SHOW ALL WORK FOR CREDIT!!!

Size: px
Start display at page:

Download "SHOW ALL WORK FOR CREDIT!!!"

Transcription

1 MATH 1342ÞP04 FALL 2010 CHAPTERS 4, 5 & 6 NAME SHOW ALL WORK FOR CREDIT!!! 1. A study was done to investigate a possible relationship the circumference (in ft) and the height (in ft) of trees. The data collected from nine trees in Marshall, Minnesota is given below. Circumference (ft) Height (ft) a. Determine the linear correlation coefficient between circumference and the height. b. Find the least-squares regression line, treating the circumference as the explanatory variable ( B) and height as the response variable ( CÑÞ c. Suppose a tree has a circumference of 4.5 ft, use the least-squares prediction line to predict the height of the tree.

2 MATH 1342.P04 PAGE 2 2. The following probability model shows the distribution of doctoral degrees from U.S. universities in 2005 by area of study. Area of Study Probability Engineering!Þ"%) Physical Sciences!Þ"!! Life Sciences!Þ"(" Mathematics!Þ!#) Computer Sciences!Þ!#' Social Sciences Humanities Education Professional and other fields!þ"(#!þ""%!þ"%%!þ!*( a. What is the probability that a randomly selected doctoral candidate who earned a degree in 2005 studied mathematics? b. What is the probability that a randomly selected doctoral candidate who earned a degree in 2005 studied humanities or social sciences? 3. The probability that a randomly selected 59-year-old male will live to be 60 years old is!þ*)*#&, according to the National Vital Statistics Report, Vol. 58, No. 10. a. What is the probability that two randomly selected 59-year-old males will live to be 51 years old? b. What is the probability that at least one of three randomly selected 59-year-old males will not live to be 60 years old?

3 MATH 1342.P04 PAGE 3 4. The following table represents the employment status and gender of the civilian labor for ages 16 to 24 (in millions). Male Female Total Employed Unemployed Total a. What is the probability that a randomly selected 16- to 24 year old individual from the labor force is unemployed and male? b. What is the probability that a randomly selected 16- to 24 year old individual from the labor force is employed or female? c. What is the probability that a randomly selected 16- to 24 year old individual from the labor force is female given that they were employed? 5. To play the Cash Five Lottery you pick 5 numbers from 1 to 37. a. How many different ways are there to pick 5 numbers? b. If you match 4 of the 5 winning numbers in Cash Five you win $472. How many ways can you match 4 of the 5 winning numbers?

4 MATH 1342.P04 PAGE 4 6. A security code consists of two letters followed by three digits 0 through 9. How many codes are possible? 7. A family has six children. If this family has four girls and two boys, how many different birth and gender orders are possible? 8. Determine whether the following distribution is a discrete probability distribution. If not, state why. B T B!!Þ!"& "!!Þ#$) #!!Þ$(% $!!Þ$%" %!!Þ!)& 9. Three males with an X-linked genetic disorder have one child each. In the following probability distribution, the random variable \ is the number of children among the three who inherit the X-linked genetic disorder. Compute the mean, variance, and standard deviation of the random variable \. B T B!!Þ%#"* "!Þ%#"* #!Þ"%!' $!Þ!"&'

5 MATH 1342.P04 PAGE Clarinex-D is a medication whose purpose is to reduce the symptoms associated with a variety of allergies. In clinical trials of Clarinex-D, 5% of the patients in the study experienced insomnia as a side effect. A random sample of 20 Clarinex-D users is obtained, and the number of patents who experienced insomnia is recorded. a. Find the probability that exactly 6 experienced insomnia as a side effect. b. Find the probability that 10 or fewer experienced insomnia as a side effect. c. Find the probability that 8 or more experienced insomnia as a side effect. d. Calculate the mean and standard deviation for the number of users a sample of 20 that would experience insomnia as a side effect.

6 MATH 1342.P04 PAGE 6 Formulas and Rules For any event I, - T I œ " T I If E and F are mutually exclusive events, then T E or F œ T E T F If E and F are not mutually exclusive events, then T E or F œ T E T F T E and F If E and F are independent events, then T E and F œ T E T F If E and F are dependent events, then T E and F œ T E T FlE If E and F are two events, then T FlE œ T E and F T E Permutations with Nondistinct Items The number of permutations of 8 objects of which 8" are of one kind, 8# are of a second th kind,..., and 85 are of the 5 kind is given by 8x 8x " 8x # â 8x 5 Frequency Distributions Binomial Distribution TI-Calculator. œ # D D B TÐBÑ 5 œ B. TÐBÑ #. œ8 : 5 œè8 : ; T \ œ B œ binompdf 8, :, B T \ Ÿ B œ binomcdf 8, :, B

P(A) = Definitions. Overview. P - denotes a probability. A, B, and C - denote specific events. P (A) - Chapter 3 Probability

