2.) What is the set of outcomes that describes the event that at least one of the items selected is defective? {AD, DA, DD}

Size: px
Start display at page:

Download "2.) What is the set of outcomes that describes the event that at least one of the items selected is defective? {AD, DA, DD}"

Transcription

1 Math 361 Practice Exam 2 (Use this information for questions 1 3) At the end of a production run manufacturing rubber gaskets, items are sampled at random and inspected to determine if the item is Acceptable (A), or Defective (D). Suppose it is planned to select two items and determine if each is either A or D. It is estimated that about 12% of the items produced are defective. 1.) What is the Sample Space, S, of the outcomes? S = {AA, AD, DA, DD} 2.) What is the set of outcomes that describes the event that at least one of the items selected is defective? {AD, DA, DD} 3.) What is the probability that at least one item selected is defective? P(X 1) = 0.88* * *0.12 = ) Suppose that S is a sample space. P(S) = 1 (The probability of getting one of the possible outcomes is equal to 1 since something has to come out of the experiment.) Use the following to answer questions 5-11: The probability distribution of random variable, X, is defined as follows: X Probability ) State the conditions for this to be a valid probability model: a.) Probabilities for each outcome must be between 0 and 1. b.) The probabilities of all possible outcomes must add to one. 6.) Find the expected value of the random variable: E(x) = -5* *.2 + 6* *.1 + 2*0.2 = 0

2 7.) Given that the variance of this distribution is 90, determine the mean and standard deviation of the random variable Y where Y = 3 + 2x E(Y) = E(3 + 2x) = E(3) + 2*E(x) = 3 + 2* 0 = 3 Var(Y) = E[ (Y E(Y)) 2 ) = E[ (Y 3) 2 ] = E[ Y 2 6Y + 9] = E(Y^2) 6*E(Y) + E(9) = E[ (3 + 2x) 2 ) ] 6*3 + 9 = E[ X + 4x 2 ] -9 = E(9) + 12*E(X) + 4*E(X 2 ) 9 = *0 + 4*E(X 2 ) 9 = 4*E(X 2 ) Var(X) = E[ (X E(x)) 2 ] = E(x 2 ) = 90 So: Var(Y) = 4*90 = 360 Standard deviation of Y = ) Suppose you draw two numbers at random from this distribution. What is the mean and standard deviation of the sum of these two numbers? µ = E(X + X) = E(X) + E(X) = = 0 σ 2 = 90, So σ 2 x+x = = 180 σ = 9.) Find P(X = 6)? ) Find P(X < 5)? = ) What is the median of X? 0 (Remember, median is the lowest category including 0.5 probability. Write them in order to see this.) 12. Suppose X is a random variable with the given distribution. What is P(1 X 2)? = Area under the curve = ½ * Base * Height = ½*1*1/2= ¼

3 13.) Suppose there are three balls in a box. On one of the balls is the number 1, on another is the number 2, and on the third is the number 3. You select two balls at random and without replacement from the box and note the two numbers observed. The sample space S consists of the three equally likely outcomes {(1, 2), (1, 3), (2, 3)} (disregarding order). Let X be the sum of the two balls selected. a.) Write the probability distribution for X X Probability of X 1/3 1/3 1/3 b.) What is the probability that the sum is at least 4. 2/3 c.) What is the mean of x? 3*1/3 + 4*1/3 + 5*1/3 = 12/3 = 4 Use the following information for questions 14 and 15 The weight of medium-size tomatoes selected at random from a bin at the local supermarket is a random variable with mean µ = 12 oz and standard deviation σ = 2 oz 14.) Suppose we pick 4 tomatoes from the bin at random. a.) How much do you expect the weight of the bag of tomatoes to be? 4*12 = 48oz b.) Find the standard deviation associated with the weight of the bag of tomatoes. Variance = 4 for a single tomato. Var(4*X) = 16*var(x) = 32. Standard Deviation = oz 15.) Suppose you only choose two tomatoes and you create the variable Y which is the difference in weight between the larger and smaller tomato. a.) What is the mean of Y? E(X Y) = E(X) E(Y) = 0 0 = 0 b.) What is the standard deviation of Y? Var(X Y) = = 8 so SD =

