Mathematical Methods: Practice Problem Solving Task - Probability

Size: px
Start display at page:

Download "Mathematical Methods: Practice Problem Solving Task - Probability"

Transcription

1 Mathematical Methods: Practice Problem Solving Task - Probability Question 1 refers to the following graph The following graph shows the probabilities of the 5 outcomes (1 to 5) from a spinner, with one missing. Question 1 (a) The missing entry on the graph would have a probability of:! A. " B. 3 C. 0.3 # D. $" E. 0.2 Show that the answer is D. 1

2 What incorrect reasoning would lead a student to choosing A as the answer? (1 mark) (b) Hence show the expected value of the number spun in the long run is (c) Ms Shardlow has tried to work out the variance for the number showing on this spinner. Unfortunately, she has made a mistake in her calculations. Her working is given below. Identify the error, circle it and then find the correct value for the variance. E(number on spinner) = 3.38 x $ x $ Pr (X = x) Variance = = (d) Find the Pr (μ σ x μ + σ) 2

3 (e) The median value and the modal value for this spinner will be, respectively: A. Median = 3.5, Mode = 4 B. Median = 3, Mode = 0.3 C. Median = 0.5, Mode = 0.3 D. Median = 4, Mode = 4 E. Median = 3, Mode = 4 Correct Response: Working and/or reasoning for your response: (3 marks) Question 2 Mr Schmidt plays basketball for his local team. A record of his performance over the last season was kept and part of this is represented in the Venn diagram below. n ε = 200 A = {goals scored} B = {attempts at a 3-point shot} (a) What is the probability that, given he scored a goal, it was a 3-pointer? A. 9$ :" B. $ # C. 0.3 D. $! 9; E Correct Response: 3

4 Working and/or reasoning for your response: (3 marks) (b) Determine whether events A and B are mathematically independent (c) (i) Let X = number of goals Mr Schmidt scores in 8 shots at goal We can write X Bi (8,!9# $;; ). The following mathematical expression gives the probability for what possible event? Describe the event in words. Pr (event) = 1 8 0!9# $;; ; "> $;;? + 8 1!9# $;;! "> $;; # ( 1 mark) (ii) Find the probability that Mr Schmidt was able to score the first 4 goals only in his 8 shots. Give your answer correct to 4 decimal places. 4

5 Question 3: The diagram below shows the graphs of two normal distribution curves with means of µ 1 and µ 2 and standard deviations of σ 1 and σ 2 respectively. X! ~N(μ!, σ! $ ) X $ ~N(μ $, σ $ $ ) (a) Which one of the following is true? A. µ 1 < µ 2 and σ 1 < σ 2 B. mode 1 < mode 2 and σ 1 > σ 2 C. µ 1 > µ 2 and σ 1 < σ 2 D. median 1 = median 2 and σ 1 > σ 2 E. µ 1 = µ 2 and σ 1 < σ 2 Correct Response: Working and/or reasoning: 5

6 (b) Sketch two normal distribution curves that would make Option B in (a) a correct response. Be careful to label your curves with X 1 and X 2 Question 4 (a) If X is a discrete variable with E(X) = 3 and VAR (X) = 25 then E ( 2X + 1) and VAR ( 2X + 1) are respectively: A. 3 and 25 B. 5 and 50 C. 7 and 51 D. 5 and 100 E. 5 and 100 Correct Response: Working/Reasoning: (3 marks) (b) Find the standard deviation of (3! $ X) 6

7 Question 5 Mrs Batsakis has two favouriate café she likes to visit for coffee, Life on Mars and Tony s Cafe. Over the holidays, she decided to visit only one of these cafes on any one day. If she visited Life on Mars one day, then the probability that she visited Tony s Cafe the next was 0.7. If she visited Tony s Cafe one day, then the probability that she will again visit Tony s Cafe on the next day is (a) This information can be summarised in the incomplete transition matrix, T, as Explain why the missing elements are 0.3 and 0.75 respectively and hence write down the probability that if Mrs Batsakis visited Tony s Cafe on one day, she will visit Life on Mars the next. (b) Find the probability, giving your answer correct to 3 decimal places, that if Mrs Batsakis visited Tony s Café on a Monday, that she would be visiting Tony s Café again on the Friday of that same week. 7