P(A) = Definitions. Overview. P - denotes a probability. A, B, and C - denote specific events. P (A) - Chapter 3 Probability Chapter 3 Probability Slide 1 Slide 2 3-1 Overview 3-2 Fundamentals 3-3 Addition Rule 3-4 Multiplication Rule: Basics 3-5 Multiplication Rule: Complements and Conditional Probability 3-6 Probabilities

More information

( ) P A B : Probability of A given B. Probability that A happens

( ) P A B : Probability of A given B. Probability that A happens A B A or B One or the other or both occurs At least one of A or B occurs Probability Review A B A and B Both A and B occur ( ) P A B : Probability of A given B. Probability that A happens given that B

More information

Topic 4 Probability. Terminology. Sample Space and Event

Topic 4 Probability. Terminology. Sample Space and Event Topic 4 Probability The Sample Space is the collection of all possible outcomes Experimental outcome An outcome from a sample space with one characteristic Event May involve two or more outcomes simultaneously

More information

Lecture Slides. Elementary Statistics Tenth Edition. by Mario F. Triola. and the Triola Statistics Series. Slide 1

Lecture Slides. Elementary Statistics Tenth Edition. by Mario F. Triola. and the Triola Statistics Series. Slide 1 Lecture Slides Elementary Statistics Tenth Edition and the Triola Statistics Series by Mario F. Triola Slide 1 4-1 Overview 4-2 Fundamentals 4-3 Addition Rule Chapter 4 Probability 4-4 Multiplication Rule:

More information

Lecture Slides. Elementary Statistics Eleventh Edition. by Mario F. Triola. and the Triola Statistics Series 4.1-1

Lecture Slides. Elementary Statistics Eleventh Edition. by Mario F. Triola. and the Triola Statistics Series 4.1-1 Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by Mario F. Triola 4.1-1 4-1 Review and Preview Chapter 4 Probability 4-2 Basic Concepts of Probability 4-3 Addition

More information

QUIZ 1 (CHAPTERS 1-4) SOLUTIONS MATH 119 FALL 2012 KUNIYUKI 105 POINTS TOTAL, BUT 100 POINTS

QUIZ 1 (CHAPTERS 1-4) SOLUTIONS MATH 119 FALL 2012 KUNIYUKI 105 POINTS TOTAL, BUT 100 POINTS QUIZ 1 (CHAPTERS 1-4) SOLUTIONS MATH 119 FALL 2012 KUNIYUKI 105 POINTS TOTAL, BUT 100 POINTS = 100% Show all work, simplify as appropriate, and use good form and procedure (as in class). Box in your final

More information

Probability and Probability Distributions. Dr. Mohammed Alahmed

Probability and Probability Distributions. Dr. Mohammed Alahmed Probability and Probability Distributions 1 Probability and Probability Distributions Usually we want to do more with data than just describing them! We might want to test certain specific inferences about

More information

University of Jordan Fall 2009/2010 Department of Mathematics

University of Jordan Fall 2009/2010 Department of Mathematics handouts Part 1 (Chapter 1 - Chapter 5) University of Jordan Fall 009/010 Department of Mathematics Chapter 1 Introduction to Introduction; Some Basic Concepts Statistics is a science related to making

More information

Binomial and Poisson Probability Distributions

Binomial and Poisson Probability Distributions Binomial and Poisson Probability Distributions Esra Akdeniz March 3, 2016 Bernoulli Random Variable Any random variable whose only possible values are 0 or 1 is called a Bernoulli random variable. What

More information

A SHORT INTRODUCTION TO PROBABILITY

A SHORT INTRODUCTION TO PROBABILITY A Lecture for B.Sc. 2 nd Semester, Statistics (General) A SHORT INTRODUCTION TO PROBABILITY By Dr. Ajit Goswami Dept. of Statistics MDKG College, Dibrugarh 19-Apr-18 1 Terminology The possible outcomes

More information

Counting principles, including permutations and combinations.

Counting 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 information

Midterm Review Honors ICM Name: Per: Remember to show work to receive credit! Circle your answers! Sets and Probability

Midterm Review Honors ICM Name: Per: Remember to show work to receive credit! Circle your answers! Sets and Probability Midterm Review Honors ICM Name: Per: Remember to show work to receive credit! Circle your answers! Unit 1 Sets and Probability 1. Let U denote the set of all the students at Green Hope High. Let D { x

More information

Multiple Choice Practice Set 1

Multiple Choice Practice Set 1 Multiple Choice Practice Set 1 This set of questions covers material from Chapter 1. Multiple choice is the same format as for the midterm. Q1. Two events each have probability 0.2 of occurring and are

More information

Announcements. Topics: To Do:

Announcements. Topics: To Do: Announcements Topics: In the Probability and Statistics module: - Sections 1 + 2: Introduction to Stochastic Models - Section 3: Basics of Probability Theory - Section 4: Conditional Probability; Law of