4 Use the following for questions In a large city, 72% of the people are known to own a cell phone, 38% are known to own a pager, and 29% own both a cell phone and a pager. Let A be the event that they own a cell phone and B be the event that they own a pager. 18.) Fill in a venn diagram representing this situation. 19.) Suppose an individual is drawn at random from this population. Find the following probabilities. a.) = 0.81 b.) = 0.29 c.) = 0.71 d.) = 43/( ) = ) Are these two events mutually exclusive? Explain. No, there is an overlap between the events. 21.) Are these two events independent? Explain. P(A) = 0.72, P(A B) = 29/38) = Because P(A) P(A B) These two events are not independent. (They are pretty close though, since the probability doesn t change much).

5 Use the following for questions 22 24: Bob has recently been hired by a shop downtown to help customers with various computer related problems. Lately, two different viruses have been bugging many customers virus Dummy and virus Smarty. It is estimated that about 65% of the customers with virus problems are bothered by virus Dummy and the remaining 35% by virus Smarty. If the computer is infected by virus Dummy, Bob has a 90% chance of fixing the problem. However, if the computer is infected by the virus Smarty, this chance is only 70%. 22.) Complete the following tree diagram representing this situation. 23.) If a computer is brought in by a customer, what is the probability that Bob will be able to fix it? P(Fix) = = ) If Bob was able to fix a computer, what is the probability that it was infected by virus Dummy? Prob(Dummy Fix) = 0.585/( ) = ) If 12 students are to be selected, what is the probability that more than 7 students in the sample voted for the democratic student party if, in a recent poll, 45% claimed to be democrat? First, they either are or are not, so this is Bernoullii trials. And, since we are looking for r successes in N trials, this is binomial (pdf). Binomialpdf (12,0.45,7) =

6 Use the following to answer questions 26 30: Chromosome defect A occurs in only one out of 200 adult males. A random sample of 1000 adult males is selected. Let the random variable X represent the number of males in the sample who have this chromosome defect. 26.) How many individuals from the sample do we expect to have this defect? P = 1/200 = and 0.005*1000 = 5 27.) What is the standard deviation on the number we expect to have this defect? Standard Deviation of a binomial = = = ) What is the probability that the first individual selected with this defect occurs on the 200 th person sampled? Geometric: 0.995*0.995* *0.995*0.005 = *0.005 = ) What is the probability that you get more than 6 people in the sample with this defect? P(X > 6) = 1- P(X 6) = 1 binomialcdf(1000, 0.005, 6) = = ) What is the probability that you find 5 or less people in the sample with this defect? binomialcdf(1000, 0.005, 6) = Use the following information to answer questions 31 35: Suppose that you are considering IQ scores, which are normally distributed with a mean of 100 and a standard deviation of 15. Determine the following. 31.) When selecting a single individual at random, what is the probability that they have a score of 80 or less? Z = (80 100)/15 = Prob = ) When selecting a single individual at random, what is the probability that they have a score between 90 and 130? Z 1 = ( )/15 = 2.00 Prob = Z 2 = (90 100)/15 = Prob = Prob in the middle = =

7 33.) When selecting a single individual at random, what IQ score is necessary in order for that individual to be in the top 8% of the population? Prob of 0.08 on the right means Prob of 0.92 on the left. This means Z = In this case: = (x 100)/15 so X = ) When selecting a single individual at random, what is the probability that they have a score of 145 or higher? P(X >= 145) = 1 P(X < 145) Z = ( ) / 15 = ) Suppose that we select 9 individuals randomly from the population. What is the probability that the mean IQ from this group is greater than 105? Z = ( )/(15/sqrt(9)) P = ) Suppose that we select 25 individuals randomly from the population. What is the probability that the mean IQ from this group is greater than 105? Z = ( )/(15/sqrt(25)) P =

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

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 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 information

AMS7: WEEK 4. CLASS 3

AMS7: 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 information

Unit 04 Review. Probability Rules

Unit 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 information

Lecture 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, 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 information

6. THE BINOMIAL DISTRIBUTION

6. 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 information

Chapter 6: Random Variables. Ch. 6-3: Binomial and Geometric Random Variables

Chapter 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 information

The Normal Probability Distribution

The 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 information

Chapter 6 Section Review day s.notebook. May 11, Honors Statistics. Aug 23-8:26 PM. 3. Review team test.

