ECEn 370 Introduction to Probability
|
|
- Austen Dale Wade
- 5 years ago
- Views:
Transcription
1 RED- You can write on this exam. ECEn 7 Introduction to Probability Section Midterm Winter, Instructor Professor Brian Mazzeo Closed Book - You can bring one 8.5 X sheet of handwritten notes on both sides. Graphing or Scientic Calculator Allowed -Hour Suggested Time Limit IMPORTANT! WRITE YOUR NAME on every page of the exam. Answer questions -7 on the provided bubble sheet. Questions -7 are worth point each. Do not discuss the exam with other students. NOTE: Use all of the digits on your calculator, or fractions, before at the end rounding to the number of signicant digits used in the problem.
2 . A batch of fty items is inspected by testing three randomly selected items. If one of the three is defective, the batch is rejected. What is the probability that the batch is accepted if it contains seven defectives? A). B).4 C).8 D).49 E).49 F).6 G).66 H).676 I).8. Ninety students, including Joe and Jane, are to be split into three classes of equal size, and this is to be done at random. What is the probability that Joe and Jane end up in the same class? A).6 B).7 C). D).6 E).8 F). G).9 H).966 I).98. If at rst you don't succeed, try, try, try again. A computer will successfully send a message across a network with probability.6. The computer will retry sending the message until it is successfully sent. Given that we know that the computer will successfully transmit the message on or before the fourth attempt, what is the probability that the computer successfully sends the message on the rst attempt? A).4 B).4 C).6 D).75 E).66 F).65 G).775 H).98 I).946
3 4. A random variable X has a probability density function given by ( fx (x) = (x ), 4 if x 4, otherwise., Find the cumulative distribution function, FX (x), and compute the value of the following expression: FX () + FX () + FX (9) A) B) /6 C) /4 D) 7/6 E) F) /6 G) 4 H) 54 I) 97/6 5. You have the following joint PMF of random variables X and Y : y 4 / / / 4 5 / Find E[Z] where Z = XY A).58 B).96 C). D).9 E) 6.8 F) 7.4 G).76 H) 4.8 I) 4.9 x
4 6. You have three ten-sided dice numbered from to. Each face has an equal probabilitiy of appearing during a roll (uniformly distributed). The outcomes, representing each die, are the random variables X, Y, and Z. Find P (min(x, Y, Z) = 8). A).5 B).9 C).7 D).7 E).64 F).9 G).5 H).6 I).4 7. You have the following joint PDF with a constant density, f X,Y (x, y) = c, in the shaded region and is zero outside that region: y Find E[X ] x A) / B) 4/ C) / D) 5/ E) /6 F) G) /6 H) 7/ I) 5/ 4
5 8. A binary signal S is transmitted, and we are given that P (S = ) =.7 and P (S = ) =.. The received signal is Y = N + S, where N is normal noise, with zero mean and unit variance, independent of S. What is the probability that S =, as a function of the observed value of -. for Y? A).4 B).6 C).44 D).656 E).677 F).7 G).74 H).8 I) We are told that the joint PDF of the random variables X and Y is a constant c on the set S shown in the gure below and is zero outside. We wish to determine the value of c and the marginal PDFs of X and Y. y 4 S x Compute the following: c + f Y (.5) + f Y (.5) + f Y (.5) + f X (.5) + f X (.5) + f X (.5) A) B) 9/4 C) 5/ D) /4 E) F) /4 G) 7/ H) 5/4 I) 4 5