More information

Monty Hall Puzzle. Draw a tree diagram of possible choices (a possibility tree ) One for each strategy switch or no-switch

Monty Hall Puzzle. Draw a tree diagram of possible choices (a possibility tree ) One for each strategy switch or no-switch Monty Hall Puzzle Example: You are asked to select one of the three doors to open. There is a large prize behind one of the doors and if you select that door, you win the prize. After you select a door,

More information

Lecture 3: Probability

Lecture 3: Probability Lecture 3: Probability 28th of October 2015 Lecture 3: Probability 28th of October 2015 1 / 36 Summary of previous lecture Define chance experiment, sample space and event Introduce the concept of the

More information

Conditional Probability Solutions STAT-UB.0103 Statistics for Business Control and Regression Models

Conditional Probability Solutions STAT-UB.0103 Statistics for Business Control and Regression Models Conditional Probability Solutions STAT-UB.0103 Statistics for Business Control and Regression Models Counting (Review) 1. There are 10 people in a club. How many ways are there to choose the following:

More information

Conditional Probability

Conditional Probability Conditional Probability Terminology: The probability of an event occurring, given that another event has already occurred. P A B = ( ) () P A B : The probability of A given B. Consider the following table:

More information

Chapter 4 Probability

Chapter 4 Probability 4-1 Review and Preview Chapter 4 Probability 4-2 Basic Concepts of Probability 4-3 Addition Rule 4-4 Multiplication Rule: Basics 4-5 Multiplication Rule: Complements and Conditional Probability 4-6 Counting

More information

Two-Way ANOVA. Chapter 15

Two-Way ANOVA. Chapter 15 Two-Way ANOVA Chapter 15 Interaction Defined An interaction is present when the effects of one IV depend upon a second IV Interaction effect : The effect of each IV across the levels of the other IV When

More information

x3,..., Multiple Regression β q α, β 1, β 2, β 3,..., β q in the model can all be estimated by least square estimators

x3,..., Multiple Regression β q α, β 1, β 2, β 3,..., β q in the model can all be estimated by least square estimators Multiple Regression Relating a response (dependent, input) y to a set of explanatory (independent, output, predictor) variables x, x 2, x 3,, x q. A technique for modeling the relationship between variables.

More information

Data Analysis and Statistical Methods Statistics 651

Data Analysis and Statistical Methods Statistics 651 Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 6 (MWF) Conditional probabilities and associations Suhasini Subba Rao Review of previous lecture

More information

Chapter 6. Probability

Chapter 6. Probability Chapter 6 robability Suppose two six-sided die is rolled and they both land on sixes. Or a coin is flipped and it lands on heads. Or record the color of the next 20 cars to pass an intersection. These

More information

Section 4.2 Basic Concepts of Probability

Section 4.2 Basic Concepts of Probability Section 4.2 Basic Concepts of Probability 2012 Pearson Education, Inc. All rights reserved. 1 of 88 Section 4.2 Objectives Identify the sample space of a probability experiment Identify simple events Use

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan Introduction The markets can be thought of as a complex interaction of a large number of random processes,

More information

Chapter 01 : What is Statistics?

Chapter 01 : What is Statistics? Chapter 01 : What is Statistics? Feras Awad Data: The information coming from observations, counts, measurements, and responses. Statistics: The science of collecting, organizing, analyzing, and interpreting

More information

2.6 Tools for Counting sample points

2.6 Tools for Counting sample points 2.6 Tools for Counting sample points When the number of simple events in S is too large, manual enumeration of every sample point in S is tedious or even impossible. (Example) If S contains N equiprobable

More information

9/6/2016. Section 5.1 Probability. Equally Likely Model. The Division Rule: P(A)=#(A)/#(S) Some Popular Randomizers.

9/6/2016. Section 5.1 Probability. Equally Likely Model. The Division Rule: P(A)=#(A)/#(S) Some Popular Randomizers. Chapter 5: Probability and Discrete Probability Distribution Learn. Probability Binomial Distribution Poisson Distribution Some Popular Randomizers Rolling dice Spinning a wheel Flipping a coin Drawing

More information

Solution: There are 30 choices for the first person to leave, 29 for the second, etc. Thus this exodus can occur in. = P (30, 8) ways.

Solution: There are 30 choices for the first person to leave, 29 for the second, etc. Thus this exodus can occur in. = P (30, 8) ways. Math-2320 Assignment 7 Solutions Problem 1: (Section 7.1 Exercise 4) There are 30 people in a class learning about permutations. One after another, eight people gradually slip out the back door. In how

More information

Chapter 4. Probability

Chapter 4. Probability Chapter 4. Probability Chapter Problem: Are polygraph instruments effective as lie detector? Table 4-1 Results from Experiments with Polygraph Instruments Did the Subject Actually Lie? No (Did Not Lie)

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2014 Introduction The markets can be thought of as a complex interaction of a large number of random

More information

Q1 Own your learning with flash cards.