8 (c) The steady state probability of Mrs Batsakis visiting Life on Mars Cafe on a given day if she was to continue this pattern of behaviour through until the end of the year would be A.!" $: B.!9 $: C. > 9 D.! 9 E. >!; Working and/or reasoning: Correct Response: Question 6 The probability density function of a continuous random variable is given by: f x = 0.1 x 2 $ x 2 bx < x A 0 elsewhere (a) State the area bounded by the curve for 0 x 2 (b) The area bounded by the curve for 2 < x A must be: A B.?!" C. #!" D. 1 E Explain why C is the correct response (1 mark) 8

9 (c) (i) Explain why the graph of f(x) must be continuous at x=2 (ii) Hence show that the value of b = R >" (d) Find the value of A (1 + 2 = 3 marks) (e) Find the median of the distribution, correct to three decimal places. END OF PART 1 9

Continuous Probability Distributions & Normal Distribution

Continuous Probability Distributions & Normal Distribution Mathematical Methods Units 3/4 Student Learning Plan Continuous Probability Distributions & Normal Distribution 7 lessons Notes: Students need practice in recognising whether a problem involves a discrete

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

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

In a binomial experiment of n trials, where p = probability of success and q = probability of failure. mean variance standard deviation

In a binomial experiment of n trials, where p = probability of success and q = probability of failure. mean variance standard deviation Name In a binomial experiment of n trials, where p = probability of success and q = probability of failure mean variance standard deviation µ = n p σ = n p q σ = n p q Notation X ~ B(n, p) The probability

More information

FACULTY OF SCIENCE DEPARTMENT OF STATISTICS

FACULTY OF SCIENCE DEPARTMENT OF STATISTICS FACULTY OF SCIENCE DEPARTMENT OF STATISTICS MODULE ATE1A10 / ATE01A1 ANALYTICAL TECHNIQUES A CAMPUS APK, DFC & SWC SUPPLEMENTARY SUMMATIVE ASSESSMENT DATE 15 JULY 2014 SESSION 15:00 17:00 ASSESSOR MODERATOR

More information

PROBABILITY DISTRIBUTIONS

PROBABILITY DISTRIBUTIONS CHAPTER 3 PROBABILITY DISTRIBUTIONS Page Contents 3.1 Introduction to Probability Distributions 51 3.2 The Normal Distribution 56 3.3 The Binomial Distribution 60 3.4 The Poisson Distribution 64 Exercise

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

Chapter 6: Random Variables and Probability Distributions

Chapter 6: Random Variables and Probability Distributions Chapter 6: Random Variables and Distributions These notes reflect material from our text, Statistics, Learning from Data, First Edition, by Roxy Pec, published by CENGAGE Learning, 2015. Random variables

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

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Business and Economics Chapter 5 Continuous Random Variables and Probability Distributions Ch. 5-1 Probability Distributions Probability Distributions Ch. 4 Discrete Continuous Ch. 5 Probability

More information

Statistics (This summary is for chapters 18, 29 and section H of chapter 19)

Statistics (This summary is for chapters 18, 29 and section H of chapter 19) Statistics (This summary is for chapters 18, 29 and section H of chapter 19) Mean, Median, Mode Mode: most common value Median: middle value (when the values are in order) Mean = total how many = x n =

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

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

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

Making Sense of Cents

Making Sense of Cents Name: Date: Making Sense of Cents Exploring the Central Limit Theorem Many of the variables that you have studied so far in this class have had a normal distribution. You have used a table of the normal

More information

Statistics vs. statistics

Statistics vs. statistics Statistics vs. statistics Question: What is Statistics (with a capital S)? Definition: Statistics is the science of collecting, organizing, summarizing and interpreting data. Note: There are 2 main ways

More information

Chapter 4 and Chapter 5 Test Review Worksheet

Chapter 4 and Chapter 5 Test Review Worksheet Name: Date: Hour: Chapter 4 and Chapter 5 Test Review Worksheet You must shade all provided graphs, you must round all z-scores to 2 places after the decimal, you must round all probabilities to at least

More information

Chapter Seven: Confidence Intervals and Sample Size

Chapter Seven: Confidence Intervals and Sample Size Chapter Seven: Confidence Intervals and Sample Size A point estimate is: The best point estimate of the population mean µ is the sample mean X. Three Properties of a Good Estimator 1. Unbiased 2. Consistent

