Business Data Analysis, A17, Test 2
|
|
- Bethany Arnold
- 5 years ago
- Views:
Transcription
1 Business Data Analysis, A17, Test 2 Name: Student number 1. (2.5 marks) An executive is considering out of town business trip on Friday. She recognizes that at least one crises could occur at the company on the day she is gone. From previous experience she has constructed a probability distribution for the number of crises in a day at the company. Number of crises Probability a) Compute the expected value of the number of crises in a day. b) Compute the standard deviation.
2 2. (3 marks) Cathy and David own and operate a small bakery. They only bake and sell bread and pastries. Since the bakery is small it only accepts cash or Interac and 60% of their customers pay with cash. Of the customers who pay with cash 55% buy only bread, 15% buy only pastries and the rest buy both bread and pastries. Of the customers who pay with Interac 30% buy only bread, 25% buy only pastries and the rest buy both bread and pastries. a) Sketch a probability tree summarizing this information. b) What is the probability that a customer who bought both bread and pastries paid with Interac? c) What is the probability that a customer will buy pastries (only pastries or with bread)? 2
3 3. (3 marks) The contingency table below presents the results of a survey of 200 executives from 4 Canadian cities. The executives were asked to identify the geographic locale of their company and their company s industry type. The executives were only allowed to select one locale and one industry type. Toronto Calgary Vancouver Montreal Finance Manufacturing Communications Suppose that a respondent to this survey is selected at random. a) What is the probability that the respondent is from Montreal or from Toronto? b) What is the probability that the respondent is from Toronto or works in finance? c) What is the probability that the respondent is from Calgary if the respondent works in communications? d) What is the probability that the respondent works in communications if the respondent is from Calgary? e) Are industry type and location independent? Give a quantitative argument. 3
4 4. (3 marks) Arthur Andersen Enterprise group concluded a survey of US small business owners to determine the challenges for growth for their business. The top challenge, selected by 46% of small-business owners was the economy. A close second was finding qualified workers (37%). Suppose that 15% of the respondents selected both the economy and finding qualified workers as challenges for growth. A small business owner is selected at random. a) What is the probability that the owner believes that finding qualified workers is a challenge if the owner believes the economy is a challenge? b) What is the probability that the owner does not believe that the economy is a challenge if the owner believes finding qualified workers is a challenge? c) What is the probability that the owner believes that neither the economy is a challenge nor finding qualified workers is a challenge? 4
5 5. (2.5 marks) You own a company which employs 18 electricians. Parts of upstate New York have lost power due to a storm and you have volunteered to send some of your electricians to help. a) In how many ways can you select a group of 10 of your electricians to send to NY? b) In how many ways can you select 10 of your electricians to go to 10 different villages in NY? The license plates in NY have three letters followed by four numbers. c) How many NY license plates are possible? d) If one of your trucks got slightly dented by a car with NY license plates and the electrician who drove the truck only remembers that the plates of the offender start with KZ and end on either 5 or 6, how many NY license plates obey these constraints? 5
6 6. (2 marks) A bank branch has an average arrival rate of 3.2 customers every 4 minutes. a) What is the probability that no customers will arrive in two minutes? b) What is the probability that precisely 25 customers will arrive in half an hour? 7. (2 marks) You are a home insurance claims adjuster and currently you have 48 open claims of which 29 are in the city and 19 are in the suburbs. You will visit six of the houses today, selected at random. a) What is the probability that four of the houses selected for a visit are in the city? b) What is the probability that less than three of the houses you will visit today are in the suburbs? c) What is the expected value and the standard deviation for the number of houses in the suburbs you will visit today? 6