Q1 Own your learning with flash cards. For this data set, find the mean, mode, median and inter-quartile range. 2, 5, 6, 4, 7, 4, 7, 2, 8, 9, 4, 11, 9, 9, 6 Q1 For this data set, find the sample variance and sample standard deviation. 89, 47,

More information

Statistics 135 Fall 2008 Final Exam

Statistics 135 Fall 2008 Final Exam Name: SID: Statistics 135 Fall 2008 Final Exam Show your work. The number of points each question is worth is shown at the beginning of the question. There are 10 problems. 1. [2] The normal equations

More information

the yellow gene from each of the two parents he wrote Experiments in Plant

the yellow gene from each of the two parents he wrote Experiments in Plant CHAPTER PROBLEM Did Mendel s results from plant hybridization experiments contradict his theory? Gregor Mendel conducted original experiments offspring can have a yellow pod only if it inherits to study

More information

Chapter 3 Probability Chapter 3 Probability 3-1 Overview 3-2 Fundamentals 3-3 Addition Rule 3-4 Multiplication Rule: Basics

Chapter 3 Probability Chapter 3 Probability 3-1 Overview 3-2 Fundamentals 3-3 Addition Rule 3-4 Multiplication Rule: Basics Chapter 3 Probability 1 3-1 Overview 3-2 Fundamentals 3-3 Addition Rule Chapter 3 Probability 3-4 Multiplication Rule: Basics 2 Overview Objectives develop sound understanding of probability values used

More information

Basic Concepts of Probability. Section 3.1 Basic Concepts of Probability. Probability Experiments. Chapter 3 Probability

Basic Concepts of Probability. Section 3.1 Basic Concepts of Probability. Probability Experiments. Chapter 3 Probability Chapter 3 Probability 3.1 Basic Concepts of Probability 3.2 Conditional Probability and the Multiplication Rule 3.3 The Addition Rule 3.4 Additional Topics in Probability and Counting Section 3.1 Basic

More information

CSC Discrete Math I, Spring Discrete Probability

CSC Discrete Math I, Spring Discrete Probability CSC 125 - Discrete Math I, Spring 2017 Discrete Probability Probability of an Event Pierre-Simon Laplace s classical theory of probability: Definition of terms: An experiment is a procedure that yields

More information

Baye s theorem. Baye s Theorem Let E and F be two possible events of an experiment, then P (F ) P (E F ) P (F ) P (E F ) + P (F ) P (E F ).

Baye s theorem. Baye s Theorem Let E and F be two possible events of an experiment, then P (F ) P (E F ) P (F ) P (E F ) + P (F ) P (E F ). Baye s Theorem Assume that you know the probability that a child will be born with blond hair given that both his parents have blond hair. You might also be interested in knowing the probability that a

More information

AP Statistics Ch 6 Probability: The Study of Randomness

AP Statistics Ch 6 Probability: The Study of Randomness Ch 6.1 The Idea of Probability Chance behavior is unpredictable in the short run but has a regular and predictable pattern in the long run. We call a phenomenon random if individual outcomes are uncertain

More information

1 Introduction. 2 Example

1 Introduction. 2 Example Statistics: Multilevel modelling Richard Buxton. 2008. Introduction Multilevel modelling is an approach that can be used to handle clustered or grouped data. Suppose we are trying to discover some of the

More information

Probability. Chapter 1 Probability. A Simple Example. Sample Space and Probability. Sample Space and Event. Sample Space (Two Dice) Probability

Probability. Chapter 1 Probability. A Simple Example. Sample Space and Probability. Sample Space and Event. Sample Space (Two Dice) Probability Probability Chapter 1 Probability 1.1 asic Concepts researcher claims that 10% of a large population have disease H. random sample of 100 people is taken from this population and examined. If 20 people

More information

Chapter 1. Introduction. J. Kim (ISU) Chapter 1 1 / 1

Chapter 1. Introduction. J. Kim (ISU) Chapter 1 1 / 1 Chapter 1 Introduction J. Kim (ISU) Chapter 1 1 / 1 Sir Francis Galton (1822-1911) Galton was a polymath who made important contributions in many fields of science, including meteorology (the anti-cyclone

More information

1. Use Scenario 3-1. In this study, the response variable is

1. Use Scenario 3-1. In this study, the response variable is Chapter 8 Bell Work Scenario 3-1 The height (in feet) and volume (in cubic feet) of usable lumber of 32 cherry trees are measured by a researcher. The goal is to determine if volume of usable lumber can

More information

Topic 2 Probability. Basic probability Conditional probability and independence Bayes rule Basic reliability

Topic 2 Probability. Basic probability Conditional probability and independence Bayes rule Basic reliability Topic 2 Probability Basic probability Conditional probability and independence Bayes rule Basic reliability Random process: a process whose outcome can not be predicted with certainty Examples: rolling