More information

MAS187/AEF258. University of Newcastle upon Tyne

MAS187/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 information

Statistics (This summary is for chapters 17, 28, 29 and section G of chapter 19)

Statistics (This summary is for chapters 17, 28, 29 and section G of chapter 19) Statistics (This summary is for chapters 17, 28, 29 and section G of chapter 19) Mean, Median, Mode Mode: most common value Median: middle value (when the values are in order) Mean = total how many = x

More information

Honors Statistics. Daily Agenda

Honors Statistics. Daily Agenda Honors Statistics Daily Agenda 1. Review OTL C6#5 2. Quiz Section 6.1 A-Skip 35, 39, 40 Crickets The length in inches of a cricket chosen at random from a field is a random variable X with mean 1.2 inches

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

Normal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial.

Normal 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 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

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

What type of distribution is this? tml

What type of distribution is this?  tml Warm Up Calculate the average Broncos score for the 2013 Season! 24, 27, 10, 10, 34, 37, 20, 51, 35, 31, 27, 28, 45, 33, 35, 52, 52, 37, 41, 49, 24, 26 What type of distribution is this? http://www.mathsisfun.com/data/quincunx.h

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

Statistics 511 Supplemental Materials

Statistics 511 Supplemental Materials Gaussian (or Normal) Random Variable In this section we introduce the Gaussian Random Variable, which is more commonly referred to as the Normal Random Variable. This is a random variable that has a bellshaped

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

Prob and Stats, Nov 7

Prob and Stats, Nov 7 Prob and Stats, Nov 7 The Standard Normal Distribution Book Sections: 7.1, 7.2 Essential Questions: What is the standard normal distribution, how is it related to all other normal distributions, and how

More information

Fall 2011 Exam Score: /75. Exam 3

Fall 2011 Exam Score: /75. Exam 3 Math 12 Fall 2011 Name Exam Score: /75 Total Class Percent to Date Exam 3 For problems 1-10, circle the letter next to the response that best answers the question or completes the sentence. You do not

More information

AP STATISTICS FALL SEMESTSER FINAL EXAM STUDY GUIDE

AP STATISTICS FALL SEMESTSER FINAL EXAM STUDY GUIDE AP STATISTICS Name: FALL SEMESTSER FINAL EXAM STUDY GUIDE Period: *Go over Vocabulary Notecards! *This is not a comprehensive review you still should look over your past notes, homework/practice, Quizzes,

More information

First Exam for MTH 23

First Exam for MTH 23 First Exam for MTH 23 October 5, 2017 Nikos Apostolakis Name: Instructions: This exam contains 6 pages (including this cover page) and 5 questions. Each question is worth 20 points, and so the perfect

More information

Uniform Probability Distribution. Continuous Random Variables &

Uniform Probability Distribution. Continuous Random Variables & Continuous Random Variables & What is a Random Variable? It is a quantity whose values are real numbers and are determined by the number of desired outcomes of an experiment. Is there any special Random

More information

Probability: Week 4. Kwonsang Lee. University of Pennsylvania February 13, 2015

Probability: Week 4. Kwonsang Lee. University of Pennsylvania February 13, 2015 Probability: Week 4 Kwonsang Lee University of Pennsylvania kwonlee@wharton.upenn.edu February 13, 2015 Kwonsang Lee STAT111 February 13, 2015 1 / 21 Probability Sample space S: the set of all possible

More information

A LEVEL MATHEMATICS QUESTIONSHEETS DISCRETE RANDOM VARIABLES

A LEVEL MATHEMATICS QUESTIONSHEETS DISCRETE RANDOM VARIABLES 1. Two fair dice are thrown. The random variable T represents the maximum score on the two dice. (For example, if the first dice shows a and the second a 4, then the value of T would be 4). a) Find the

More information

Lecture 8. The Binomial Distribution. Binomial Distribution. Binomial Distribution. Probability Distributions: Normal and Binomial

Lecture 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 information

Section 3.4 The Normal Distribution

Section 3.4 The Normal Distribution Section 3.4 The Normal Distribution Properties of the Normal Distribution Curve 1. We denote the normal random variable with X = x. 2. The curve has a peak at x = µ. 3. The curve is symmetric about the