7 8. (2 marks) According to the Financial Planners Standards Council, 31% of certified financial planners (CFP s) earn $150,000 or more per year. Suppose that a random sample of 15 CFP s is selected. a) What is the probability that more than three CFP s in this sample earn more than $150,000 per year? b) What is the expected number of CFP s in this sample who earn more than $150,000 per year and what is the standard deviation? Various Formulas p(a B) = p(a) + p(b) p(a B) p(a B) = p(a)p(b A) p(a B) = p(b A)p(A) p(b) np k = n! (n k)!, nc k = n! (n k)!k! E(X) = xp(x), V ar(x) = x 2 p(x) E(X) 2, σ = V ar(x) p(x) = n C x p x (1 p) n x, E(X) = np, V ar(x) = np(1 p) p(x) = k C x N k C n x NC n, E(X) = np, V ar(x) = np(1 p) N n N 1, p = k N. p(x) = e λ λ x, E(X) = λ, V ar(x) = λ. x! 7
PROBABILITY AND STATISTICS, A16, TEST 1
PROBABILITY AND STATISTICS, A16, TEST 1 Name: Student number (1) (1.5 marks) i) Let A and B be mutually exclusive events with p(a) = 0.7 and p(b) = 0.2. Determine p(a B ) and also p(a B). ii) Let C and
More informationOutcome Person Person 1 Agree Agree 2 Disagree Disagree 3 Agree Disagree 4 Disagree Agree
Chapter 5 Exercise 1/ Some people are in favour of reducing federal taxes to increase consumer spending and others are against it. Two persons are selected and their opinions are recorded. Assuming no
More informationThe Normal Probability Distribution
1 The Normal Probability Distribution Key Definitions Probability Density Function: An equation used to compute probabilities for continuous random variables where the output value is greater than zero
More informationExamples CH 4 X P(X).2.3?.2
Examples CH 4 1. Consider the discrete probability distribution when answering the following question. X 2 4 5 10 P(X).2.3?.2 a. Find the probability that X is large than 2. b. Calculate the mean and variance
More information(c) The probability that a randomly selected driver having a California drivers license
Statistics Test 2 Name: KEY 1 Classify each statement as an example of classical probability, empirical probability, or subjective probability (a An executive for the Krusty-O cereal factory makes an educated
More informationDiscrete Probability Distribution
1 Discrete Probability Distribution Key Definitions Discrete Random Variable: Has a countable number of values. This means that each data point is distinct and separate. Continuous Random Variable: Has
More informationEcon 6900: Statistical Problems. Instructor: Yogesh Uppal
Econ 6900: Statistical Problems Instructor: Yogesh Uppal Email: yuppal@ysu.edu Lecture Slides 4 Random Variables Probability Distributions Discrete Distributions Discrete Uniform Probability Distribution
More informationChapter 3 - Lecture 5 The Binomial Probability Distribution
Chapter 3 - Lecture 5 The Binomial Probability October 12th, 2009 Experiment Examples Moments and moment generating function of a Binomial Random Variable Outline Experiment Examples A binomial experiment
More informationUnit 04 Review. Probability Rules
Unit 04 Review Probability Rules A sample space contains all the possible outcomes observed in a trial of an experiment, a survey, or some random phenomenon. The sum of the probabilities for all possible
More informationThe Uniform Distribution
Connexions module: m46972 The Uniform Distribution OpenStax College This work is produced by The Connexions Project and licensed under the Creative Commons Attribution License 3.0 The uniform distribution
More informationAMS7: WEEK 4. CLASS 3
AMS7: WEEK 4. CLASS 3 Sampling distributions and estimators. Central Limit Theorem Normal Approximation to the Binomial Distribution Friday April 24th, 2015 Sampling distributions and estimators REMEMBER:
More informationA continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x)
Section 6-2 I. Continuous Probability Distributions A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x) to represent a probability density
More informationMAS1403. Quantitative Methods for Business Management. Semester 1, Module leader: Dr. David Walshaw
MAS1403 Quantitative Methods for Business Management Semester 1, 2018 2019 Module leader: Dr. David Walshaw Additional lecturers: Dr. James Waldren and Dr. Stuart Hall Announcements This week is a computer
More informationSTA 6166 Fall 2007 Web-based Course. Notes 10: Probability Models
