1. Find the area under the standard normal curve between z = 0 and z = 3. (a) (b) (c) (d)

Size: px
Start display at page:

Download "1. Find the area under the standard normal curve between z = 0 and z = 3. (a) (b) (c) (d)"

Transcription

1 STA 2023 Practice 3 You may receive assistace from the Math Ceter. These problems are iteded to provide supplemetary problems i preparatio for test 3. This packet does ot ecessarily reflect the umber, type, or topics of problems that will appear o the actual test, or is it guarateed to cover every topic that may appear o the test. Be sure to study all assiged homework problems. Part I Multiple Choice 1. Fid the area uder the stadard ormal curve betwee z = 0 ad z = 3. (a) (b) (c) (d) Fid the area uder the stadard ormal curve to the right of z = 1. (a) (b) (c) (d) For a stadard ormal curve, fid the z-score that separates the bottom 90% from the top 10%. (a) 1.28 (b) 0.28 (c) 1.52 (d) For a stadard ormal curve, fid the z-scores for which 90% of the distributio s area lies betwee z ad z. (a) ( 1.28, 1.28) (b) ( 1.645, 1.645) (c) ( 1.96, 1.96) (d) ( 0.90, 0.90) 5. IQ test scores are ormally distributed witha mea of 101 ad a stadard deviatio of 20. A idividual s IQ score is foud to be 111. Fid the z-score correspodig to this value. (a) (b) 2.00 (c) 0.5 (d) Suppose that prices of a certai model of ew homes are ormally distributed with a mea of $150,000 ad a stadard deviatio of $1100. Fid the percetage of buyers who paid betwee $148,900 ad $151,100. (a) 68% (b) 34% (c) 32% (d) 47.5% 7. A physical fitess associatio is icludig the mile ru i its secodary-school fitess test. The time for this evet for boys i secodary school is kow to possess a ormal distributio with a mea of 460 secods ad a stadard deviatio of 60 secods. The fitess associatio wats to recogize the fastest 10% of the boys with certificates of recogitio. What time would the boys eed to beat i order to ear a certificate of recogitio from the fitess associatio? (a) 537 secods (b) 383 secods (c) 361 secods (d) 559 secods

2 8. The amout of moey collected by the sack bar at a large uiversity has bee recorded daily for the past five years. Records idicate that the mea daily amout collected is $4650 ad the stadard deviatio is $400. The distributio is skewed to the right due to several high volume days (football game days). Suppose that 100 days were radomly selected from the five years ad the average amout collected from those days was recorded. Which of the followig describes the samplig distributio of the sample mea? (a) ormally distributed with a mea of $4650 ad a stadard deviatio of $400. (b) ormally distributed with a mea of $465 ad a stadard deviatio of $40. (c) skewed to the right with a mea of $4650 ad a stadard deviatio of $400. (d) ormally distributed with a mea of $4650 ad a stadard deviatio of $40. Part II 9. The amout of cor chips dispesed ito a 14-ouce bag by the dispesig machie has bee idetified as possessig a ormal distributio with a mea of 14.5 ouces ad a stadard deviatio of 0.3 ouces. Suppose 100 bags of chips were radomly selected from this dispesig machie. Fid the probability that the sample mea weight of these 100 bags exceeded 14.6 ouces. 10. A simple radom sample of size = 25 is draw from a populatio that is ormally distributed with populatio stadard deviatio σ = 17, ad the sample mea, x, is 123. Costruct a 94% cofidece iterval to estimate the mea.

3 11. A Gallup poll coducted December 20-21, 1999 asked Americas how may hours of TV they watch durig the week. How may subjects would be eeded i order to estimate the umber of hours of TV Americas watched withi 0.5 hours with 95% cofidece? Iitial survey results idicate that σ = Compute the mea ad stadard deviatio (to three decimal places) of the radom variable X whose probability distributio is x P (X = x) To play a certai gamblig game, a player wagers $2. The probability of wiig is 0.025, with a payoff of $50, a et gai of $48. The probability of losig is 0.975, for a loss of $2. Fid ad iterpret the expected value (to the player) of this game. 14. Depakote is a medicatio whose purpose is to reduce the pai associated with migraie headaches. I cliical trials ad exteded studies of Depakote, 2% of the patiets i the study experieced weight gai as a side effect. Let X, a biomial radom variable, be the umber of patiets who experiece weight gai as a side effect. (a) Compute the mea ad stadard deviatio of the radom variable X, the umber of patiets experiecig weight gai i 600 trials of the probability experimet. (b) Iterpret the mea.