6 . From the gure above in problem 9, compute the following where F X,Y is the CDF of the joint PDF. F X,Y (, ) + F X,Y (.5,.5) + F X,Y (.5,.5) + F X,Y (,.5) + F X,Y (.5, 4.5) + F X,Y (4.5, 6) A) /6 B) /6 C) /6 D) 4/6 E) 5/6 F) 6/6 G) 7/6 H) 8/6 I) 9/6. Each morning, Hungry Hungry Horace eats some sausages. On any given morning, the number of sausages he eats is equally likely to be,, 8, or 9, independent of what he has done in the past. Let X be the number of sausages that Harry eats in days. Compute q = E[X] + var(x). A) < q B) < q C) < q 5 D) 5 < q 7 E) 7 < q 9 F) 9 < q G) < q H) < q 5 I) 5 < q 7. A stock market trader buys shares of stock A and shares of stock B. Let X and Y be the price changes of A and B, respectively, over a certain time period, and assume that the joint PMF of X and Y is uniform over the set of integers x and y satisfying x, x y Find the mean of the trader's prots (or losses if negative). A) - B) -/ C) D) / E) F) / G) H) 5/ I) 6
7 . Let X be a random variable that takes values from to 9 with equal probability /. Find the PMF of the random variable Y = min ( X, X 4, X 5 ). Calculate p Y () + p Y () A) / B) / C) / D) 4/ E) 5/ F) 6/ G) 7/ H) 8/ I) 9/ 4. Al thows darts at a circular target of radius m and is equally likely to hit any point on the target. Let X be the distance of Al's hit from the center. Find E[X] + var(x). A).8 m B).8 m C).85 m D).87 m E).89 m F).8 m G).8 m H).85 m I).87 m 5. Let A, B, and C be three events in Ω. If P (A) =, P (B) = 4, P (C) =, P (A B) = 8, P (A C) = 6, and P (B C) =, nd P (A B C). A) 6/4 B) 7/4 C) 8/4 D) 9/4 E) /4 F) /4 G) /4 H) /4 I) 7
8 6. Suppose you are in a Family Home Evening Group with ten men and ten women. On any given Monday night, the probability of a boy showing up is 8/ and the probability of a girl showing up is 9/, independently of any one else. What is the probability, P, that less than two people show up? A) < P < B) < P < C) 4 < P < D) 5 < P < 4 E) 6 < P < 5 F) 7 < P < 6 G) 8 < P < 7 H) 9 < P < 8 I) < P < 9 7. Suppose I have a uniform random variable, X, with the following PMF: { /5, if x =, 4, 5, 6, 7 p X (x) =, otherwise. Suppose I have a random variable, Z = (X 4). What is E[Z]? A) - B) -/ C) D) / E) F) / G) H) 5/ I) 8. Messages transmitted by a computer in Provo through a data network are destined for Salt Lake City with probability.5, Las Vegas, with probability., and for Denver with probability.. The transit time X of a message is random. Its mean is.5 seconds if it is destined for Salt Lake City,. seconds if it is destined for Las Vegas, and. seconds if it is destined for Denver. Calculate E [X]. A).45 B).45 C).45 D).45 E).5 F).5 G).45 H) 4.5 I) 45 8
9 The following conditional PMF is used for problems 9 and. You have the following conditional PMF of random variable Y conditioned on random variable X: y 4 /4 / / /4 4 5 x and marginal PMF of X given by: /6, if x =,,, 4 p X (x) = /, if x = 5, otherwise. The above conditional PMF is used for problems 9 and. 9. For the conditions above, compute E[Z], where Z = XY. A) B) C) D) 4 E) 5 F) 6 G) 7 H) 8 I) 9. Find the probability P (min(x, Y ) = ). A) /4 B) /8 C) /6 D) / E) / F) 7/ G) / H) /4 I) 9
10 . A proportion, p, of BYU students have served missions. I question N BYU students and I sum up the number that have served missions. Since I can't talk to all of the students, to estimate the proportion I then take my sum and divide by N to form a sample average to estimate p. Mark on your answer sheet all of the statements that are true (you can have multiple bubbles lled in on this one, if necessary). A) The expected value of the sample average decreases as N increases. B) The expected value of the sample average is the same as N increases. C) The expected value of the sample average increases as N increases. D) The variance of the sample average decreases as N increases. E) The variance of the sample average stays the same as N increases. F) The variance of the sample average increases as N increases. G) If N is, then the expected value of the sample average is or, depending on p. H) If N is, then the expected value of the sample average is p. I) If I ask a dierent set of N BYU students, I will always get the same sample average. J) None of the above are true.. Let X be the roll of a fair six-sided die and let A be the event that the roll is an even number. Find p X A (4). A) / B) / C) / D) 4/ E) 5/ F) 6/ G) 7/ H) 8/ I) 9/. The PMF for X is given by p X (x) = Let Y = X. What is p Y ( ) + p Y () + p Y () + p Y ()? A) /9 B) /9 C) /9 D) 4/9 E) 5/9 F) 6/9 G) 7/9 H) 8/9 I) { /9, if x is an integer in the range [ 4, 4],, otherwise