STA 6166 Fall 2007 Web-based Course 1 Notes 10: Probability Models We first saw the normal model as a useful model for the distribution of some quantitative variables. We ve also seen that if we make a
More informationChapter 6 Continuous Probability Distributions. Learning objectives
Chapter 6 Continuous s Slide 1 Learning objectives 1. Understand continuous probability distributions 2. Understand Uniform distribution 3. Understand Normal distribution 3.1. Understand Standard normal
More information4.2 Bernoulli Trials and Binomial Distributions
Arkansas Tech University MATH 3513: Applied Statistics I Dr. Marcel B. Finan 4.2 Bernoulli Trials and Binomial Distributions A Bernoulli trial 1 is an experiment with exactly two outcomes: Success and
More informationECON 214 Elements of Statistics for Economists 2016/2017
ECON 214 Elements of Statistics for Economists 2016/2017 Topic Probability Distributions: Binomial and Poisson Distributions Lecturer: Dr. Bernardin Senadza, Dept. of Economics bsenadza@ug.edu.gh College
More informationBinomial Random Variables
Models for Counts Solutions COR1-GB.1305 Statistics and Data Analysis Binomial Random Variables 1. A certain coin has a 25% of landing heads, and a 75% chance of landing tails. (a) If you flip the coin
More informationChapter 3 - Lecture 3 Expected Values of Discrete Random Va
Chapter 3 - Lecture 3 Expected Values of Discrete Random Variables October 5th, 2009 Properties of expected value Standard deviation Shortcut formula Properties of the variance Properties of expected value
More informationLecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial
Lecture 23 STAT 225 Introduction to Probability Models April 4, 2014 approximation Whitney Huang Purdue University 23.1 Agenda 1 approximation 2 approximation 23.2 Characteristics of the random variable:
More informationStatistics 6 th Edition
Statistics 6 th Edition Chapter 5 Discrete Probability Distributions Chap 5-1 Definitions Random Variables Random Variables Discrete Random Variable Continuous Random Variable Ch. 5 Ch. 6 Chap 5-2 Discrete
More informationSection 7.5 The Normal Distribution. Section 7.6 Application of the Normal Distribution
Section 7.6 Application of the Normal Distribution A random variable that may take on infinitely many values is called a continuous random variable. A continuous probability distribution is defined by
More information8.1 Binomial Distributions
8.1 Binomial Distributions The Binomial Setting The 4 Conditions of a Binomial Setting: 1.Each observation falls into 1 of 2 categories ( success or fail ) 2 2.There is a fixed # n of observations. 3.All
More informationUsing the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the
Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the mean, use the CLT for the mean. If you are being asked to
More informationDepartment of Quantitative Methods & Information Systems. Business Statistics. Chapter 6 Normal Probability Distribution QMIS 120. Dr.
Department of Quantitative Methods & Information Systems Business Statistics Chapter 6 Normal Probability Distribution QMIS 120 Dr. Mohammad Zainal Chapter Goals After completing this chapter, you should
More informationNo, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation.
1) If n 100 and p 0.02 in a binomial experiment, does this satisfy the rule for a normal approximation? Why or why not? No, because np 100(0.02) 2. The value of np must be greater than or equal to 5 to
More informationChapter 7: Sampling Distributions Chapter 7: Sampling Distributions
Chapter 7: Sampling Distributions Objectives: Students will: Define a sampling distribution. Contrast bias and variability. Describe the sampling distribution of a proportion (shape, center, and spread).
More informationMEMORIAL UNIVERSITY OF NEWFOUNDLAND DEPARTMENT OF MATHEMATICS AND STATISTICS MIDTERM EXAM - STATISTICS FALL 2014, SECTION 005
MEMORIAL UNIVERSITY OF NEWFOUNDLAND DEPARTMENT OF MATHEMATICS AND STATISTICS MIDTERM EXAM - STATISTICS 2550 - FALL 2014, SECTION 005 Instructor: A. Oyet Date: October 16, 2014 Name(Surname First): Student
More informationMean of a Discrete Random variable. Suppose that X is a discrete random variable whose distribution is : :
Dr. Kim s Note (December 17 th ) The values taken on by the random variable X are random, but the values follow the pattern given in the random variable table. What is a typical value of a random variable
More informationMA 1125 Lecture 14 - Expected Values. Wednesday, October 4, Objectives: Introduce expected values.