Chapter 6 Section Review day s.notebook. May 11, Honors Statistics. Aug 23-8:26 PM. 3. Review team test. Honors Statistics Aug 23-8:26 PM 3. Review team test Aug 23-8:31 PM 1 Nov 27-10:28 PM 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 Nov 27-9:53 PM 2 May 8-7:44 PM May 1-9:09 PM 3 Dec 1-2:08 PM Sep

More information

Version 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.

Version 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 information

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 - Would the correlation between x and y in the table above be positive or negative? The correlation is negative. -

More information

Unit2: Probabilityanddistributions. 3. Normal and binomial distributions

Unit2: Probabilityanddistributions. 3. Normal and binomial distributions Announcements Unit2: Probabilityanddistributions 3. Normal and binomial distributions Sta 101 - Summer 2017 Duke University, Department of Statistical Science PS: Explain your reasoning + show your work

More information

4 Random Variables and Distributions

4 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 information

A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon.

A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon. Chapter 14: random variables p394 A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon. Consider the experiment of tossing a coin. Define a random variable

More information

Unit2: Probabilityanddistributions. 3. Normal and binomial distributions

Unit2: Probabilityanddistributions. 3. Normal and binomial distributions Announcements Unit2: Probabilityanddistributions 3. Normal and binomial distributions Sta 101 - Fall 2017 Duke University, Department of Statistical Science Formatting of problem set submissions: Bad:

More information

NORMAL RANDOM VARIABLES (Normal or gaussian distribution)

NORMAL RANDOM VARIABLES (Normal or gaussian distribution) NORMAL RANDOM VARIABLES (Normal or gaussian distribution) Many variables, as pregnancy lengths, foot sizes etc.. exhibit a normal distribution. The shape of the distribution is a symmetric bell shape.

More information

Chapter Six Probability Distributions

Chapter Six Probability Distributions 6.1 Probability Distributions Discrete Random Variable Chapter Six Probability Distributions x P(x) 2 0.08 4 0.13 6 0.25 8 0.31 10 0.16 12 0.01 Practice. Construct a probability distribution for the number

More information

Normal distribution. We say that a random variable X follows the normal distribution if the probability density function of X is given by

Normal 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 information

Binomial and Normal Distributions. Example: Determine whether the following experiments are binomial experiments. Explain.

Binomial and Normal Distributions. Example: Determine whether the following experiments are binomial experiments. Explain. Binomial and Normal Distributions Objective 1: Determining if an Experiment is a Binomial Experiment For an experiment to be considered a binomial experiment, four things must hold: 1. The experiment is

More information

Probability & Sampling The Practice of Statistics 4e Mostly Chpts 5 7

Probability & Sampling The Practice of Statistics 4e Mostly Chpts 5 7 Probability & Sampling The Practice of Statistics 4e Mostly Chpts 5 7 Lew Davidson (Dr.D.) Mallard Creek High School Lewis.Davidson@cms.k12.nc.us 704-786-0470 Probability & Sampling The Practice of Statistics

More information

Solutions for practice questions: Chapter 15, Probability Distributions If you find any errors, please let me know at

Solutions for practice questions: Chapter 15, Probability Distributions If you find any errors, please let me know at Solutions for practice questions: Chapter 15, Probability Distributions If you find any errors, please let me know at mailto:msfrisbie@pfrisbie.com. 1. Let X represent the savings of a resident; X ~ N(3000,

More information

Chapter 6: Random Variables

Chapter 6: Random Variables Chapter 6: Random Variables Section 6. The Practice of Statistics, 4 th edition For AP* STARNES, YATES, MOORE Chapter 6 Random Variables 6.1 Discrete and Continuous Random Variables 6. 6.3 Binomial and

More information

Exam II Math 1342 Capters 3-5 HCCS. Name

Exam 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 information

The Central Limit Theorem. Sec. 8.2: The Random Variable. it s Distribution. it s Distribution

The Central Limit Theorem. Sec. 8.2: The Random Variable. it s Distribution. it s Distribution The Central Limit Theorem Sec. 8.1: The Random Variable it s Distribution Sec. 8.2: The Random Variable it s Distribution X p and and How Should You Think of a Random Variable? Imagine a bag with numbers