More information

. (i) What is the probability that X is at most 8.75? =.875

. (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 information

3. Continuous Probability Distributions

3. Continuous Probability Distributions 3.1 Continuous probability distributions 3. Continuous Probability Distributions K The normal probability distribution A continuous random variable X is said to have a normal distribution if it has a probability

More information

Chapter 7 1. Random Variables

Chapter 7 1. Random Variables Chapter 7 1 Random Variables random variable numerical variable whose value depends on the outcome of a chance experiment - discrete if its possible values are isolated points on a number line - continuous

More information

UNIT 4 MATHEMATICAL METHODS

UNIT 4 MATHEMATICAL METHODS UNIT 4 MATHEMATICAL METHODS PROBABILITY Section 1: Introductory Probability Basic Probability Facts Probabilities of Simple Events Overview of Set Language Venn Diagrams Probabilities of Compound Events

More information

Chapter ! Bell Shaped

Chapter ! Bell Shaped Chapter 6 6-1 Business Statistics: A First Course 5 th Edition Chapter 7 Continuous Probability Distributions Learning Objectives In this chapter, you learn:! To compute probabilities from the normal distribution!

More information

Chapter 2: Random Variables (Cont d)

Chapter 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 information

6.1 Graphs of Normal Probability Distributions:

6.1 Graphs of Normal Probability Distributions: 6.1 Graphs of Normal Probability Distributions: Normal Distribution one of the most important examples of a continuous probability distribution, studied by Abraham de Moivre (1667 1754) and Carl Friedrich

More information

Answer Key: Quiz2-Chapter5: Discrete Probability Distribution

Answer 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 information

Continuous Distributions

Continuous 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

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 - HW3 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) Continuous random variables are

More information

Lecture 9. Probability Distributions. Outline. Outline

Lecture 9. Probability Distributions. Outline. Outline Outline Lecture 9 Probability Distributions 6-1 Introduction 6- Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7- Properties of the Normal Distribution

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. 6.1-6.2 Quiz Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the 1) X is a normally distributed random variable with a mean of 11.00. If the probability that

More information

Class 16. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

Class 16. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700 Class 16 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 013 by D.B. Rowe 1 Agenda: Recap Chapter 7. - 7.3 Lecture Chapter 8.1-8. Review Chapter 6. Problem Solving

More information

Department 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. 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 information

Lecture 9. Probability Distributions

Lecture 9. Probability Distributions Lecture 9 Probability Distributions Outline 6-1 Introduction 6-2 Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7-2 Properties of the Normal Distribution

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

Sampling Distribution

Sampling Distribution MAT 2379 (Spring 2012) Sampling Distribution Definition : Let X 1,..., X n be a collection of random variables. We say that they are identically distributed if they have a common distribution. Definition

More information

Functional Skills Mathematics Level 1 sample assessment

Functional Skills Mathematics Level 1 sample assessment Functional Skills Mathematics Level 1 sample assessment Marking scheme PAPER-BASED These materials relate to the assessments that will be in use from September 015 www.cityandguilds.com June 015 Version

More information

Two hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER

Two hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER Two hours MATH20802 To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER STATISTICAL METHODS Answer any FOUR of the SIX questions.

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

Chapter 4 Continuous Random Variables and Probability Distributions

Chapter 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 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

Set up a normal distribution curve, to help estimate the percent of the band that, on average, practices a greater number of hours than Alexis.

Set up a normal distribution curve, to help estimate the percent of the band that, on average, practices a greater number of hours than Alexis. Section 5.5 Z-Scores Example 1 Alexis plays in her school jazz band. Band members practice an average of 16.5 h per week, with a standard deviation of 4.2 h. Alexis practices an average of 22 h per week.

More information

The Normal Distribution

The Normal Distribution 5.1 Introduction to Normal Distributions and the Standard Normal Distribution Section Learning objectives: 1. How to interpret graphs of normal probability distributions 2. How to find areas under the

More information

Consider the following examples: ex: let X = tossing a coin three times and counting the number of heads

Consider the following examples: ex: let X = tossing a coin three times and counting the number of heads Overview Both chapters and 6 deal with a similar concept probability distributions. The difference is that chapter concerns itself with discrete probability distribution while chapter 6 covers continuous

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

TOPIC: PROBABILITY DISTRIBUTIONS

TOPIC: PROBABILITY DISTRIBUTIONS TOPIC: PROBABILITY DISTRIBUTIONS There are two types of random variables: A Discrete random variable can take on only specified, distinct values. A Continuous random variable can take on any value within

More information

INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.

INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. Summary of the previous lecture Moments of a distribubon Measures of

More information

Two Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00

Two Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00 Two Hours MATH38191 Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER STATISTICAL MODELLING IN FINANCE 22 January 2015 14:00 16:00 Answer ALL TWO questions

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

ST. DAVID S MARIST INANDA

ST. DAVID S MARIST INANDA ST. DAVID S MARIST INANDA MATHEMATICS NOVEMBER EXAMINATION GRADE 11 PAPER 1 8 th NOVEMBER 2016 EXAMINER: MRS S RICHARD MARKS: 125 MODERATOR: MRS C KENNEDY TIME: 2 1 Hours 2 NAME: PLEASE PUT A CROSS NEXT

More information

ECON 214 Elements of Statistics for Economists 2016/2017

ECON 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 information

Lecture 6: Chapter 6

Lecture 6: Chapter 6 Lecture 6: Chapter 6 C C Moxley UAB Mathematics 3 October 16 6.1 Continuous Probability Distributions Last week, we discussed the binomial probability distribution, which was discrete. 6.1 Continuous Probability

More information

6.1 Discrete & Continuous Random Variables. Nov 4 6:53 PM. Objectives

6.1 Discrete & Continuous Random Variables. Nov 4 6:53 PM. Objectives 6.1 Discrete & Continuous Random Variables examples vocab Objectives Today we will... - Compute probabilities using the probability distribution of a discrete random variable. - Calculate and interpret

More information

Stat 101 Exam 1 - Embers Important Formulas and Concepts 1

Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1 Chapter 1 1.1 Definitions Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1. Data Any collection of numbers, characters, images, or other items that provide information about something. 2.

More information

Central Limit Theorem (cont d) 7/28/2006

Central Limit Theorem (cont d) 7/28/2006 Central Limit Theorem (cont d) 7/28/2006 Central Limit Theorem for Binomial Distributions Theorem. For the binomial distribution b(n, p, j) we have lim npq b(n, p, np + x npq ) = φ(x), n where φ(x) is

More information

Statistics S1 Advanced/Advanced Subsidiary

Statistics S1 Advanced/Advanced Subsidiary Paper Reference(s) 6683/01 Edexcel GCE Statistics S1 Advanced/Advanced Subsidiary Tuesday 10 June 2014 Morning Time: 1 hour 30 minutes Materials required for examination Mathematical Formulae (Pink) Items

More information

Label the section where the total demand is the same as one demand and where total demand is different from both individual demand curves.

Label the section where the total demand is the same as one demand and where total demand is different from both individual demand curves. UVic Econ 103C with Peter Bell Technical Practice Exam #1 Markets Assigned: Monday May 12. Due: 5PM Friday May 23. Please submit a computer and/or handwritten response to each question. Please submit your

More information

Test 7A AP Statistics Name: Directions: Work on these sheets.

Test 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 information

Lecture 9 - Sampling Distributions and the CLT

Lecture 9 - Sampling Distributions and the CLT Lecture 9 - Sampling Distributions and the CLT Sta102/BME102 Colin Rundel September 23, 2015 1 Variability of Estimates Activity Sampling distributions - via simulation Sampling distributions - via CLT

More information

Shifting and rescaling data distributions

Shifting and rescaling data distributions Shifting and rescaling data distributions It is useful to consider the effect of systematic alterations of all the values in a data set. The simplest such systematic effect is a shift by a fixed constant.