More information

1.6/1.7 - Conditional Probability and Bayes Theorem

1.6/1.7 - Conditional Probability and Bayes Theorem 1.6/1.7 - Conditional Probability and Bayes Theorem Math 166-502 Blake Boudreaux Department of Mathematics Texas A&M University February 1, 2018 Blake Boudreaux (Texas A&M University) 1.6/1.7 - Conditional

More information

CHAPTER 5 Probabilistic Features of the Distributions of Certain Sample Statistics

CHAPTER 5 Probabilistic Features of the Distributions of Certain Sample Statistics CHAPTER 5 Probabilistic Features of the Distributions of Certain Sample Statistics Key Words Sampling Distributions Distribution of the Sample Mean Distribution of the difference between Two Sample Means

More information

Chapter 8 Sequences, Series, and Probability

Chapter 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 information

Econometrics of causal inference. Throughout, we consider the simplest case of a linear outcome equation, and homogeneous

Econometrics of causal inference. Throughout, we consider the simplest case of a linear outcome equation, and homogeneous Econometrics of causal inference Throughout, we consider the simplest case of a linear outcome equation, and homogeneous effects: y = βx + ɛ (1) where y is some outcome, x is an explanatory variable, and

More information

Quantitative Methods for Decision Making

Quantitative Methods for Decision Making January 14, 2012 Lecture 3 Probability Theory Definition Mutually exclusive events: Two events A and B are mutually exclusive if A B = φ Definition Special Addition Rule: Let A and B be two mutually exclusive

More information

CHAPTER 4 PROBABILITY AND PROBABILITY DISTRIBUTIONS

CHAPTER 4 PROBABILITY AND PROBABILITY DISTRIBUTIONS CHAPTER 4 PROBABILITY AND PROBABILITY DISTRIBUTIONS 4.2 Events and Sample Space De nition 1. An experiment is the process by which an observation (or measurement) is obtained Examples 1. 1: Tossing a pair

More information

Probabilistic models

Probabilistic models Kolmogorov (Andrei Nikolaevich, 1903 1987) put forward an axiomatic system for probability theory. Foundations of the Calculus of Probabilities, published in 1933, immediately became the definitive formulation

More information

Text: Brualdi, Introductory Combinatorics 5th Ed. Prof: Paul Terwilliger Selected solutions II for Chapter 2

Text: Brualdi, Introductory Combinatorics 5th Ed. Prof: Paul Terwilliger Selected solutions II for Chapter 2 Math 7 Text: Brualdi, Introductory Combinatorics th Ed Prof: Paul Terwilliger Selected solutions II for Chapter 0 We proceed in stages: The answer is! pick gender to the parent s right order the girls

More information

Topic 5: Probability. 5.4 Combined Events and Conditional Probability Paper 1

Topic 5: Probability. 5.4 Combined Events and Conditional Probability Paper 1 Topic 5: Probability Standard Level 5.4 Combined Events and Conditional Probability Paper 1 1. In a group of 16 students, 12 take art and 8 take music. One student takes neither art nor music. The Venn

More information

Final Examination. Your name: Circle the name of your Tutorial Instructor: David Hanson Jelani Sayan

Final Examination. Your name: Circle the name of your Tutorial Instructor: David Hanson Jelani Sayan Massachusetts Institute of Technology 6.042J/18.062J, Fall 05: Mathematics for Computer Science December 21 Prof. Albert R. Meyer and Prof. Ronitt Rubinfeld revised December 22, 2005, 1118 minutes Circle

More information

MATH 227 CP 7 SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.

MATH 227 CP 7 SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. MATH 227 CP 7 SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Find the mean, µ, for the binomial distribution which has the stated values of n and p.

More information

green green green/green green green yellow green/yellow green yellow green yellow/green green yellow yellow yellow/yellow yellow

green green green/green green green yellow green/yellow green yellow green yellow/green green yellow yellow yellow/yellow yellow CHAPTER PROBLEM Did Mendel s results from plant hybridization experiments contradict his theory? Gregor Mendel conducted original experiments to study the genetic traits of pea plants. In 1865 he wrote

More information

Solution: Solution: Solution:

Solution: Solution: Solution: Chapter 5: Exponential and Logarithmic Functions a. The exponential growth function is y = f(t) = ab t, where a = 2000 because the initial population is 2000 squirrels The annual growth rate is 3% per

More information

Senior Math Circles March 3, 2010 Counting Techniques and Probability II

Senior Math Circles March 3, 2010 Counting Techniques and Probability II 1 University of Waterloo Faculty of Mathematics Senior Math Circles March 3, 2010 Counting Techniques and Probability II Centre for Education in Mathematics and Computing Counting Rules Multiplication

More information

Chapter 2 Statistics. Mean, Median, Mode, and Range Definitions

Chapter 2 Statistics. Mean, Median, Mode, and Range Definitions M a t h C h a p t e r 2 S t a t i s t i c s P a g e 1 of 16 Chapter 2 Statistics Mean, Median, Mode, and Range Definitions Mean : The "Mean" is computed by adding all of the numbers in the data together