11 4. A parking lot contains cars, of which happen to be lemons. We select 4 of these cars at random and take them for a test drive. Find the probability that of the cars tested turn out to be lemons. A) 5. 5 B).4 C).4 D) 4. E) 4.6 F) 4.86 G) 5. H) 8. I) 4. For problems 5 and 6, A source transmits a message (a string of symbols) through a noisy communication channel. Each sysmbol is or with probability.4 and.6, respectively, and is received incorrectly with probability. and.7, respectively. Errors in dierent symbol transmissions are independent. 5. What is the probability that the string of symbols is received correctly? A).8 B).89 C).56 D).84 E).44 F).864 G).9 H).4 I) In an eort to improve reliability, each symbol is transmitted three times and the received string is decoded by majority rule. In orther words, a (or ) is trasmitted as (or, respectively), and it is decoded at the receiver as a (or ) if and only if the received three-symbol string contains at least two s (or s, respectively). What is the probability that a is correctly decoded? A).44 B).44 C).445 D).447 E).449 F).45 G).45 H).455 I).457
12 7. This is a dicult problem for the last one. The joint pdf of random variables X and Y is given by { y f X,Y (x, y) = e x/y e y x >, y >, otherwise Find P (X > Y = ). A).45 B).47 C).49 D).5 E).5 F).55 G).57 H).59 I).6
ECEn 370 Introduction to Probability
ECEn 7 Midterm RED- You can write on this exam. ECEn 7 Introduction to Probability Section Midterm Winter, Instructor Professor Brian Mazzeo Closed Book - You can bring one 8.5 X sheet of handwritten notes
More informationCentral Limit Theorem, Joint Distributions Spring 2018
Central Limit Theorem, Joint Distributions 18.5 Spring 218.5.4.3.2.1-4 -3-2 -1 1 2 3 4 Exam next Wednesday Exam 1 on Wednesday March 7, regular room and time. Designed for 1 hour. You will have the full
More informationStat 231 Exam 1 Fall 2011
Stat 231 Exam 1 Fall 2011 I have neither given nor received unauthorized assistance on this exam. Name Signed Date Name Printed ATTENTION! Incorrect numerical answers unaccompanied by supporting reasoning
More informationChapter 4 and 5 Note Guide: Probability Distributions
Chapter 4 and 5 Note Guide: Probability Distributions Probability Distributions for a Discrete Random Variable A discrete probability distribution function has two characteristics: Each probability is
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 information(Practice Version) Midterm Exam 1
EECS 126 Probability and Random Processes University of California, Berkeley: Fall 2014 Kannan Ramchandran September 19, 2014 (Practice Version) Midterm Exam 1 Last name First name SID Rules. DO NOT open
More informationDiscrete Random Variables
Discrete Random Variables ST 370 A random variable is a numerical value associated with the outcome of an experiment. Discrete random variable When we can enumerate the possible values of the variable
More informationCentral Limit Theorem 11/08/2005
Central Limit Theorem 11/08/2005 A More General Central Limit Theorem Theorem. Let X 1, X 2,..., X n,... be a sequence of independent discrete random variables, and let S n = X 1 + X 2 + + X n. For each
More informationThis exam contains 8 pages (including this cover page) and 5 problems. Check to see if any pages are missing.