More information

MLLunsford 1. Activity: Central Limit Theorem Theory and Computations

MLLunsford 1. Activity: Central Limit Theorem Theory and Computations MLLunsford 1 Activity: Central Limit Theorem Theory and Computations Concepts: The Central Limit Theorem; computations using the Central Limit Theorem. Prerequisites: The student should be familiar with

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

Sampling and sampling distribution

Sampling and sampling distribution Sampling and sampling distribution September 12, 2017 STAT 101 Class 5 Slide 1 Outline of Topics 1 Sampling 2 Sampling distribution of a mean 3 Sampling distribution of a proportion STAT 101 Class 5 Slide

More information

Much of what appears here comes from ideas presented in the book:

Much of what appears here comes from ideas presented in the book: Chapter 11 Robust statistical methods Much of what appears here comes from ideas presented in the book: Huber, Peter J. (1981), Robust statistics, John Wiley & Sons (New York; Chichester). There are many

More information

MA131 Lecture 8.2. The normal distribution curve can be considered as a probability distribution curve for normally distributed variables.

MA131 Lecture 8.2. The normal distribution curve can be considered as a probability distribution curve for normally distributed variables. Normal distribution curve as probability distribution curve The normal distribution curve can be considered as a probability distribution curve for normally distributed variables. The area under the normal

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

DATA HANDLING Five-Number Summary

DATA HANDLING Five-Number Summary DATA HANDLING Five-Number Summary The five-number summary consists of the minimum and maximum values, the median, and the upper and lower quartiles. The minimum and the maximum are the smallest and greatest

More information

On one of the feet? 1 2. On red? 1 4. Within 1 of the vertical black line at the top?( 1 to 1 2

On one of the feet? 1 2. On red? 1 4. Within 1 of the vertical black line at the top?( 1 to 1 2 Continuous Random Variable If I spin a spinner, what is the probability the pointer lands... On one of the feet? 1 2. On red? 1 4. Within 1 of the vertical black line at the top?( 1 to 1 2 )? 360 = 1 180.

More information

Edexcel Statistics 1 Normal Distribution Edited by: K V Kumaran

Edexcel Statistics 1 Normal Distribution Edited by: K V Kumaran Edexcel Statistics 1 Normal Distribution Edited by: K V Kumaran kumarmaths.weebly.com 1 kumarmaths.weebly.com 2 kumarmaths.weebly.com 3 kumarmaths.weebly.com 4 kumarmaths.weebly.com 5 kumarmaths.weebly.com

More information

Confidence Intervals Introduction

Confidence Intervals Introduction Confidence Intervals Introduction A point estimate provides no information about the precision and reliability of estimation. For example, the sample mean X is a point estimate of the population mean μ

More information

FORMULA FOR STANDARD DEVIATION:

FORMULA FOR STANDARD DEVIATION: Chapter 5 Review: Statistics Textbook p.210-282 Summary: p.238-239, p.278-279 Practice Questions p.240, p.280-282 Z- Score Table p.592 Key Concepts: Central Tendency, Standard Deviation, Graphing, Normal

More information

Mr. Orchard s Math 141 WIR 8.5, 8.6, 5.1 Week 13

Mr. Orchard s Math 141 WIR 8.5, 8.6, 5.1 Week 13 1. Find the following probabilities, where Z is a random variable with a standard normal distribution and X is a normal random variable with mean µ = 380 and standard deviation σ = 21: (Round your answers

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

As you draw random samples of size n, as n increases, the sample means tend to be normally distributed.

As you draw random samples of size n, as n increases, the sample means tend to be normally distributed. The Central Limit Theorem The central limit theorem (clt for short) is one of the most powerful and useful ideas in all of statistics. The clt says that if we collect samples of size n with a "large enough

More information

Section 7.5 The Normal Distribution. Section 7.6 Application of the Normal Distribution

Section 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 information

Class 11. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

Class 11. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700 Class 11 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 2017 by D.B. Rowe 1 Agenda: Recap Chapter 5.3 continued Lecture 6.1-6.2 Go over Eam 2. 2 5: Probability

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

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

Chapter 5. Continuous Random Variables and Probability Distributions. 5.1 Continuous Random Variables

Chapter 5. Continuous Random Variables and Probability Distributions. 5.1 Continuous Random Variables Chapter 5 Continuous Random Variables and Probability Distributions 5.1 Continuous Random Variables 1 2CHAPTER 5. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Probability Distributions Probability

More information

Page Points Score Total: 100

Page Points Score Total: 100 Math 1130 Spring 2019 Sample Midterm 2b 2/28/19 Name (Print): Username.#: Lecturer: Rec. Instructor: Rec. Time: This exam contains 10 pages (including this cover page) and 9 problems. Check to see if any

More information