More information

What is Probability? Probability. Sample Spaces and Events. Simple Event

What is Probability? Probability. Sample Spaces and Events. Simple Event What is Probability? Probability Peter Lo Probability is the numerical measure of likelihood that the event will occur. Simple Event Joint Event Compound Event Lies between 0 & 1 Sum of events is 1 1.5

More information

Descriptive Statistics Class Practice [133 marks]

Descriptive Statistics Class Practice [133 marks] Descriptive Statistics Class Practice [133 marks] The weekly wages (in dollars) of 80 employees are displayed in the cumulative frequency curve below. 1a. (i) (ii) Write down the median weekly wage. Find

More information

Population Aging, Labor Demand, and the Structure of Wages

Population Aging, Labor Demand, and the Structure of Wages Population Aging, Labor Demand, and the Structure of Wages Margarita Sapozhnikov 1 Robert K. Triest 2 1 CRA International 2 Federal Reserve Bank of Boston Assessing the Impact of New England s Demographics

More information

Probability Theory The Binomial and Poisson Distributions. Sections 5.2 and 5.3

Probability Theory The Binomial and Poisson Distributions. Sections 5.2 and 5.3 Probability Theory The Binomial and Poisson Distributions Sections 5.2 and 5.3 Models for count data The binomial distributions provide a theoretical model for count data having a fixed maximum Examples:

More information

2016 Preliminary Examination II Pre-University 3

2016 Preliminary Examination II Pre-University 3 016 Preliminary Eamination II Pre-University 3 MATHEMATICS 9740/0 Paper 1 September 016 Additional Materials: Answer Paper List of Formulae (MF 15) 3 hours READ THESE INSTRUCTIONS FIRST Write your name

More information

Module 8 Probability

Module 8 Probability Module 8 Probability Probability is an important part of modern mathematics and modern life, since so many things involve randomness. The ClassWiz is helpful for calculating probabilities, especially those

More information

Practice problems from chapters 2 and 3

Practice problems from chapters 2 and 3 Practice problems from chapters and 3 Question-1. For each of the following variables, indicate whether it is quantitative or qualitative and specify which of the four levels of measurement (nominal, ordinal,

More information

MCB1007 Introduction to Probability and Statistics. First Midterm. Fall Solutions

MCB1007 Introduction to Probability and Statistics. First Midterm. Fall Solutions İstanbul Kültür University MCB7 Introduction to Probability and Statistics First Midterm Fall 4-5 Solutions Directions You have 9 minutes to complete the eam Please do not leave the eamination room in

More information

Chapter 2 Solutions Page 12 of 28

Chapter 2 Solutions Page 12 of 28 Chapter 2 Solutions Page 12 of 28 2.34 Yes, a stem-and-leaf plot provides sufficient information to determine whether a dataset contains an outlier. Because all individual values are shown, it is possible

More information

DSST Principles of Statistics

DSST Principles of Statistics DSST Principles of Statistics Time 10 Minutes 98 Questions Each incomplete statement is followed by four suggested completions. Select the one that is best in each case. 1. Which of the following variables

More information

Probability 5-4 The Multiplication Rules and Conditional Probability

Probability 5-4 The Multiplication Rules and Conditional Probability Outline Lecture 8 5-1 Introduction 5-2 Sample Spaces and 5-3 The Addition Rules for 5-4 The Multiplication Rules and Conditional 5-11 Introduction 5-11 Introduction as a general concept can be defined

More information

1. Poisson distribution is widely used in statistics for modeling rare events.

1. Poisson distribution is widely used in statistics for modeling rare events. Discrete probability distributions - Class 5 January 20, 2014 Debdeep Pati Poisson distribution 1. Poisson distribution is widely used in statistics for modeling rare events. 2. Ex. Infectious Disease

More information

Introduction to Probability, Fall 2009

Introduction to Probability, Fall 2009 Introduction to Probability, Fall 2009 Math 30530 Review questions for exam 1 solutions 1. Let A, B and C be events. Some of the following statements are always true, and some are not. For those that are

More information

Discrete Probability

Discrete Probability MAT 258 Discrete Mathematics Discrete Probability Kenneth H. Rosen and Kamala Krithivasan Discrete Mathematics 7E Global Edition Chapter 7 Reproduced without explicit consent Fall 2016 Week 11 Probability

More information

It applies to discrete and continuous random variables, and a mix of the two.