4 (c) Would it be uusual to observe 16 patiets who experiece weight gai i a radom sample of 600 patiets who take the medicatio? Why? 15. Mark Price holds the record for percetage of free throws made i the atioal Basketball Associatio, at 90.4%. Assumig that free throws are idepedet evets, compute the probability that i his ext 10 free throws (a) Mark Price makes exactly 8. (b) Mark Price makes 8 or more. (c) Mark Price makes fewer tha 7. (d) Mark Price makes betwee 6 ad 8, iclusive. 16. A stadardized test has a mea score of µ = 1000 ad a stadard deviatio of σ = 150. Fid the test score that correspods to the 80 th percetile.

5 For each of the followig, determie whether a biomial radom variable is beig described. Circle yes if the variable is a biomial radom variable, ad circle o if it is ot. 17. Hak The Tak Wilso claims he ca eat more hot dogs tha ayoe else. To prove his claim, The Tak eats hot dogs util he gets sick. The umber of hot dogs he eats is recorded. YES O 18. For a particular airlie, the probability that a radomly selected flight will arrive o time is A radom sample of 100 flights is selected, ad the umber of o-time flights is recorded. (Assume idepedece) YES O 19. Accordig to ielso Media Research, 75% of all Uited States households have cable televisio. I a small tow of 40 households, a radom sample of 10 households is asked whether they have cable televisio. The umber of households with cable televisio is recorded. YES O 20. A persoel director wishes to estimate the mea scores for a proposed aptitude test that may be used i screeig applicats for clerical positios. Usig a sample of = 100 applicats, she foud x = 75.6 ad s = Costruct a 95% cofidece iterval estimate for the true mea. 21. A large casualty isurace compay is revisig its rate schedules. A staff actuary wishes to estimate the average size of claims resultig from fire damage i apartmet compleses havig betwee 10 ad 20 uits. The curret year s claim-settlemet experiece will be used as a sample. There were 19 claim settlemets for buildigs i this category. The average claim size was $73,249 with a stadard deviatio of $37,246. Costruct a 90% cofidece iterval estimate of the mea claim size. 22. The highway patrol director i a certai state has ordered a crackdow o druke drivers. To see if his safety campaig is workig, the director has ordered a samplig study to estimate the proportio of all fatal traffic accidets caused by drikig. I a radom sample of = 100 accidets, 42 percet were attributable to alcohol. Assumig that the accidet ppopulatio is large, costruct a 95% cofidece iterval for the populatio proportio of accidets caused by druk drivig.

6 Formula Page µ = x σ = (xi µ) 2 x = x s = (xi x) 2 1 µ = xi f i σ = (xi µ) 2 f i x = xi f i z = x µ σ s = (xi x) 2 f i 1 z = x x s P (A) + P (A) = 1 P (A B) = P (A ad B) P (B) P (A or B) = P (A) + P (B) P (A ad B) P (A ad B) = P (A) P (B A) E(X) = µ = x P (X = x) E(X) = µ = p σ 2 = (x µ) 2 P (X = x) C r =! r!( r)! σ 2 = pq P (X = x) = C x p x q ( x) σ x = σ σˆp = p q z = x µ σ x x ± z α/2 σ ( zα/2 σ = E ) 2 t = x µ s/ x ± t α/2 s z = ˆp p p q ˆp ± z α/2 = ˆp ˆq ˆp ˆq ( zα/2 ) 2 E

A point estimate is the value of a statistic that estimates the value of a parameter.

A point estimate is the value of a statistic that estimates the value of a parameter. Chapter 9 Estimatig the Value of a Parameter Chapter 9.1 Estimatig a Populatio Proportio Objective A : Poit Estimate A poit estimate is the value of a statistic that estimates the value of a parameter.