Stat 475 Winter 207 Midterm Exam 27 February 207 Name: This exam contains 8 pages (including this cover page) and 5 problems Check to see if any pages are missing You may only use an SOA-approved calculator
More informationProblem A Grade x P(x) To get "C" 1 or 2 must be 1 0.05469 B A 2 0.16410 3 0.27340 4 0.27340 5 0.16410 6 0.05470 7 0.00780 0.2188 0.5468 0.2266 Problem B Grade x P(x) To get "C" 1 or 2 must 1 0.31150 be
More informationLecture Stat 302 Introduction to Probability - Slides 15
Lecture Stat 30 Introduction to Probability - Slides 15 AD March 010 AD () March 010 1 / 18 Continuous Random Variable Let X a (real-valued) continuous r.v.. It is characterized by its pdf f : R! [0, )
More informationContinuous Random Variables: The Uniform Distribution *
OpenStax-CNX module: m16819 1 Continuous Random Variables: The Uniform Distribution * Susan Dean Barbara Illowsky, Ph.D. This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution
More informationDiscrete Random Variables
Discrete Random Variables In this chapter, we introduce a new concept that of a random variable or RV. A random variable is a model to help us describe the state of the world around us. Roughly, a RV can
More informationVIDEO 1. A random variable is a quantity whose value depends on chance, for example, the outcome when a die is rolled.
Part 1: Probability Distributions VIDEO 1 Name: 11-10 Probability and Binomial Distributions A random variable is a quantity whose value depends on chance, for example, the outcome when a die is rolled.
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 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 informationFall 2015 Math 141:505 Exam 3 Form A
Fall 205 Math 4:505 Exam 3 Form A Last Name: First Name: Exam Seat #: UIN: On my honor, as an Aggie, I have neither given nor received unauthorized aid on this academic work Signature: INSTRUCTIONS Part
More informationThis exam contains 8 pages (including this cover page) and 5 problems. Check to see if any pages are missing.
Stat 475 Winter 2017 Midterm Exam 27 February 2017 Name: This exam contains 8 pages (including this cover page) and 5 problems Check to see if any pages are missing You may only use an SOA-approved calculator
More informationUniversity of California, Los Angeles Department of Statistics. Normal distribution
University of California, Los Angeles Department of Statistics Statistics 110A Instructor: Nicolas Christou Normal distribution The normal distribution is the most important distribution. It describes
More informationLearning Goals: * Determining the expected value from a probability distribution. * Applying the expected value formula to solve problems.
Learning Goals: * Determining the expected value from a probability distribution. * Applying the expected value formula to solve problems. The following are marks from assignments and tests in a math class.
More informationEcon 250 Fall Due at November 16. Assignment 2: Binomial Distribution, Continuous Random Variables and Sampling
Econ 250 Fall 2010 Due at November 16 Assignment 2: Binomial Distribution, Continuous Random Variables and Sampling 1. Suppose a firm wishes to raise funds and there are a large number of independent financial
More informationMATH/STAT 3360, Probability FALL 2012 Toby Kenney
MATH/STAT 3360, Probability FALL 2012 Toby Kenney In Class Examples () August 31, 2012 1 / 81 A statistics textbook has 8 chapters. Each chapter has 50 questions. How many questions are there in total
More informationNormal distribution. We say that a random variable X follows the normal distribution if the probability density function of X is given by
Normal distribution The normal distribution is the most important distribution. It describes well the distribution of random variables that arise in practice, such as the heights or weights of people,
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 informationProbability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions
April 9th, 2018 Lecture 20: Special distributions Week 1 Chapter 1: Axioms of probability Week 2 Chapter 3: Conditional probability and independence Week 4 Chapters 4, 6: Random variables Week 9 Chapter
More informationLECTURE CHAPTER 3 DESCRETE RANDOM VARIABLE
LECTURE CHAPTER 3 DESCRETE RANDOM VARIABLE MSc Đào Việt Hùng Email: hungdv@tlu.edu.vn Random Variable A random variable is a function that assigns a real number to each outcome in the sample space of a
More information. (i) What is the probability that X is at most 8.75? =.875
Worksheet 1 Prep-Work (Distributions) 1)Let X be the random variable whose c.d.f. is given below. F X 0 0.3 ( x) 0.5 0.8 1.0 if if if if if x 5 5 x 10 10 x 15 15 x 0 0 x Compute the mean, X. (Hint: First
More informationChapter 2: Random Variables (Cont d)
Chapter : Random Variables (Cont d) Section.4: The Variance of a Random Variable Problem (1): Suppose that the random variable X takes the values, 1, 4, and 6 with probability values 1/, 1/6, 1/, and 1/6,