It applies to discrete and continuous random variables, and a mix of the two. 3. Bayes Theorem 3.1 Bayes Theorem A straightforward application of conditioning: using p(x, y) = p(x y) p(y) = p(y x) p(x), we obtain Bayes theorem (also called Bayes rule) p(x y) = p(y x) p(x). p(y)

More information

DRAFT. M118 Exam Jam Concise. Contents. Chapter 2: Set Theory 2. Chapter 3: Combinatorics 3. Chapter 4: Probability 4. Chapter 5: Statistics 6

DRAFT. M118 Exam Jam Concise. Contents. Chapter 2: Set Theory 2. Chapter 3: Combinatorics 3. Chapter 4: Probability 4. Chapter 5: Statistics 6 Contents Chapter 2: Set Theory 2 Chapter 3: Combinatorics 3 Chapter 4: Probability 4 Chapter 5: Statistics 6 Chapter 6: Linear Equations and Matrix Algebra 8 Chapter 7: Linear Programming: Graphical Solutions

More information

M118 Exam Jam. Contents. Chapter 2: Set Theory 2. Chapter 3: Combinatorics 5. Chapter 4: Probability 7. Chapter 5: Statistics 12

M118 Exam Jam. Contents. Chapter 2: Set Theory 2. Chapter 3: Combinatorics 5. Chapter 4: Probability 7. Chapter 5: Statistics 12 Contents Chapter 2: Set Theory 2 Chapter 3: Combinatorics 5 Chapter 4: Probability 7 Chapter 5: Statistics 12 Chapter 6: Linear Equations and Matrix Algebra 17 Chapter 7: Linear Programming: Graphical

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. MGF 1106 Math for Liberal Arts I Summer 2008 - Practice Final Exam Dr. Schnackenberg If you do not agree with the given answers, answer "E" for "None of the above". MULTIPLE CHOICE. Choose the one alternative

More information

CS Homework 2: Combinatorics & Discrete Events Due Date: September 25, 2018 at 2:20 PM

CS Homework 2: Combinatorics & Discrete Events Due Date: September 25, 2018 at 2:20 PM CS1450 - Homework 2: Combinatorics & Discrete Events Due Date: September 25, 2018 at 2:20 PM Question 1 A website allows the user to create an 8-character password that consists of lower case letters (a-z)

More information

Gender Recognition Act Survey ONLINE Fieldwork: 19th-21st October 2018

Gender Recognition Act Survey ONLINE Fieldwork: 19th-21st October 2018 Recognition Act Survey ONLINE Fieldwork: thst October 0 Table Q. Do you think that those who wish to legally change their gender on official documentation (e.g. birth certificate, passport) should or should

More information

Math 1040 Sample Final Examination. Problem Points Score Total 200

Math 1040 Sample Final Examination. Problem Points Score Total 200 Name: Math 1040 Sample Final Examination Relax and good luck! Problem Points Score 1 25 2 25 3 25 4 25 5 25 6 25 7 25 8 25 Total 200 1. (25 points) The systolic blood pressures of 20 elderly patients in

More information

Conditional Probability

Conditional Probability Test 1 Results You will get back your test 1 papers on Friday. There is a generous nonlinear curve of the scores: if x is your raw score, your grade out of 100 can be computed as 100x x x = 10 44 = 50

More information

2) For a normal distribution, the skewness and kurtosis measures are as follows: A) 1.96 and 4 B) 1 and 2 C) 0 and 3 D) 0 and 0

2) For a normal distribution, the skewness and kurtosis measures are as follows: A) 1.96 and 4 B) 1 and 2 C) 0 and 3 D) 0 and 0 Introduction to Econometrics Midterm April 26, 2011 Name Student ID MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. (5,000 credit for each correct

More information

Unit 1: Statistics and Probability (Calculator)

Unit 1: Statistics and Probability (Calculator) Write your name here Surname Other names Edexcel GCSE Centre Number Candidate Number Mathematics B Unit 1: Statistics and Probability (Calculator) Monday 11 June 2012 Afternoon Time: 1 hour 15 minutes

More information

4-1 BASIC CONCEPTS OF PROBABILITY

4-1 BASIC CONCEPTS OF PROBABILITY CHAPTER 4 4-1 BASIC CONCEPTS OF PROBABILITY Identify probabilities as values between 0 and 1, and interpret those values as expressions of likelihood of events Develop the ability to calculate probabilities

More information

Statistical Theory 1

Statistical Theory 1 Statistical Theory 1 Set Theory and Probability Paolo Bautista September 12, 2017 Set Theory We start by defining terms in Set Theory which will be used in the following sections. Definition 1 A set is

More information

Utilizing Hierarchical Linear Modeling in Evaluation: Concepts and Applications

Utilizing Hierarchical Linear Modeling in Evaluation: Concepts and Applications Utilizing Hierarchical Linear Modeling in Evaluation: Concepts and Applications C.J. McKinney, M.A. Antonio Olmos, Ph.D. Kate DeRoche, M.A. Mental Health Center of Denver Evaluation 2007: Evaluation and