More information

Chapter 8: Estimation of Mean & Proportion. Introduction

Chapter 8: Estimation of Mean & Proportion. Introduction Chapter 8: Estimatio of Mea & Proportio 8.1 Estimatio, Poit Estimate, ad Iterval Estimate 8.2 Estimatio of a Populatio Mea: σ Kow 8.3 Estimatio of a Populatio Mea: σ Not Kow 8.4 Estimatio of a Populatio

More information

Estimating Proportions with Confidence

Estimating Proportions with Confidence Aoucemets: Discussio today is review for midterm, o credit. You may atted more tha oe discussio sectio. Brig sheets of otes ad calculator to midterm. We will provide Scatro form. Homework: (Due Wed Chapter

More information

BASIC STATISTICS ECOE 1323

BASIC STATISTICS ECOE 1323 BASIC STATISTICS ECOE 33 SPRING 007 FINAL EXAM NAME: ID NUMBER: INSTRUCTIONS:. Write your ame ad studet ID.. You have hours 3. This eam must be your ow work etirely. You caot talk to or share iformatio

More information

Introduction to Probability and Statistics Chapter 7

Introduction to Probability and Statistics Chapter 7 Itroductio to Probability ad Statistics Chapter 7 Ammar M. Sarha, asarha@mathstat.dal.ca Departmet of Mathematics ad Statistics, Dalhousie Uiversity Fall Semester 008 Chapter 7 Statistical Itervals Based

More information

SCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME

SCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME All Right Reserved No. of Pages - 10 No of Questios - 08 SCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME YEAR I SEMESTER I (Group B) END SEMESTER EXAMINATION

More information

Lecture 4: Probability (continued)

Lecture 4: Probability (continued) Lecture 4: Probability (cotiued) Desity Curves We ve defied probabilities for discrete variables (such as coi tossig). Probabilities for cotiuous or measuremet variables also are evaluated usig relative

More information

Lecture 5: Sampling Distribution

Lecture 5: Sampling Distribution Lecture 5: Samplig Distributio Readigs: Sectios 5.5, 5.6 Itroductio Parameter: describes populatio Statistic: describes the sample; samplig variability Samplig distributio of a statistic: A probability

More information

. (The calculated sample mean is symbolized by x.)

. (The calculated sample mean is symbolized by x.) Stat 40, sectio 5.4 The Cetral Limit Theorem otes by Tim Pilachowski If you have t doe it yet, go to the Stat 40 page ad dowload the hadout 5.4 supplemet Cetral Limit Theorem. The homework (both practice

More information

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Meas ad Proportios Itroductio: I this chapter we wat to fid out the value of a parameter for a populatio. We do t kow the value of this parameter for the etire

More information

Standard Deviations for Normal Sampling Distributions are: For proportions For means _

Standard Deviations for Normal Sampling Distributions are: For proportions For means _ Sectio 9.2 Cofidece Itervals for Proportios We will lear to use a sample to say somethig about the world at large. This process (statistical iferece) is based o our uderstadig of samplig models, ad will

More information

ii. Interval estimation:

ii. Interval estimation: 1 Types of estimatio: i. Poit estimatio: Example (1) Cosider the sample observatios 17,3,5,1,18,6,16,10 X 8 X i i1 8 17 3 5 118 6 16 10 8 116 8 14.5 14.5 is a poit estimate for usig the estimator X ad

More information

Inferential Statistics and Probability a Holistic Approach. Inference Process. Inference Process. Chapter 8 Slides. Maurice Geraghty,

Inferential Statistics and Probability a Holistic Approach. Inference Process. Inference Process. Chapter 8 Slides. Maurice Geraghty, Iferetial Statistics ad Probability a Holistic Approach Chapter 8 Poit Estimatio ad Cofidece Itervals This Course Material by Maurice Geraghty is licesed uder a Creative Commos Attributio-ShareAlike 4.0

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

Sampling Distributions and Estimation

Sampling Distributions and Estimation Samplig Distributios ad Estimatio T O P I C # Populatio Proportios, π π the proportio of the populatio havig some characteristic Sample proportio ( p ) provides a estimate of π : x p umber of successes

More information

Today: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3)

Today: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3) Today: Fiish Chapter 9 (Sectios 9.6 to 9.8 ad 9.9 Lesso 3) ANNOUNCEMENTS: Quiz #7 begis after class today, eds Moday at 3pm. Quiz #8 will begi ext Friday ad ed at 10am Moday (day of fial). There will be

More information

Confidence Intervals Introduction

Confidence Intervals Introduction Cofidece Itervals Itroductio A poit estimate provides o iformatio about the precisio ad reliability of estimatio. For example, the sample mea X is a poit estimate of the populatio mea μ but because of

More information

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Meas ad Proportios Itroductio: We wat to kow the value of a parameter for a populatio. We do t kow the value of this parameter for the etire populatio because

More information

Statistics for Economics & Business

Statistics for Economics & Business Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie

More information

1 Random Variables and Key Statistics

1 Random Variables and Key Statistics Review of Statistics 1 Radom Variables ad Key Statistics Radom Variable: A radom variable is a variable that takes o differet umerical values from a sample space determied by chace (probability distributio,

More information

Math 124: Lecture for Week 10 of 17

Math 124: Lecture for Week 10 of 17 What we will do toight 1 Lecture for of 17 David Meredith Departmet of Mathematics Sa Fracisco State Uiversity 2 3 4 April 8, 2008 5 6 II Take the midterm. At the ed aswer the followig questio: To be revealed

More information

CHAPTER 8 Estimating with Confidence

CHAPTER 8 Estimating with Confidence CHAPTER 8 Estimatig with Cofidece 8.2 Estimatig a Populatio Proportio The Practice of Statistics, 5th Editio Stares, Tabor, Yates, Moore Bedford Freema Worth Publishers Estimatig a Populatio Proportio

More information

Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1

Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1 Chapter 8 Cofidece Iterval Estimatio Copyright 2015, 2012, 2009 Pearso Educatio, Ic. Chapter 8, Slide 1 Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for

More information

ST 305: Exam 2 Fall 2014

ST 305: Exam 2 Fall 2014 ST 305: Exam Fall 014 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad

More information

B = A x z

B = A x z 114 Block 3 Erdeky == Begi 6.3 ============================================================== 1 / 8 / 2008 1 Correspodig Areas uder a ormal curve ad the stadard ormal curve are equal. Below: Area B = Area

More information

Basic formula for confidence intervals. Formulas for estimating population variance Normal Uniform Proportion

Basic formula for confidence intervals. Formulas for estimating population variance Normal Uniform Proportion Basic formula for the Chi-square test (Observed - Expected ) Expected Basic formula for cofidece itervals sˆ x ± Z ' Sample size adjustmet for fiite populatio (N * ) (N + - 1) Formulas for estimatig populatio

More information

point estimator a random variable (like P or X) whose values are used to estimate a population parameter

point estimator a random variable (like P or X) whose values are used to estimate a population parameter Estimatio We have oted that the pollig problem which attempts to estimate the proportio p of Successes i some populatio ad the measuremet problem which attempts to estimate the mea value µ of some quatity

More information

Sampling Distributions & Estimators

Sampling Distributions & Estimators API-209 TF Sessio 2 Teddy Svoroos September 18, 2015 Samplig Distributios & Estimators I. Estimators The Importace of Samplig Radomly Three Properties of Estimators 1. Ubiased 2. Cosistet 3. Efficiet I

More information

Topic-7. Large Sample Estimation

Topic-7. Large Sample Estimation Topic-7 Large Sample Estimatio TYPES OF INFERENCE Ò Estimatio: É Estimatig or predictig the value of the parameter É What is (are) the most likely values of m or p? Ò Hypothesis Testig: É Decidig about

More information

Chapter 10 - Lecture 2 The independent two sample t-test and. confidence interval

Chapter 10 - Lecture 2 The independent two sample t-test and. confidence interval Assumptios Idepedet Samples - ukow σ 1, σ - 30 or m 30 - Upooled case Idepedet Samples - ukow σ 1, σ - 30 or m 30 - Pooled case Idepedet samples - Pooled variace - Large samples Chapter 10 - Lecture The

More information

1. Suppose X is a variable that follows the normal distribution with known standard deviation σ = 0.3 but unknown mean µ.

1. Suppose X is a variable that follows the normal distribution with known standard deviation σ = 0.3 but unknown mean µ. Chapter 9 Exercises Suppose X is a variable that follows the ormal distributio with kow stadard deviatio σ = 03 but ukow mea µ (a) Costruct a 95% cofidece iterval for µ if a radom sample of = 6 observatios

More information

CHAPTER 8 CONFIDENCE INTERVALS

CHAPTER 8 CONFIDENCE INTERVALS CHAPTER 8 CONFIDENCE INTERVALS Cofidece Itervals is our first topic i iferetial statistics. I this chapter, we use sample data to estimate a ukow populatio parameter: either populatio mea (µ) or populatio

More information

Chapter 17 Sampling Distribution Models

Chapter 17 Sampling Distribution Models Chapter 17 Samplig Distributio Models 353 Chapter 17 Samplig Distributio Models 1. Sed moey. All of the histograms are cetered aroud p 0.05. As gets larger, the shape of the histograms get more uimodal