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. First Name: Last Name: SID: Class Time: M Tu W Th math10 - HW5 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) Which choice is another term that

More information

Math 243 Section 4.3 The Binomial Distribution

Math 243 Section 4.3 The Binomial Distribution Math 243 Section 4.3 The Binomial Distribution Overview Notation for the mean, standard deviation and variance The Binomial Model Bernoulli Trials Notation for the mean, standard deviation and variance

More information

Homework: 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 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 information

Counting Basics. Venn diagrams

Counting Basics. Venn diagrams Counting Basics Sets Ways of specifying sets Union and intersection Universal set and complements Empty set and disjoint sets Venn diagrams Counting Inclusion-exclusion Multiplication principle Addition

More information

Math 14 Lecture Notes Ch. 4.3

Math 14 Lecture Notes Ch. 4.3 4.3 The Binomial Distribution Example 1: The former Sacramento King's DeMarcus Cousins makes 77% of his free throws. If he shoots 3 times, what is the probability that he will make exactly 0, 1, 2, or

More information

No, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation.

No, 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 information

ECOSOC MS EXCEL LECTURE SERIES DISTRIBUTIONS

ECOSOC MS EXCEL LECTURE SERIES DISTRIBUTIONS ECOSOC MS EXCEL LECTURE SERIES DISTRIBUTIONS Module Excel provides probabilities for the following functions: (Note- There are many other functions also but here we discuss only those which will help in

More information

CH 5 Normal Probability Distributions Properties of the Normal Distribution

CH 5 Normal Probability Distributions Properties of the Normal Distribution Properties of the Normal Distribution Example A friend that is always late. Let X represent the amount of minutes that pass from the moment you are suppose to meet your friend until the moment your friend

More information

FINAL REVIEW W/ANSWERS

FINAL REVIEW W/ANSWERS FINAL REVIEW W/ANSWERS ( 03/15/08 - Sharon Coates) Concepts to review before answering the questions: A population consists of the entire group of people or objects of interest to an investigator, while

More information

AP Statistics Ch 8 The Binomial and Geometric Distributions

AP Statistics Ch 8 The Binomial and Geometric Distributions Ch 8.1 The Binomial Distributions The Binomial Setting A situation where these four conditions are satisfied is called a binomial setting. 1. Each observation falls into one of just two categories, which

More information

5.1 Mean, Median, & Mode

5.1 Mean, Median, & Mode 5.1 Mean, Median, & Mode definitions Mean: Median: Mode: Example 1 The Blue Jays score these amounts of runs in their last 9 games: 4, 7, 2, 4, 10, 5, 6, 7, 7 Find the mean, median, and mode: Example 2

More information

AP Statistics Test 5

AP 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 information

Homework: Due Wed, Feb 20 th. Chapter 8, # 60a + 62a (count together as 1), 74, 82

Homework: 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 information

A.REPRESENTATION OF DATA

A.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 information

Chapter 6: Random Variables

Chapter 6: Random Variables Chapter 6: Random Variables Section 6.3 The Practice of Statistics, 4 th edition For AP* STARNES, YATES, MOORE Chapter 6 Random Variables 6.1 Discrete and Continuous Random Variables 6.2 Transforming and

More information

Binomial Random Variables. Binomial Random Variables

Binomial 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 information

4.2 Bernoulli Trials and Binomial Distributions

4.2 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 information

5.2 Random Variables, Probability Histograms and Probability Distributions

5.2 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 information

MTH 245: Mathematics for Management, Life, and Social Sciences

MTH 245: Mathematics for Management, Life, and Social Sciences 1/14 MTH 245: Mathematics for Management, Life, and Social Sciences Section 7.6 Section 7.6: The Normal Distribution. 2/14 The Normal Distribution. Figure: Abraham DeMoivre Section 7.6: The Normal Distribution.