More information

Basic Statistics for SGPE Students Part II: Probability theory 1

Basic Statistics for SGPE Students Part II: Probability theory 1 Basic Statistics for SGPE Students Part II: Probability theory 1 Mark Mitchell mark.mitchell@ed.ac.uk Nicolai Vitt n.vitt@ed.ac.uk University of Edinburgh September 2016 1 Thanks to Achim Ahrens, Anna

More information

Instructions. Information. Advice

Instructions. Information. Advice Instructions Use black ink 7C or ball-point pen. Fill in the boxes at the top of this page with your name, centre number and candidate number. Answer all questions. Answer the questions in the spaces provided

More information

Topic 3 Populations and Samples

Topic 3 Populations and Samples BioEpi540W Populations and Samples Page 1 of 33 Topic 3 Populations and Samples Topics 1. A Feeling for Populations v Samples 2 2. Target Populations, Sampled Populations, Sampling Frames 5 3. On Making

More information

Chapter 6 Continuous Probability Distributions

Chapter 6 Continuous Probability Distributions Continuous Probability Distributions Learning Objectives 1. Understand the difference between how probabilities are computed for discrete and continuous random variables. 2. Know how to compute probability

More information

Unit 7: Multiple linear regression 1. Introduction to multiple linear regression

Unit 7: Multiple linear regression 1. Introduction to multiple linear regression Announcements Unit 7: Multiple linear regression 1. Introduction to multiple linear regression Sta 101 - Fall 2017 Duke University, Department of Statistical Science Work on your project! Due date- Sunday

More information

MATH STUDENT BOOK. 12th Grade Unit 9

MATH STUDENT BOOK. 12th Grade Unit 9 MATH STUDENT BOOK 12th Grade Unit 9 Unit 9 COUNTING PRINCIPLES MATH 1209 COUNTING PRINCIPLES INTRODUCTION 1. PROBABILITY DEFINITIONS, SAMPLE SPACES, AND PROBABILITY ADDITION OF PROBABILITIES 11 MULTIPLICATION

More information

3/30/2009. Probability Distributions. Binomial distribution. TI-83 Binomial Probability

3/30/2009. Probability Distributions. Binomial distribution. TI-83 Binomial Probability Random variable The outcome of each procedure is determined by chance. Probability Distributions Normal Probability Distribution N Chapter 6 Discrete Random variables takes on a countable number of values

More information

15.1 The Regression Model: Analysis of Residuals

15.1 The Regression Model: Analysis of Residuals 15.1 The Regression Model: Analysis of Residuals Tom Lewis Fall Term 2009 Tom Lewis () 15.1 The Regression Model: Analysis of Residuals Fall Term 2009 1 / 12 Outline 1 The regression model 2 Estimating

More information

Homework (due Wed, Oct 27) Chapter 7: #17, 27, 28 Announcements: Midterm exams keys on web. (For a few hours the answer to MC#1 was incorrect on

Homework (due Wed, Oct 27) Chapter 7: #17, 27, 28 Announcements: Midterm exams keys on web. (For a few hours the answer to MC#1 was incorrect on Homework (due Wed, Oct 27) Chapter 7: #17, 27, 28 Announcements: Midterm exams keys on web. (For a few hours the answer to MC#1 was incorrect on Version A.) No grade disputes now. Will have a chance to

More information

Random variables. Lecture 5 - Discrete Distributions. Discrete Probability distributions. Example - Discrete probability model

Random variables. Lecture 5 - Discrete Distributions. Discrete Probability distributions. Example - Discrete probability model Random Variables Random variables Lecture 5 - Discrete Distributions Sta02 / BME02 Colin Rundel Setember 8, 204 A random variable is a numeric uantity whose value deends on the outcome of a random event

More information

Probability P{E} Example Consider. Find P{HH}. simultaneously. = # ways E occurs

Probability P{E} Example Consider. Find P{HH}. simultaneously. = # ways E occurs Probability and the Binomial Distribution Definition: A probability is the chance of some event, E, occurring in a specified manner. NOTATION: P{E} We can view probabilitie es from a Relative Frequency

More information

Probabilistic models

Probabilistic models Probabilistic models Kolmogorov (Andrei Nikolaevich, 1903 1987) put forward an axiomatic system for probability theory. Foundations of the Calculus of Probabilities, published in 1933, immediately became

More information

Sampling WITHOUT replacement, Order IS important Number of Samples = 6

Sampling WITHOUT replacement, Order IS important Number of Samples = 6 : Different strategies sampling 2 out of numbers {1,2,3}: Sampling WITHOUT replacement, Order IS important Number of Samples = 6 (1,2) (1,3) (2,1) (2,3) (3,1) (3,2) : Different strategies sampling 2 out

More information

(quantitative or categorical variables) Numerical descriptions of center, variability, position (quantitative variables)

(quantitative or categorical variables) Numerical descriptions of center, variability, position (quantitative variables) 3. Descriptive Statistics Describing data with tables and graphs (quantitative or categorical variables) Numerical descriptions of center, variability, position (quantitative variables) Bivariate descriptions

More information