More information

Exam 2. Instructor: Cynthia Rudin TA: Dimitrios Bisias. October 25, 2011

Exam 2. Instructor: Cynthia Rudin TA: Dimitrios Bisias. October 25, 2011 15.075 Exam 2 Istructor: Cythia Rudi TA: Dimitrios Bisias October 25, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 You are i charge of a study

More information

Lecture 4: Parameter Estimation and Confidence Intervals. GENOME 560 Doug Fowler, GS

Lecture 4: Parameter Estimation and Confidence Intervals. GENOME 560 Doug Fowler, GS Lecture 4: Parameter Estimatio ad Cofidece Itervals GENOME 560 Doug Fowler, GS (dfowler@uw.edu) 1 Review: Probability Distributios Discrete: Biomial distributio Hypergeometric distributio Poisso distributio

More information

AY Term 2 Mock Examination

AY Term 2 Mock Examination AY 206-7 Term 2 Mock Examiatio Date / Start Time Course Group Istructor 24 March 207 / 2 PM to 3:00 PM QF302 Ivestmet ad Fiacial Data Aalysis G Christopher Tig INSTRUCTIONS TO STUDENTS. This mock examiatio

More information

A random variable is a variable whose value is a numerical outcome of a random phenomenon.

A random variable is a variable whose value is a numerical outcome of a random phenomenon. The Practice of Statistics, d ed ates, Moore, ad Stares Itroductio We are ofte more iterested i the umber of times a give outcome ca occur tha i the possible outcomes themselves For example, if we toss

More information

Elementary Statistics and Inference. Elementary Statistics and Inference. Chapter 20 Chance Errors in Sampling (cont.) 22S:025 or 7P:025.

Elementary Statistics and Inference. Elementary Statistics and Inference. Chapter 20 Chance Errors in Sampling (cont.) 22S:025 or 7P:025. Elemetary Statistics ad Iferece 22S:025 or 7P:025 Lecture 27 1 Elemetary Statistics ad Iferece 22S:025 or 7P:025 Chapter 20 2 D. The Correctio Factor - (page 367) 1992 Presidetial Campaig Texas 12.5 x

More information

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory Olie appedices from Couterparty Risk ad Credit Value Adjustmet a APPENDIX 8A: Formulas for EE, PFE ad EPE for a ormal distributio Cosider a ormal distributio with mea (expected future value) ad stadard

More information

The Idea of a Confidence Interval

The Idea of a Confidence Interval AP Statistics Ch. 8 Notes Estimatig with Cofidece I the last chapter, we aswered questios about what samples should look like assumig that we kew the true values of populatio parameters (like μ, σ, ad

More information

Exam 1 Spring 2015 Statistics for Applications 3/5/2015

Exam 1 Spring 2015 Statistics for Applications 3/5/2015 8.443 Exam Sprig 05 Statistics for Applicatios 3/5/05. Log Normal Distributio: A radom variable X follows a Logormal(θ, σ ) distributio if l(x) follows a Normal(θ, σ ) distributio. For the ormal radom

More information

Lecture 5 Point Es/mator and Sampling Distribu/on

Lecture 5 Point Es/mator and Sampling Distribu/on Lecture 5 Poit Es/mator ad Samplig Distribu/o Fall 03 Prof. Yao Xie, yao.xie@isye.gatech.edu H. Milto Stewart School of Idustrial Systems & Egieerig Georgia Tech Road map Poit Es/ma/o Cofidece Iterval

More information

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Busiess ad Ecoomics Chapter 8 Estimatio: Additioal Topics Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-1 8. Differece Betwee Two Meas: Idepedet Samples Populatio meas,

More information

Chpt 5. Discrete Probability Distributions. 5-3 Mean, Variance, Standard Deviation, and Expectation

Chpt 5. Discrete Probability Distributions. 5-3 Mean, Variance, Standard Deviation, and Expectation Chpt 5 Discrete Probability Distributios 5-3 Mea, Variace, Stadard Deviatio, ad Expectatio 1/23 Homework p252 Applyig the Cocepts Exercises p253 1-19 2/23 Objective Fid the mea, variace, stadard deviatio,

More information

Chapter 8 Interval Estimation. Estimation Concepts. General Form of a Confidence Interval

Chapter 8 Interval Estimation. Estimation Concepts. General Form of a Confidence Interval Chapter 8 Iterval Estimatio Estimatio Cocepts Usually ca't take a cesus, so we must make decisios based o sample data It imperative that we take the risk of samplig error ito accout whe we iterpret sample

