expenses Involved
More informationCIVL Why are we studying probability and statistics? Learning Objectives. Basic Laws and Axioms of Probability
CIVL 3103 Basic Laws and Axioms of Probability Why are we studying probability and statistics? How can we quantify risks of decisions based on samples from a population? How should samples be selected
More informationMATH Notebook 5 Fall 2018/2019
MATH442601 2 Notebook 5 Fall 2018/2019 prepared by Professor Jenny Baglivo c Copyright 2004-2019 by Jenny A. Baglivo. All Rights Reserved. 5 MATH442601 2 Notebook 5 3 5.1 Sequences of IID Random Variables.............................
More informationStats Probability Theory
Stats 241.3 Probability Theory Instructor: Office: W.H.Laverty 235 McLean Hall Phone: 966-6096 Lectures: Evaluation: M T W Th F 1:30pm - 2:50pm Thorv 105 Lab: T W Th 3:00-3:50 Thorv 105 Assignments, Labs,
More informationLecture 2: Repetition of probability theory and statistics
Algorithms for Uncertainty Quantification SS8, IN2345 Tobias Neckel Scientific Computing in Computer Science TUM Lecture 2: Repetition of probability theory and statistics Concept of Building Block: Prerequisites:
More informationChapter Learning Objectives. Probability Distributions and Probability Density Functions. Continuous Random Variables
Chapter 4: Continuous Random Variables and Probability s 4-1 Continuous Random Variables 4-2 Probability s and Probability Density Functions 4-3 Cumulative Functions 4-4 Mean and Variance of a Continuous
More informationGrade Math (HL) Curriculum
Grade 11-12 Math (HL) Curriculum Unit of Study (Core Topic 1 of 7): Algebra Sequences and Series Exponents and Logarithms Counting Principles Binomial Theorem Mathematical Induction Complex Numbers Uses
More informationRecitation 2: Probability
Recitation 2: Probability Colin White, Kenny Marino January 23, 2018 Outline Facts about sets Definitions and facts about probability Random Variables and Joint Distributions Characteristics of distributions
More informationTopic 2: Probability & Distributions. Road Map Probability & Distributions. ECO220Y5Y: Quantitative Methods in Economics. Dr.
Topic 2: Probability & Distributions ECO220Y5Y: Quantitative Methods in Economics Dr. Nick Zammit University of Toronto Department of Economics Room KN3272 n.zammit utoronto.ca November 21, 2017 Dr. Nick
More informationSTA2603/205/1/2014 /2014. ry II. Tutorial letter 205/1/
STA263/25//24 Tutorial letter 25// /24 Distribution Theor ry II STA263 Semester Department of Statistics CONTENTS: Examination preparation tutorial letterr Solutions to Assignment 6 2 Dear Student, This
More informationProbability Models. 4. What is the definition of the expectation of a discrete random variable?
1 Probability Models The list of questions below is provided in order to help you to prepare for the test and exam. It reflects only the theoretical part of the course. You should expect the questions
More informationSTOCHASTIC PROBABILITY THEORY PROCESSES. Universities Press. Y Mallikarjuna Reddy EDITION
PROBABILITY THEORY STOCHASTIC PROCESSES FOURTH EDITION Y Mallikarjuna Reddy Department of Electronics and Communication Engineering Vasireddy Venkatadri Institute of Technology, Guntur, A.R < Universities
More informationFoundations of Probability and Statistics
Foundations of Probability and Statistics William C. Rinaman Le Moyne College Syracuse, New York Saunders College Publishing Harcourt Brace College Publishers Fort Worth Philadelphia San Diego New York
More informationStatistics for Managers Using Microsoft Excel/SPSS Chapter 4 Basic Probability And Discrete Probability Distributions
Statistics for Managers Using Microsoft Excel/SPSS Chapter 4 Basic Probability And Discrete Probability Distributions 1999 Prentice-Hall, Inc. Chap. 4-1 Chapter Topics Basic Probability Concepts: Sample
More informationPreface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of
Preface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of Probability Sampling Procedures Collection of Data Measures
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 informationCSE 312 Final Review: Section AA
CSE 312 TAs December 8, 2011 General Information General Information Comprehensive Midterm General Information Comprehensive Midterm Heavily weighted toward material after the midterm Pre-Midterm Material
More informationLectures on Elementary Probability. William G. Faris
Lectures on Elementary Probability William G. Faris February 22, 2002 2 Contents 1 Combinatorics 5 1.1 Factorials and binomial coefficients................. 5 1.2 Sampling with replacement.....................