More information

STT315 Chapter 4 Random Variables & Probability Distributions AM KM

STT315 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 information

Fall 2015 Math 141:505 Exam 3 Form A

Fall 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 information

MATH 118 Class Notes For Chapter 5 By: Maan Omran

MATH 118 Class Notes For Chapter 5 By: Maan Omran MATH 118 Class Notes For Chapter 5 By: Maan Omran Section 5.1 Central Tendency Mode: the number or numbers that occur most often. Median: the number at the midpoint of a ranked data. Ex1: The test scores

More information

Business Statistics 41000: Probability 4

Business Statistics 41000: Probability 4 Business Statistics 41000: Probability 4 Drew D. Creal University of Chicago, Booth School of Business February 14 and 15, 2014 1 Class information Drew D. Creal Email: dcreal@chicagobooth.edu Office:

More information

Chapter 4 and 5 Note Guide: Probability Distributions

Chapter 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 information

Econ 6900: Statistical Problems. Instructor: Yogesh Uppal

Econ 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 information

Study Guide: Chapter 5, Sections 1 thru 3 (Probability Distributions)

Study Guide: Chapter 5, Sections 1 thru 3 (Probability Distributions) Study Guide: Chapter 5, Sections 1 thru 3 (Probability Distributions) Name SHORT ANSWER. 1) Fill in the missing value so that the following table represents a probability distribution. x 1 2 3 4 P(x) 0.09

More information

1. Steve says I have two children, one of which is a boy. Given this information, what is the probability that Steve has two boys?

1. Steve says I have two children, one of which is a boy. Given this information, what is the probability that Steve has two boys? Chapters 6 8 Review 1. Steve says I have two children, one of which is a boy. Given this information, what is the probability that Steve has two boys? (A) 1 (B) 3 1 (C) 3 (D) 4 1 (E) None of the above..

More information

Introduction to Statistics I

Introduction 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 information

Statistics, Their Distributions, and the Central Limit Theorem

Statistics, Their Distributions, and the Central Limit Theorem Statistics, Their Distributions, and the Central Limit Theorem MATH 3342 Sections 5.3 and 5.4 Sample Means Suppose you sample from a popula0on 10 0mes. You record the following sample means: 10.1 9.5 9.6

More information

1. (9; 3ea) The table lists the survey results of 100 non-senior students. Math major Art major Biology major

1. (9; 3ea) The table lists the survey results of 100 non-senior students. Math major Art major Biology major Math 54 Test #2(Chapter 4, 5, 6, 7) Name: Show all necessary work for full credit. You may use graphing calculators for your calculation, but you must show all detail and use the proper notations. Total

More information

MATH 112 Section 7.3: Understanding Chance

MATH 112 Section 7.3: Understanding Chance MATH 112 Section 7.3: Understanding Chance Prof. Jonathan Duncan Walla Walla University Autumn Quarter, 2007 Outline 1 Introduction to Probability 2 Theoretical vs. Experimental Probability 3 Advanced

More information

Midterm Test 1 (Sample) Student Name (PRINT):... Student Signature:... Use pencil, so that you can erase and rewrite if necessary.

Midterm Test 1 (Sample) Student Name (PRINT):... Student Signature:... Use pencil, so that you can erase and rewrite if necessary. MA 180/418 Midterm Test 1 (Sample) Student Name (PRINT):............................................. Student Signature:................................................... Use pencil, so that you can erase

More information

MANAGEMENT PRINCIPLES AND STATISTICS (252 BE)

MANAGEMENT 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 information

(Practice Version) Midterm Exam 1

(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 information

Examples of continuous probability distributions: The normal and standard normal

Examples of continuous probability distributions: The normal and standard normal Examples of continuous probability distributions: The normal and standard normal The Normal Distribution f(x) Changing μ shifts the distribution left or right. Changing σ increases or decreases the spread.

More information

Standard Normal, Inverse Normal and Sampling Distributions

Standard Normal, Inverse Normal and Sampling Distributions Standard Normal, Inverse Normal and Sampling Distributions Section 5.5 & 6.6 Cathy Poliak, Ph.D. cathy@math.uh.edu Office in Fleming 11c Department of Mathematics University of Houston Lecture 9-3339 Cathy

More information

Chapter 6 Continuous Probability Distributions. Learning objectives

Chapter 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 information

8.1 Binomial Distributions

8.1 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 information

STA258H5. Al Nosedal and Alison Weir. Winter Al Nosedal and Alison Weir STA258H5 Winter / 41