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 informationReview for Final Exam Spring 2014 Jeremy Orloff and Jonathan Bloom
Review for Final Exam 18.05 Spring 2014 Jeremy Orloff and Jonathan Bloom THANK YOU!!!! JON!! PETER!! RUTHI!! ERIKA!! ALL OF YOU!!!! Probability Counting Sets Inclusion-exclusion principle Rule of product
More informationChapter 3 Discrete Random Variables and Probability Distributions
Chapter 3 Discrete Random Variables and Probability Distributions Part 4: Special Discrete Random Variable Distributions Sections 3.7 & 3.8 Geometric, Negative Binomial, Hypergeometric NOTE: The discrete
More informationName Period AP Statistics Unit 5 Review
Name Period AP Statistics Unit 5 Review Multiple Choice 1. Jay Olshansky from the University of Chicago was quoted in Chance News as arguing that for the average life expectancy to reach 100, 18% of people
More information4 Random Variables and Distributions
4 Random Variables and Distributions Random variables A random variable assigns each outcome in a sample space. e.g. called a realization of that variable to Note: We ll usually denote a random variable
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 informationAnswer Key: Quiz2-Chapter5: Discrete Probability Distribution
Economics 70: Applied Business Statistics For Economics & Business (Summer 01) Answer Key: Quiz-Chapter5: Discrete Probability Distribution The number of electrical outages in a city varies from day to
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 informationHomework: Due Wed, Feb 20 th. Chapter 8, # 60a + 62a (count together as 1), 74, 82
Announcements: Week 5 quiz begins at 4pm today and ends at 3pm on Wed If you take more than 20 minutes to complete your quiz, you will only receive partial credit. (It doesn t cut you off.) Today: Sections
More informationChapter 7 Study Guide: The Central Limit Theorem
Chapter 7 Study Guide: The Central Limit Theorem Introduction Why are we so concerned with means? Two reasons are that they give us a middle ground for comparison and they are easy to calculate. In this
More informationExample - Let X be the number of boys in a 4 child family. Find the probability distribution table:
Chapter7 Probability Distributions and Statistics Distributions of Random Variables tthe value of the result of the probability experiment is a RANDOM VARIABLE. Example - Let X be the number of boys in
More information6. Continous Distributions
6. Continous Distributions Chris Piech and Mehran Sahami May 17 So far, all random variables we have seen have been discrete. In all the cases we have seen in CS19 this meant that our RVs could only take
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 informationM249 Diagnostic Quiz
THE OPEN UNIVERSITY Faculty of Mathematics and Computing M249 Diagnostic Quiz Prepared by the Course Team [Press to begin] c 2005, 2006 The Open University Last Revision Date: May 19, 2006 Version 4.2
More informationStatistics Chapter 8
Statistics Chapter 8 Binomial & Geometric Distributions Time: 1.5 + weeks Activity: A Gaggle of Girls The Ferrells have 3 children: Jennifer, Jessica, and Jaclyn. If we assume that a couple is equally
More information6. THE BINOMIAL DISTRIBUTION
6. THE BINOMIAL DISTRIBUTION Eg: For 1000 borrowers in the lowest risk category (FICO score between 800 and 850), what is the probability that at least 250 of them will default on their loan (thereby rendering
More informationChapter 4 Continuous Random Variables and Probability Distributions
Chapter 4 Continuous Random Variables and Probability Distributions Part 2: More on Continuous Random Variables Section 4.5 Continuous Uniform Distribution Section 4.6 Normal Distribution 1 / 27 Continuous
More informationMath489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5
Math489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5 Steve Dunbar Due Fri, October 9, 7. Calculate the m.g.f. of the random variable with uniform distribution on [, ] and then
More informationProbability Theory. Mohamed I. Riffi. Islamic University of Gaza
Probability Theory Mohamed I. Riffi Islamic University of Gaza Table of contents 1. Chapter 2 Discrete Distributions The binomial distribution 1 Chapter 2 Discrete Distributions Bernoulli trials and the
More informationSession Window. Variable Name Row. Worksheet Window. Double click on MINITAB icon. You will see a split screen: Getting Started with MINITAB
STARTING MINITAB: Double click on MINITAB icon. You will see a split screen: Session Window Worksheet Window Variable Name Row ACTIVE WINDOW = BLUE INACTIVE WINDOW = GRAY f(x) F(x) Getting Started with
More informationProbability and Statistics
Probability and Statistics Alvin Lin Probability and Statistics: January 2017 - May 2017 Binomial Random Variables There are two balls marked S and F in a basket. Select a ball 3 times with replacement.