More informationUQ, Semester 1, 2017, Companion to STAT2201/CIVL2530 Exam Formulae and Tables
UQ, Semester 1, 2017, Companion to STAT2201/CIVL2530 Exam Formulae and Tables To be provided to students with STAT2201 or CIVIL-2530 (Probability and Statistics) Exam Main exam date: Tuesday, 20 June 1
More informationMath Review Sheet, Fall 2008
1 Descriptive Statistics Math 3070-5 Review Sheet, Fall 2008 First we need to know about the relationship among Population Samples Objects The distribution of the population can be given in one of the
More informationApplications in Differentiation Page 3
Applications in Differentiation Page 3 Continuity and Differentiability Page 3 Gradients at Specific Points Page 5 Derivatives of Hybrid Functions Page 7 Derivatives of Composite Functions Page 8 Joining
More informationProblem 1. Problem 2. Problem 3. Problem 4
Problem Let A be the event that the fungus is present, and B the event that the staph-bacteria is present. We have P A = 4, P B = 9, P B A =. We wish to find P AB, to do this we use the multiplication
More informationChapter 5. Chapter 5 sections
1 / 43 sections Discrete univariate distributions: 5.2 Bernoulli and Binomial distributions Just skim 5.3 Hypergeometric distributions 5.4 Poisson distributions Just skim 5.5 Negative Binomial distributions
More information5. Conditional Distributions
1 of 12 7/16/2009 5:36 AM Virtual Laboratories > 3. Distributions > 1 2 3 4 5 6 7 8 5. Conditional Distributions Basic Theory As usual, we start with a random experiment with probability measure P on an
More informationProbability Distributions Columns (a) through (d)
Discrete Probability Distributions Columns (a) through (d) Probability Mass Distribution Description Notes Notation or Density Function --------------------(PMF or PDF)-------------------- (a) (b) (c)
More informationWeek 2. Review of Probability, Random Variables and Univariate Distributions
Week 2 Review of Probability, Random Variables and Univariate Distributions Probability Probability Probability Motivation What use is Probability Theory? Probability models Basis for statistical inference
More informationSESSION CLASS-XI SUBJECT : MATHEMATICS FIRST TERM
TERMWISE SYLLABUS SESSION-2018-19 CLASS-XI SUBJECT : MATHEMATICS MONTH July, 2018 to September 2018 CONTENTS FIRST TERM Unit-1: Sets and Functions 1. Sets Sets and their representations. Empty set. Finite
More informationFault-Tolerant Computer System Design ECE 60872/CS 590. Topic 2: Discrete Distributions
Fault-Tolerant Computer System Design ECE 60872/CS 590 Topic 2: Discrete Distributions Saurabh Bagchi ECE/CS Purdue University Outline Basic probability Conditional probability Independence of events Series-parallel
More informationCounting principles, including permutations and combinations.
1 Counting principles, including permutations and combinations. The binomial theorem: expansion of a + b n, n ε N. THE PRODUCT RULE If there are m different ways of performing an operation and for each
More informationStochastic Models of Manufacturing Systems
Stochastic Models of Manufacturing Systems Ivo Adan Organization 2/47 7 lectures (lecture of May 12 is canceled) Studyguide available (with notes, slides, assignments, references), see http://www.win.tue.nl/
More informationProgram of the entrance exam Wiskunde A
CENTRALE COMMISSIE VOORTENTAMEN WISKUNDE Program of the entrance exam Wiskunde A Valid from December 2018 The entrance exam Wiskunde A is taken as a written examination with open questions. The exam time
More informationMATHEMATICS SYLLABUS SECONDARY 6th YEAR
European Schools Office of the Secretary-General Pedagogical development Unit Ref.: 2010-D-601-en-2 Orig.: FR MATHEMATICS SYLLABUS SECONDARY 6th YEAR Elementary level 3 period/week course APPROVED BY THE
More informationMath 493 Final Exam December 01
Math 493 Final Exam December 01 NAME: ID NUMBER: Return your blue book to my office or the Math Department office by Noon on Tuesday 11 th. On all parts after the first show enough work in your exam booklet
More informationTHE QUEEN S UNIVERSITY OF BELFAST
THE QUEEN S UNIVERSITY OF BELFAST 0SOR20 Level 2 Examination Statistics and Operational Research 20 Probability and Distribution Theory Wednesday 4 August 2002 2.30 pm 5.30 pm Examiners { Professor R M
More informationLecture 1: Probability Fundamentals
Lecture 1: Probability Fundamentals IB Paper 7: Probability and Statistics Carl Edward Rasmussen Department of Engineering, University of Cambridge January 22nd, 2008 Rasmussen (CUED) Lecture 1: Probability
More informationLecture 1. Chapter 1. (Part I) Material Covered in This Lecture: Chapter 1, Chapter 2 ( ). 1. What is Statistics?
Lecture 1 (Part I) Material Covered in This Lecture: Chapter 1, Chapter 2 (2.1 --- 2.6). Chapter 1 1. What is Statistics? 2. Two definitions. (1). Population (2). Sample 3. The objective of statistics.