MA 5 Lecture 4 - Expected Values Wednesday, October 4, 27 Objectives: Introduce expected values.. Means, Variances, and Standard Deviations of Probability Distributions Two classes ago, we computed the
More informationAP Statistics Test 5
AP Statistics Test 5 Name: Date: Period: ffl If X is a discrete random variable, the the mean of X and the variance of X are given by μ = E(X) = X xp (X = x); Var(X) = X (x μ) 2 P (X = x): ffl If X is
More informationTHE UNIVERSITY OF THE WEST INDIES (DEPARTMENT OF MANAGEMENT STUDIES)
THE UNIVERSITY OF THE WEST INDIES (DEPARTMENT OF MANAGEMENT STUDIES) Mid-Semester Exam: Summer2005 June 20:2005; 7:00 9:00 pm MS 23C: Introduction to Quantitative Methods Instructions 1. This exam has
More informationMAS187/AEF258. University of Newcastle upon Tyne
MAS187/AEF258 University of Newcastle upon Tyne 2005-6 Contents 1 Collecting and Presenting Data 5 1.1 Introduction...................................... 5 1.1.1 Examples...................................
More informationEXERCISES FOR PRACTICE SESSION 2 OF STAT CAMP
EXERCISES FOR PRACTICE SESSION 2 OF STAT CAMP Note 1: The exercises below that are referenced by chapter number are taken or modified from the following open-source online textbook that was adapted by
More informationChapter 5 Student Lecture Notes 5-1. Department of Quantitative Methods & Information Systems. Business Statistics
Chapter 5 Student Lecture Notes 5-1 Department of Quantitative Methods & Information Systems Business Statistics Chapter 5 Discrete Probability Distributions QMIS 120 Dr. Mohammad Zainal Chapter Goals
More informationFinal review: Practice problems
Final review: Practice problems 1. A manufacturer of airplane parts knows from past experience that the probability is 0.8 that an order will be ready for shipment on time, and it is 0.72 that an order
More informationChapter 4 Discrete Random variables
Chapter 4 Discrete Random variables A is a variable that assumes numerical values associated with the random outcomes of an experiment, where only one numerical value is assigned to each sample point.
More informationIntroduction to Business Statistics QM 120 Chapter 6
DEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS Introduction to Business Statistics QM 120 Chapter 6 Spring 2008 Chapter 6: Continuous Probability Distribution 2 When a RV x is discrete, we can
More informationRandom Variables Handout. Xavier Vilà
Random Variables Handout Xavier Vilà Course 2004-2005 1 Discrete Random Variables. 1.1 Introduction 1.1.1 Definition of Random Variable A random variable X is a function that maps each possible outcome
More informationSTT 315 Practice Problems Chapter 3.7 and 4
STT 315 Practice Problems Chapter 3.7 and 4 Answer the question True or False. 1) The number of children in a family can be modelled using a continuous random variable. 2) For any continuous probability
More informationMAS187/AEF258. University of Newcastle upon Tyne
MAS187/AEF258 University of Newcastle upon Tyne 2005-6 Contents 1 Collecting and Presenting Data 5 1.1 Introduction...................................... 5 1.1.1 Examples...................................