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 informationSTOR Lecture 7. Random Variables - I
STOR 435.001 Lecture 7 Random Variables - I Shankar Bhamidi UNC Chapel Hill 1 / 31 Example 1a: Suppose that our experiment consists of tossing 3 fair coins. Let Y denote the number of heads that appear.
More informationBinomial Random Variables. Binomial Random Variables
Bernoulli Trials Definition A Bernoulli trial is a random experiment in which there are only two possible outcomes - success and failure. 1 Tossing a coin and considering heads as success and tails as
More information3. The n observations are independent. Knowing the result of one observation tells you nothing about the other observations.
Binomial and Geometric Distributions - Terms and Formulas Binomial Experiments - experiments having all four conditions: 1. Each observation falls into one of two categories we call them success or failure.
More informationImportant Terms. Summary. multinomial distribution 234 Poisson distribution 235. expected value 220 hypergeometric distribution 238
6 6 Summary Many variables have special probability distributions. This chapter presented several of the most common probability distributions, including the binomial distribution, the multinomial distribution,
More informationMA : Introductory Probability
MA 320-001: Introductory Probability David Murrugarra Department of Mathematics, University of Kentucky http://www.math.uky.edu/~dmu228/ma320/ Spring 2017 David Murrugarra (University of Kentucky) MA 320:
More informationSec 5.2. Mean Variance Expectation. Bluman, Chapter 5 1
Sec 5.2 Mean Variance Expectation Bluman, Chapter 5 1 Review: Do you remember the following? The symbols for Variance Standard deviation Mean The relationship between variance and standard deviation? Bluman,
More informationCS145: Probability & Computing
CS145: Probability & Computing Lecture 8: Variance of Sums, Cumulative Distribution, Continuous Variables Instructor: Eli Upfal Brown University Computer Science Figure credits: Bertsekas & Tsitsiklis,
More information4.1 Probability Distributions
Probability and Statistics Mrs. Leahy Chapter 4: Discrete Probability Distribution ALWAYS KEEP IN MIND: The Probability of an event is ALWAYS between: and!!!! 4.1 Probability Distributions Random Variables
More informationBusiness Statistics Fall Quarter 2015 Midterm Answer Key
Business Statistics 4000 Fall Quarter 205 Midterm Answer Key Name (print please): Open book (but be courteous to your neighbors). No internet. A simple calculator may be helpful for computing numerical
More informationSTUDY SET 2. Continuous Probability Distributions. ANSWER: Without continuity correction P(X>10) = P(Z>-0.66) =
STUDY SET 2 Continuous Probability Distributions 1. The normal distribution is used to approximate the binomial under certain conditions. What is the best way to approximate the binomial using the normal?