STA258H5. Al Nosedal and Alison Weir. Winter Al Nosedal and Alison Weir STA258H5 Winter / 41 STA258H5 Al Nosedal and Alison Weir Winter 2017 Al Nosedal and Alison Weir STA258H5 Winter 2017 1 / 41 NORMAL APPROXIMATION TO THE BINOMIAL DISTRIBUTION. Al Nosedal and Alison Weir STA258H5 Winter 2017

More information

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions

Probability 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 information

Density curves. (James Madison University) February 4, / 20

Density curves. (James Madison University) February 4, / 20 Density curves Figure 6.2 p 230. A density curve is always on or above the horizontal axis, and has area exactly 1 underneath it. A density curve describes the overall pattern of a distribution. Example

More information

1/2 2. Mean & variance. Mean & standard deviation

1/2 2. Mean & variance. Mean & standard deviation Question # 1 of 10 ( Start time: 09:46:03 PM ) Total Marks: 1 The probability distribution of X is given below. x: 0 1 2 3 4 p(x): 0.73? 0.06 0.04 0.01 What is the value of missing probability? 0.54 0.16

More information

Math Week in Review #10. Experiments with two outcomes ( success and failure ) are called Bernoulli or binomial trials.

Math Week in Review #10. Experiments with two outcomes ( success and failure ) are called Bernoulli or binomial trials. Math 141 Spring 2006 c Heather Ramsey Page 1 Section 8.4 - Binomial Distribution Math 141 - Week in Review #10 Experiments with two outcomes ( success and failure ) are called Bernoulli or binomial trials.

More information

STUDY SET 2. Continuous Probability Distributions. ANSWER: Without continuity correction P(X>10) = P(Z>-0.66) =

STUDY 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 information

MATH1215: Mathematical Thinking Sec. 08 Spring Worksheet 9: Solution. x P(x)

MATH1215: 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 information

Review of the Topics for Midterm I

Review of the Topics for Midterm I Review of the Topics for Midterm I STA 100 Lecture 9 I. Introduction The objective of statistics is to make inferences about a population based on information contained in a sample. A population is the

More information

Section Introduction to Normal Distributions

Section Introduction to Normal Distributions Section 6.1-6.2 Introduction to Normal Distributions 2012 Pearson Education, Inc. All rights reserved. 1 of 105 Section 6.1-6.2 Objectives Interpret graphs of normal probability distributions Find areas

More information

A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon.

A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon. Chapter 14: random variables p394 A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon. Consider the experiment of tossing a coin. Define a random variable

More information

Week 7. Texas A& M University. Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4

Week 7. Texas A& M University. Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4 Week 7 Oğuz Gezmiş Texas A& M University Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4 Oğuz Gezmiş (TAMU) Topics in Contemporary Mathematics II Week7 1 / 19

More information

Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance

Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance Prof. Tesler Math 186 Winter 2017 Prof. Tesler Ch. 5: Confidence Intervals, Sample Variance Math 186 / Winter 2017 1 / 29 Estimating parameters

More information

Chapter 8: Binomial and Geometric Distributions

Chapter 8: Binomial and Geometric Distributions Chapter 8: Binomial and Geometric Distributions Section 8.1 Binomial Distributions The Practice of Statistics, 4 th edition For AP* STARNES, YATES, MOORE Section 8.1 Binomial Distribution Learning Objectives

More information

Part 1 In which we meet the law of averages. The Law of Averages. The Expected Value & The Standard Error. Where Are We Going?

Part 1 In which we meet the law of averages. The Law of Averages. The Expected Value & The Standard Error. Where Are We Going? 1 The Law of Averages The Expected Value & The Standard Error Where Are We Going? Sums of random numbers The law of averages Box models for generating random numbers Sums of draws: the Expected Value Standard

More information

Review for Final Exam Spring 2014 Jeremy Orloff and Jonathan Bloom

Review 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 information

Chapter 6. The Normal Probability Distributions

Chapter 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 information

Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc.

Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc. Chapter 8 Measures of Center Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc. Data that can only be integer

More information

Example. Chapter 8 Probability Distributions and Statistics Section 8.1 Distributions of Random Variables

Example. Chapter 8 Probability Distributions and Statistics Section 8.1 Distributions of Random Variables Chapter 8 Probability Distributions and Statistics Section 8.1 Distributions of Random Variables You are dealt a hand of 5 cards. Find the probability distribution table for the number of hearts. Graph

More information

Mathacle. PSet Stats, Concepts In Statistics Level Number Name: Date: Distribution Distribute in anyway but normal

Mathacle. 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 information

Probability is the tool used for anticipating what the distribution of data should look like under a given model.

Probability is the tool used for anticipating what the distribution of data should look like under a given model. AP Statistics NAME: Exam Review: Strand 3: Anticipating Patterns Date: Block: III. Anticipating Patterns: Exploring random phenomena using probability and simulation (20%-30%) Probability is the tool used

More information

4.3 Normal distribution

4.3 Normal distribution 43 Normal distribution Prof Tesler Math 186 Winter 216 Prof Tesler 43 Normal distribution Math 186 / Winter 216 1 / 4 Normal distribution aka Bell curve and Gaussian distribution The normal distribution

More information

Section Distributions of Random Variables

Section 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 information

2011 Pearson Education, Inc

2011 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 information

Sampling & populations

Sampling & populations Sampling & populations Sample proportions Sampling distribution - small populations Sampling distribution - large populations Sampling distribution - normal distribution approximation Mean & variance of

More information

Section 6.3 Binomial and Geometric Random Variables

Section 6.3 Binomial and Geometric Random Variables Section 6.3 Binomial and Geometric Random Variables Mrs. Daniel AP Stats Binomial Settings A binomial setting arises when we perform several independent trials of the same chance process and record the

More information

Discrete Random Variables

Discrete 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 information

Central Limit Theorem, Joint Distributions Spring 2018

Central 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 information

Discrete Random Variables

Discrete 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 information

Chapter 5 Probability Distributions. Section 5-2 Random Variables. Random Variable Probability Distribution. Discrete and Continuous Random Variables

Chapter 5 Probability Distributions. Section 5-2 Random Variables. Random Variable Probability Distribution. Discrete and Continuous Random Variables Chapter 5 Probability Distributions Section 5-2 Random Variables 5-2 Random Variables 5-3 Binomial Probability Distributions 5-4 Mean, Variance and Standard Deviation for the Binomial Distribution Random

More information

MTH 245: Mathematics for Management, Life, and Social Sciences

MTH 245: Mathematics for Management, Life, and Social Sciences 1/14 MTH 245: Mathematics for Management, Life, and Social Sciences May 18, 2015 Section 7.6 Section 7.6: The Normal Distribution. 2/14 The Normal Distribution. Figure: Abraham DeMoivre Section 7.6: The

More information

A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x)

A 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 information

Chapter 8 Homework Solutions Compiled by Joe Kahlig. speed(x) freq 25 x < x < x < x < x < x < 55 5

Chapter 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 information

Exercises for Chapter (5)

Exercises for Chapter (5) Exercises for Chapter (5) MULTILE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) 500 families were interviewed and the number of children per family was

More information

Section Random Variables and Histograms

Section Random Variables and Histograms Section 3.1 - Random Variables and Histograms 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 information

Lecture 7 Random Variables

Lecture 7 Random Variables Lecture 7 Random Variables Definition: A random variable is a variable whose value is a numerical outcome of a random phenomenon, so its values are determined by chance. We shall use letters such as X

More information

MATH 264 Problem Homework I

MATH 264 Problem Homework I MATH Problem Homework I Due to December 9, 00@:0 PROBLEMS & SOLUTIONS. A student answers a multiple-choice examination question that offers four possible answers. Suppose that the probability that the

More information

LECTURE 6 DISTRIBUTIONS

LECTURE 6 DISTRIBUTIONS LECTURE 6 DISTRIBUTIONS OVERVIEW Uniform Distribution Normal Distribution Random Variables Continuous Distributions MOST OF THE SLIDES ADOPTED FROM OPENINTRO STATS BOOK. NORMAL DISTRIBUTION Unimodal and

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distribution Properties of a Binomial Experiment 1. It consists of a fixed number of observations called trials. 2. Each trial can result in one of only two mutually exclusive outcomes labeled

More information

4.1 Probability Distributions

4.1 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 information