More informationMath 14 Lecture Notes Ch The Normal Approximation to the Binomial Distribution. P (X ) = nc X p X q n X =
6.4 The Normal Approximation to the Binomial Distribution Recall from section 6.4 that g A binomial experiment is a experiment that satisfies the following four requirements: 1. Each trial can have only
More informationMATH1215: Mathematical Thinking Sec. 08 Spring Worksheet 9: Solution. x P(x)
N. Name: MATH: Mathematical Thinking Sec. 08 Spring 0 Worksheet 9: Solution Problem Compute the expected value of this probability distribution: x 3 8 0 3 P(x) 0. 0.0 0.3 0. Clearly, a value is missing
More informationDiscrete Random Variables and Probability Distributions
Chapter 4 Discrete Random Variables and Probability Distributions 4.1 Random Variables A quantity resulting from an experiment that, by chance, can assume different values. A random variable is a variable
More informationConfidence Intervals: Review
University of Utah February 28, 2018 1 2 Law of Large Numbers Draw your samples from any population with finite mean µ. Then LLN says Law of Large Numbers Draw your samples from any population with finite
More informationSection Distributions of Random Variables
Section 8.1 - Distributions of Random Variables Definition: A random variable is a rule that assigns a number to each outcome of an experiment. Example 1: Suppose we toss a coin three times. Then we could
More informationIntroduction to Statistics I
Introduction to Statistics I Keio University, Faculty of Economics Continuous random variables Simon Clinet (Keio University) Intro to Stats November 1, 2018 1 / 18 Definition (Continuous random variable)
More informationIE 5441: Financial Decision Making
IE 5441 1 IE 5441: Financial Decision Making Professor Department of Industrial and Systems Engineering College of Science and Engineering University of Minnesota IE 5441 2 Lecture Hours: Tuesday, Thursday
More informationSTOR 155 Introductory Statistics (Chap 5) Lecture 14: Sampling Distributions for Counts and Proportions
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL STOR 155 Introductory Statistics (Chap 5) Lecture 14: Sampling Distributions for Counts and Proportions 5/31/11 Lecture 14 1 Statistic & Its Sampling Distribution
More information3-1 Graphing Relationships
Warm Up State whether each word or phrase represents an amount that is increasing, decreasing, or constant. 1. stays the same constant 2. rises 3. drops increasing decreasing 4. slows down decreasing Match
More informationThe Binomial Probability Distribution
The Binomial Probability Distribution MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2017 Objectives After this lesson we will be able to: determine whether a probability
More information2011 Pearson Education, Inc
Statistics for Business and Economics Chapter 4 Random Variables & Probability Distributions Content 1. Two Types of Random Variables 2. Probability Distributions for Discrete Random Variables 3. The Binomial
More informationChapter 4 Discrete Random variables
Chapter 4 Discrete Random variables A is a variable that assumes numerical values associated with the random outcomes of an experiment, where only one numerical value is assigned to each sample point.
More informationChapter 8 Homework Solutions Compiled by Joe Kahlig. speed(x) freq 25 x < x < x < x < x < x < 55 5
H homework problems, C-copyright Joe Kahlig Chapter Solutions, Page Chapter Homework Solutions Compiled by Joe Kahlig. (a) finite discrete (b) infinite discrete (c) continuous (d) finite discrete (e) continuous.
More informationBIOS 4120: Introduction to Biostatistics Breheny. Lab #7. I. Binomial Distribution. RCode: dbinom(x, size, prob) binom.test(x, n, p = 0.