More informationProbability Notes. Compiled by Paul J. Hurtado. Last Compiled: September 6, 2017
Probability Notes Compiled by Paul J. Hurtado Last Compiled: September 6, 2017 About These Notes These are course notes from a Probability course taught using An Introduction to Mathematical Statistics
More informationCAM Ph.D. Qualifying Exam in Numerical Analysis CONTENTS
CAM Ph.D. Qualifying Exam in Numerical Analysis CONTENTS Preliminaries Round-off errors and computer arithmetic, algorithms and convergence Solutions of Equations in One Variable Bisection method, fixed-point
More informationtossing a coin selecting a card from a deck measuring the commuting time on a particular morning
2 Probability Experiment An experiment or random variable is any activity whose outcome is unknown or random upfront: tossing a coin selecting a card from a deck measuring the commuting time on a particular
More informationSTAT 3610: Review of Probability Distributions
STAT 3610: Review of Probability Distributions Mark Carpenter Professor of Statistics Department of Mathematics and Statistics August 25, 2015 Support of a Random Variable Definition The support of a random
More informationReview (probability, linear algebra) CE-717 : Machine Learning Sharif University of Technology
Review (probability, linear algebra) CE-717 : Machine Learning Sharif University of Technology M. Soleymani Fall 2012 Some slides have been adopted from Prof. H.R. Rabiee s and also Prof. R. Gutierrez-Osuna
More informationLecture 10: Probability distributions TUESDAY, FEBRUARY 19, 2019
Lecture 10: Probability distributions DANIEL WELLER TUESDAY, FEBRUARY 19, 2019 Agenda What is probability? (again) Describing probabilities (distributions) Understanding probabilities (expectation) Partial
More informationNEW YORK ALGEBRA TABLE OF CONTENTS
NEW YORK ALGEBRA TABLE OF CONTENTS CHAPTER 1 NUMBER SENSE & OPERATIONS TOPIC A: Number Theory: Properties of Real Numbers {A.N.1} PART 1: Closure...1 PART 2: Commutative Property...2 PART 3: Associative
More informationClosed book and notes. 120 minutes. Cover page, five pages of exam. No calculators.
IE 230 Seat # Closed book and notes. 120 minutes. Cover page, five pages of exam. No calculators. Score Final Exam, Spring 2005 (May 2) Schmeiser Closed book and notes. 120 minutes. Consider an experiment
More informationTheorem 2.1 (Caratheodory). A (countably additive) probability measure on a field has an extension. n=1
Chapter 2 Probability measures 1. Existence Theorem 2.1 (Caratheodory). A (countably additive) probability measure on a field has an extension to the generated σ-field Proof of Theorem 2.1. Let F 0 be
More information3. The Multivariate Hypergeometric Distribution
1 of 6 7/16/2009 6:47 AM Virtual Laboratories > 12. Finite Sampling Models > 1 2 3 4 5 6 7 8 9 3. The Multivariate Hypergeometric Distribution Basic Theory As in the basic sampling model, we start with
More informationJoint Distribution of Two or More Random Variables
Joint Distribution of Two or More Random Variables Sometimes more than one measurement in the form of random variable is taken on each member of the sample space. In cases like this there will be a few
More informationProbability and Applications
Chapter 5 Probability and Applications 5.2 SOME USEFUL DEFINITIONS Random experiment: a process that has an unknown outcome or outcomes that are known only after the process is completed. Event: an outcome
More informationA Probability Primer. A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes.
A Probability Primer A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes. Are you holding all the cards?? Random Events A random event, E,
More informationProbability and Stochastic Processes
Probability and Stochastic Processes A Friendly Introduction Electrical and Computer Engineers Third Edition Roy D. Yates Rutgers, The State University of New Jersey David J. Goodman New York University
More informationSTAT 712 MATHEMATICAL STATISTICS I
STAT 72 MATHEMATICAL STATISTICS I Fall 207 Lecture Notes Joshua M. Tebbs Department of Statistics University of South Carolina c by Joshua M. Tebbs TABLE OF CONTENTS Contents Probability Theory. Set Theory......................................2
More informationSummary of basic probability theory Math 218, Mathematical Statistics D Joyce, Spring 2016
8. For any two events E and F, P (E) = P (E F ) + P (E F c ). Summary of basic probability theory Math 218, Mathematical Statistics D Joyce, Spring 2016 Sample space. A sample space consists of a underlying
More informationChapter 1 Statistical Reasoning Why statistics? Section 1.1 Basics of Probability Theory
Chapter 1 Statistical Reasoning Why statistics? Uncertainty of nature (weather, earth movement, etc. ) Uncertainty in observation/sampling/measurement Variability of human operation/error imperfection
More informationMathematics Syllabus UNIT I ALGEBRA : 1. SETS, RELATIONS AND FUNCTIONS
Mathematics Syllabus UNIT I ALGEBRA : 1. SETS, RELATIONS AND FUNCTIONS (i) Sets and their Representations: Finite and Infinite sets; Empty set; Equal sets; Subsets; Power set; Universal set; Venn Diagrams;
More informationStat 426 : Homework 1.
Stat 426 : Homework 1. Moulinath Banerjee University of Michigan Announcement: The homework carries 120 points and contributes 10 points to the total grade. (1) A geometric random variable W takes values
More information