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 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 informationMath 227 (Statistics) Chapter 6 Practice Test MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Math 227 (Statistics) Chapter 6 Practice Test MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Using the following uniform density curve, answer the
More informationSampling & populations
Sampling & populations Sample proportions Sampling distribution - small populations Sampling distribution - large populations Sampling distribution - normal distribution approximation Mean & variance of
More informationThe Uniform Distribution
The Uniform Distribution EXAMPLE 1 The previous problem is an example of the uniform probability distribution. Illustrate the uniform distribution. The data that follows are 55 smiling times, in seconds,
More informationA.REPRESENTATION OF DATA
A.REPRESENTATION OF DATA (a) GRAPHS : PART I Q: Why do we need a graph paper? Ans: You need graph paper to draw: (i) Histogram (ii) Cumulative Frequency Curve (iii) Frequency Polygon (iv) Box-and-Whisker
More informationMath 160 Professor Busken Chapter 5 Worksheets
Math 160 Professor Busken Chapter 5 Worksheets Name: 1. Find the expected value. Suppose you play a Pick 4 Lotto where you pay 50 to select a sequence of four digits, such as 2118. If you select the same
More informationChapter 4: Commonly Used Distributions. Statistics for Engineers and Scientists Fourth Edition William Navidi
Chapter 4: Commonly Used Distributions Statistics for Engineers and Scientists Fourth Edition William Navidi 2014 by Education. This is proprietary material solely for authorized instructor use. Not authorized
More information3. The n observations are independent. Knowing the result of one observation tells you nothing about the other observations.
Binomial and Geometric Distributions - Terms and Formulas Binomial Experiments - experiments having all four conditions: 1. Each observation falls into one of two categories we call them success or failure.
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 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 informationChapter 8: The Binomial and Geometric Distributions
Chapter 8: The Binomial and Geometric Distributions 8.1 Binomial Distributions 8.2 Geometric Distributions 1 Let me begin with an example My best friends from Kent School had three daughters. What is the
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 informationCH 6 Review Normal Probability Distributions College Statistics
CH 6 Review Normal Probability Distributions College Statistics Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Using the following uniform density
More informationTABLE OF CONTENTS - VOLUME 2
TABLE OF CONTENTS - VOLUME 2 CREDIBILITY SECTION 1 - LIMITED FLUCTUATION CREDIBILITY PROBLEM SET 1 SECTION 2 - BAYESIAN ESTIMATION, DISCRETE PRIOR PROBLEM SET 2 SECTION 3 - BAYESIAN CREDIBILITY, DISCRETE
More informationChapter 5. Discrete Probability Distributions. McGraw-Hill, Bluman, 7 th ed, Chapter 5 1
Chapter 5 Discrete Probability Distributions McGraw-Hill, Bluman, 7 th ed, Chapter 5 1 Chapter 5 Overview Introduction 5-1 Probability Distributions 5-2 Mean, Variance, Standard Deviation, and Expectation
More informationProbability Models. Grab a copy of the notes on the table by the door
Grab a copy of the notes on the table by the door Bernoulli Trials Suppose a cereal manufacturer puts pictures of famous athletes in boxes of cereal, in the hope of increasing sales. The manufacturer announces
More informationSTAT 3090 Test 2 - Version B Fall Student s Printed Name: PLEASE READ DIRECTIONS!!!!
Student s Printed Name: Instructor: XID: Section #: Read each question very carefully. You are permitted to use a calculator on all portions of this exam. You are NOT allowed to use any textbook, notes,
More informationIntroduction to Probability and Inference HSSP Summer 2017, Instructor: Alexandra Ding July 19, 2017
Introduction to Probability and Inference HSSP Summer 2017, Instructor: Alexandra Ding July 19, 2017 Please fill out the attendance sheet! Suggestions Box: Feedback and suggestions are important to the
More informationFrequency and Severity with Coverage Modifications
Frequency and Severity with Coverage Modifications Chapter 8 Stat 477 - Loss Models Chapter 8 (Stat 477) Coverage Modifications Brian Hartman - BYU 1 / 23 Introduction Introduction In the previous weeks,
More information5.2 Random Variables, Probability Histograms and Probability Distributions
Chapter 5 5.2 Random Variables, Probability Histograms and Probability Distributions A random variable (r.v.) can be either continuous or discrete. It takes on the possible values of an experiment. It
More informationMath 227 Practice Test 2 Sec Name
Math 227 Practice Test 2 Sec 4.4-6.2 Name Find the indicated probability. ) A bin contains 64 light bulbs of which 0 are defective. If 5 light bulbs are randomly selected from the bin with replacement,
More informationMean, Variance, and Expectation. Mean
3 Mean, Variance, and Expectation The mean, variance, and standard deviation for a probability distribution are computed differently from the mean, variance, and standard deviation for samples. This section
More information6.042/18.062J Mathematics for Computer Science November 30, 2006 Tom Leighton and Ronitt Rubinfeld. Expected Value I
6.42/8.62J Mathematics for Computer Science ovember 3, 26 Tom Leighton and Ronitt Rubinfeld Lecture otes Expected Value I The expectation or expected value of a random variable is a single number that
More informationMath 1070 Final Exam Practice Spring 2014
University of Connecticut Department of Mathematics Math 1070 Practice Spring 2014 Name: Instructor Name: Section: Read This First! This is a closed notes, closed book exam. You can not receive aid on
More informationThe Normal Distribution
Will Monroe CS 09 The Normal Distribution Lecture Notes # July 9, 207 Based on a chapter by Chris Piech The single most important random variable type is the normal a.k.a. Gaussian) random variable, parametrized
More informationNormal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial.