BIOS 4120: Introduction to Biostatistics Breheny Lab #7 I. Binomial Distribution P(X = k) = ( n k )pk (1 p) n k RCode: dbinom(x, size, prob) binom.test(x, n, p = 0.5) P(X < K) = P(X = 0) + P(X = 1) + +
More informationMath 14, Homework 7.1 p. 379 # 7, 9, 18, 20, 21, 23, 25, 26 Name
7.1 p. 379 # 7, 9, 18, 0, 1, 3, 5, 6 Name 7. Find each. (a) z α Step 1 Step Shade the desired percent under the mean statistics calculator to 99% confidence interval 3 1 0 1 3 µ 3σ µ σ µ σ µ µ+σ µ+σ µ+3σ
More informationHonors Statistics. Daily Agenda
Honors Statistics Aug 23-8:26 PM Daily Agenda 1. Review OTL C6#4 Chapter 6.2 rules for means and variances Aug 23-8:31 PM 1 Nov 21-8:16 PM Working out Choose a person aged 19 to 25 years at random and
More informationRandom Variable: Definition
Random Variables Random Variable: Definition A Random Variable is a numerical description of the outcome of an experiment Experiment Roll a die 10 times Inspect a shipment of 100 parts Open a gas station
More informationHomework: Due Wed, Nov 3 rd Chapter 8, # 48a, 55c and 56 (count as 1), 67a
Homework: Due Wed, Nov 3 rd Chapter 8, # 48a, 55c and 56 (count as 1), 67a Announcements: There are some office hour changes for Nov 5, 8, 9 on website Week 5 quiz begins after class today and ends at
More informationKeller: Stats for Mgmt & Econ, 7th Ed July 17, 2006
Chapter 7 Random Variables and Discrete Probability Distributions 7.1 Random Variables A random variable is a function or rule that assigns a number to each outcome of an experiment. Alternatively, the
More information2017 Fall QMS102 Tip Sheet 2
Chapter 5: Basic Probability 2017 Fall QMS102 Tip Sheet 2 (Covering Chapters 5 to 8) EVENTS -- Each possible outcome of a variable is an event, including 3 types. 1. Simple event = Described by a single
More informationHHH HHT HTH THH HTT THT TTH TTT
AP Statistics Name Unit 04 Probability Period Day 05 Notes Discrete & Continuous Random Variables Random Variable: Probability Distribution: Example: A probability model describes the possible outcomes
More informationChpt The Binomial Distribution
Chpt 5 5-4 The Binomial Distribution 1 /36 Chpt 5-4 Chpt 5 Homework p262 Applying the Concepts Exercises p263 1-11, 14-18, 23, 24, 26 2 /36 Objective Chpt 5 Find the exact probability for x successes in
More informationStatistics for Managers Using Microsoft Excel 7 th Edition
Statistics for Managers Using Microsoft Excel 7 th Edition Chapter 7 Sampling Distributions Statistics for Managers Using Microsoft Excel 7e Copyright 2014 Pearson Education, Inc. Chap 7-1 Learning Objectives
More informationA probability distribution shows the possible outcomes of an experiment and the probability of each of these outcomes.
Introduction In the previous chapter we discussed the basic concepts of probability and described how the rules of addition and multiplication were used to compute probabilities. In this chapter we expand
More informationECON 214 Elements of Statistics for Economists 2016/2017
ECON 214 Elements of Statistics for Economists 2016/2017 Topic The Normal Distribution Lecturer: Dr. Bernardin Senadza, Dept. of Economics bsenadza@ug.edu.gh College of Education School of Continuing and
More informationMANAGEMENT PRINCIPLES AND STATISTICS (252 BE)
MANAGEMENT PRINCIPLES AND STATISTICS (252 BE) Normal and Binomial Distribution Applied to Construction Management Sampling and Confidence Intervals Sr Tan Liat Choon Email: tanliatchoon@gmail.com Mobile:
More information6 If and then. (a) 0.6 (b) 0.9 (c) 2 (d) Which of these numbers can be a value of probability distribution of a discrete random variable
1. A number between 0 and 1 that is use to measure uncertainty is called: (a) Random variable (b) Trial (c) Simple event (d) Probability 2. Probability can be expressed as: (a) Rational (b) Fraction (c)
More informationBusiness Statistics. Chapter 5 Discrete Probability Distributions QMIS 120. Dr. Mohammad Zainal
Department of Quantitative Methods & Information Systems Business Statistics Chapter 5 Discrete Probability Distributions QMIS 120 Dr. Mohammad Zainal Chapter Goals After completing this chapter, you should
More informationChapter 6. The Normal Probability Distributions
Chapter 6 The Normal Probability Distributions 1 Chapter 6 Overview Introduction 6-1 Normal Probability Distributions 6-2 The Standard Normal Distribution 6-3 Applications of the Normal Distribution 6-5
More informationWrite your name: UNIVERSITY OF WASHINGTON Department of Economics
Write your name: UNIVERSITY OF WASHINGTON Department of Economics Economics 200, Fall 2008 Instructor: Scott First Hour Examination ***Use Brief Answers (making the key points) & Label All Graphs Completely
More information5. In fact, any function of a random variable is also a random variable
Random Variables - Class 11 October 14, 2012 Debdeep Pati 1 Random variables 1.1 Expectation of a function of a random variable 1. Expectation of a function of a random variable 2. We know E(X) = x xp(x)
More informationLecture 8. The Binomial Distribution. Binomial Distribution. Binomial Distribution. Probability Distributions: Normal and Binomial
Lecture 8 The Binomial Distribution Probability Distributions: Normal and Binomial 1 2 Binomial Distribution >A binomial experiment possesses the following properties. The experiment consists of a fixed
More informationTheoretical Foundations
Theoretical Foundations Probabilities Monia Ranalli monia.ranalli@uniroma2.it Ranalli M. Theoretical Foundations - Probabilities 1 / 27 Objectives understand the probability basics quantify random phenomena
More informationHonors Statistics. 3. Review OTL C6#3. 4. Normal Curve Quiz. Chapter 6 Section 2 Day s Notes.notebook. May 02, 2016.
Honors Statistics Aug 23-8:26 PM 3. Review OTL C6#3 4. Normal Curve Quiz Aug 23-8:31 PM 1 May 1-9:09 PM Apr 28-10:29 AM 2 27, 28, 29, 30 Nov 21-8:16 PM Working out Choose a person aged 19 to 25 years at
More informationDiscrete Probability Distributions and application in Business
http://wiki.stat.ucla.edu/socr/index.php/socr_courses_2008_thomson_econ261 Discrete Probability Distributions and application in Business By Grace Thomson DISCRETE PROBALITY DISTRIBUTIONS Discrete Probabilities
More informationCS Homework 4: Expectations & Empirical Distributions Due Date: October 9, 2018
CS1450 - Homework 4: Expectations & Empirical Distributions Due Date: October 9, 2018 Question 1 Consider a set of n people who are members of an online social network. Suppose that each pair of people
More informationVersion A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.
Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x
More informationcontinuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence
continuous rv Let X be a continuous rv. Then a probability distribution or probability density function (pdf) of X is a function f(x) such that for any two numbers a and b with a b, P(a X b) = b a f (x)dx.
More information5.1 Sampling Distributions for Counts and Proportions. Ulrich Hoensch MAT210 Rocky Mountain College Billings, MT 59102
5.1 Sampling Distributions for Counts and Proportions Ulrich Hoensch MAT210 Rocky Mountain College Billings, MT 59102 Sampling and Population Distributions Example: Count of People with Bachelor s Degrees
More information2. The sum of all the probabilities in the sample space must add up to 1
Continuous Random Variables and Continuous Probability Distributions Continuous Random Variable: A variable X that can take values on an interval; key feature remember is that the values of the variable
More informationPROB NON CALC ANS SL
IB Questionbank Maths SL PROB NON CALC ANS SL 0 min 0 marks. (a) P(X = ) = = A N 7 (b) P(X = ) = P(X = k) = k setting the sum of probabilities = M k e.g. + + =, + k = k k 9 = 9 accept = A k = AG N0 ( )
More informationBayes s Rule Example. defective. An MP3 player is selected at random and found to be defective. What is the probability it came from Factory I?
Bayes s Rule Example A company manufactures MP3 players at two factories. Factory I produces 60% of the MP3 players and Factory II produces 40%. Two percent of the MP3 players produced at Factory I are
More information300 total 50 left handed right handed = 250
Probability Rules 1. There are 300 students at a certain school. All students indicated they were either right handed or left handed but not both. Fifty of the students are left handed. How many students
More informationMidterm Exam III Review
Midterm Exam III Review Dr. Joseph Brennan Math 148, BU Dr. Joseph Brennan (Math 148, BU) Midterm Exam III Review 1 / 25 Permutations and Combinations ORDER In order to count the number of possible ways
More informationExam II Math 1342 Capters 3-5 HCCS. Name
Exam II Math 1342 Capters 3-5 HCCS Name Date Provide an appropriate response. 1) A single six-sided die is rolled. Find the probability of rolling a number less than 3. A) 0.5 B) 0.1 C) 0.25 D 0.333 1)
More informationAssignment 2 (Solution) Probability and Statistics
Assignment 2 (Solution) Probability and Statistics Dr. Jitesh J. Thakkar Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Instruction Total No. of Questions: 15.