Lecture 21,22, 23 Text: A Course in Probability by Weiss 8.5 STAT 225 Introduction to Probability Models March 31, 2014 Standard Sums of Whitney Huang Purdue University 21,22, 23.1 Agenda 1 2 Standard
More informationSTT315 Chapter 4 Random Variables & Probability Distributions AM KM
Before starting new chapter: brief Review from Algebra Combinations In how many ways can we select x objects out of n objects? In how many ways you can select 5 numbers out of 45 numbers ballot to win
More informationTest 7A AP Statistics Name: Directions: Work on these sheets.
Test 7A AP Statistics Name: Directions: Work on these sheets. Part 1: Multiple Choice. Circle the letter corresponding to the best answer. 1. Suppose X is a random variable with mean µ. Suppose we observe
More informationDO NOT OPEN THIS QUESTION BOOKLET UNTIL YOU ARE TOLD TO DO SO
QUESTION BOOKLET EE 126 Spring 2006 Final Exam Wednesday, May 17, 8am 11am DO NOT OPEN THIS QUESTION BOOKLET UNTIL YOU ARE TOLD TO DO SO You have 180 minutes to complete the final. The final consists of
More informationFormula for the Multinomial Distribution
6 5 Other Types of Distributions (Optional) In addition to the binomial distribution, other types of distributions are used in statistics. Three of the most commonly used distributions are the multinomial
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Exam Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find a z-score satisfying the given condition. 1) 20.1% of the total area is to the right
More information4-2 Probability Distributions and Probability Density Functions. Figure 4-2 Probability determined from the area under f(x).
4-2 Probability Distributions and Probability Density Functions Figure 4-2 Probability determined from the area under f(x). 4-2 Probability Distributions and Probability Density Functions Definition 4-2
More informationExam 2 - Pretest DS-23
Exam 2 - Pretest DS-23 Chapter (4,5,6) Odds 10/3/2017 Ferbrache MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the problem. 1) A single die
More informationMathacle. PSet Stats, Concepts In Statistics Level Number Name: Date: Distribution Distribute in anyway but normal
Distribution Distribute in anyway but normal VI. DISTRIBUTION A probability distribution is a mathematical function that provides the probabilities of occurrence of all distinct outcomes in the sample
More informationWhat is the probability of success? Failure? How could we do this simulation using a random number table?
Probability Ch.4, sections 4.2 & 4.3 Binomial and Geometric Distributions Name: Date: Pd: 4.2. What is a binomial distribution? How do we find the probability of success? Suppose you have three daughters.
More informationMarch 21, Fractal Friday: Sign up & pay now!
Welcome to the Fourth Quarter. We did Discrete Random Variables at the start of HL1 (1516) Q4 We did Continuous RVs at the end of HL2 (1617) Q3 and start of Q4 7½ weeks plus finals Three Units: Discrete
More information4. Basic distributions with R
4. Basic distributions with R CA200 (based on the book by Prof. Jane M. Horgan) 1 Discrete distributions: Binomial distribution Def: Conditions: 1. An experiment consists of n repeated trials 2. Each trial
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 informationName: CS3130: Probability and Statistics for Engineers Practice Final Exam Instructions: You may use any notes that you like, but no calculators or computers are allowed. Be sure to show all of your work.
More information