More informationData Analysis and Statistical Methods Statistics 651
Data Analysis and Statistical Methods Statistics 651 http://wwwstattamuedu/~suhasini/teachinghtml Suhasini Subba Rao Review of previous lecture The main idea in the previous lecture is that the sample
More informationProbability Review. Pages Lecture 1: Probability Theory 1 Main Ideas 2 Trees 4 Additional Examples 5
Probability Review Pages Lecture 1: Probability Theory 1 Main Ideas 2 Trees 4 Additional Examples 5 Lecture 2: Random Variables (RVs) & Distributions 8 Definitions, Notation, & Ideas 8 Additional Examples
More informationsetting the sum of probabilities = 1 k = 3 AG N (a) (i) s = 1 A1 N1
. (a) P(X = ) 7 N (b) P(X = ) = k P(X = k) = setting the sum of probabilities = () () M e.g. k =, 5 + k = k k = 9 accept 9 k = AG N0 (c) correct substitution into E xp( X X x) e.g. 9 8 EX 7 N [7]. (a)
More informationBusiness Statistics 41000: Homework # 2
Business Statistics 41000: Homework # 2 Drew Creal Due date: At the beginning of lecture # 5 Remarks: These questions cover Lectures #3 and #4. Question # 1. Discrete Random Variables and Their Distributions
More informationChapter 6: Random Variables. Ch. 6-3: Binomial and Geometric Random Variables
Chapter : Random Variables Ch. -3: Binomial and Geometric Random Variables X 0 2 3 4 5 7 8 9 0 0 P(X) 3???????? 4 4 When the same chance process is repeated several times, we are often interested in whether
More informationL04: Homework Answer Key
L04: Homework Answer Key Instructions: You are encouraged to collaborate with other students on the homework, but it is important that you do your own work. Before working with someone else on the assignment,
More information6.5: THE NORMAL APPROXIMATION TO THE BINOMIAL AND
CD6-12 6.5: THE NORMAL APPROIMATION TO THE BINOMIAL AND POISSON DISTRIBUTIONS In the earlier sections of this chapter the normal probability distribution was discussed. In this section another useful aspect
More informationChapter 7: Random Variables and Discrete Probability Distributions
Chapter 7: Random Variables and Discrete Probability Distributions 7. Random Variables and Probability Distributions This section introduced the concept of a random variable, which assigns a numerical
More informationMath 251: Practice Questions Hints and Answers. Review II. Questions from Chapters 4 6
Math 251: Practice Questions Hints and Answers Review II. Questions from Chapters 4 6 II.A Probability II.A.1. The following is from a sample of 500 bikers who attended the annual rally in Sturgis South
More informationBinomal and Geometric Distributions
Binomal and Geometric Distributions Sections 3.2 & 3.3 Cathy Poliak, Ph.D. cathy@math.uh.edu Office in Fleming 11c Department of Mathematics University of Houston Lecture 7-2311 Cathy Poliak, Ph.D. cathy@math.uh.edu
More informationPart 10: The Binomial Distribution
Part 10: The Binomial Distribution The binomial distribution is an important example of a probability distribution for a discrete random variable. It has wide ranging applications. One readily available
More informationChapter 8 Homework Solutions Compiled by Joe Kahlig
homewk problems, B-copyright Joe Kahlig Chapter Solutions, Page Chapter omewk Solutions Compiled by Joe Kahlig 0. 0. 0. 0.. You are counting the number of games and there are a limited number of games
More informationContinuous Distributions
Quantitative Methods 2013 Continuous Distributions 1 The most important probability distribution in statistics is the normal distribution. Carl Friedrich Gauss (1777 1855) Normal curve A normal distribution
More information