More information

43. A 000 par value 5-year bod with 8.0% semiaual coupos was bought to yield 7.5% covertible semiaually. Determie the amout of premium amortized i the 6 th coupo paymet. (A).00 (B).08 (C).5 (D).5 (E).34

More information

Sampling Distributions and Estimation

Sampling Distributions and Estimation Cotets 40 Samplig Distributios ad Estimatio 40.1 Samplig Distributios 40. Iterval Estimatio for the Variace 13 Learig outcomes You will lear about the distributios which are created whe a populatio is

More information

The Valuation of the Catastrophe Equity Puts with Jump Risks

The Valuation of the Catastrophe Equity Puts with Jump Risks The Valuatio of the Catastrophe Equity Puts with Jump Risks Shih-Kuei Li Natioal Uiversity of Kaohsiug Joit work with Chia-Chie Chag Outlie Catastrophe Isurace Products Literatures ad Motivatios Jump Risk

More information

Twitter: @Owe134866 www.mathsfreeresourcelibrary.com Prior Kowledge Check 1) State whether each variable is qualitative or quatitative: a) Car colour Qualitative b) Miles travelled by a cyclist c) Favourite

More information

NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE)

NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE) NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE) READ THE INSTRUCTIONS VERY CAREFULLY 1) Time duratio is 2 hours

More information

REINSURANCE ALLOCATING RISK

REINSURANCE ALLOCATING RISK 6REINSURANCE Reisurace is a risk maagemet tool used by isurers to spread risk ad maage capital. The isurer trasfers some or all of a isurace risk to aother isurer. The isurer trasferrig the risk is called

More information

18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013

18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013 18.S096 Problem Set 5 Fall 2013 Volatility Modelig Due Date: 10/29/2013 1. Sample Estimators of Diffusio Process Volatility ad Drift Let {X t } be the price of a fiacial security that follows a geometric

More information

Introduction to Statistical Inference

Introduction to Statistical Inference Itroductio to Statistical Iferece Fial Review CH1: Picturig Distributios With Graphs 1. Types of Variable -Categorical -Quatitative 2. Represetatios of Distributios (a) Categorical -Pie Chart -Bar Graph

More information

Section 3.3 Exercises Part A Simplify the following. 1. (3m 2 ) 5 2. x 7 x 11

Section 3.3 Exercises Part A Simplify the following. 1. (3m 2 ) 5 2. x 7 x 11 123 Sectio 3.3 Exercises Part A Simplify the followig. 1. (3m 2 ) 5 2. x 7 x 11 3. f 12 4. t 8 t 5 f 5 5. 3-4 6. 3x 7 4x 7. 3z 5 12z 3 8. 17 0 9. (g 8 ) -2 10. 14d 3 21d 7 11. (2m 2 5 g 8 ) 7 12. 5x 2

More information

STRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans

STRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans CMM Subject Support Strad: FINANCE Uit 3 Loas ad Mortgages: Text m e p STRAND: FINANCE Uit 3 Loas ad Mortgages TEXT Cotets Sectio 3.1 Aual Percetage Rate (APR) 3.2 APR for Repaymet of Loas 3.3 Credit Purchases

More information

MATH : EXAM 2 REVIEW. A = P 1 + AP R ) ny

MATH : EXAM 2 REVIEW. A = P 1 + AP R ) ny MATH 1030-008: EXAM 2 REVIEW Origially, I was havig you all memorize the basic compoud iterest formula. I ow wat you to memorize the geeral compoud iterest formula. This formula, whe = 1, is the same as

More information

Non-Inferiority Logrank Tests

Non-Inferiority Logrank Tests Chapter 706 No-Iferiority Lograk Tests Itroductio This module computes the sample size ad power for o-iferiority tests uder the assumptio of proportioal hazards. Accrual time ad follow-up time are icluded

More information

BIOSTATS 540 Fall Estimation Page 1 of 72. Unit 6. Estimation. Use at least twelve observations in constructing a confidence interval

BIOSTATS 540 Fall Estimation Page 1 of 72. Unit 6. Estimation. Use at least twelve observations in constructing a confidence interval BIOSTATS 540 Fall 015 6. Estimatio Page 1 of 7 Uit 6. Estimatio Use at least twelve observatios i costructig a cofidece iterval - Gerald va Belle What is the mea of the blood pressures of all the studets

More information

NOTES ON ESTIMATION AND CONFIDENCE INTERVALS. 1. Estimation

NOTES ON ESTIMATION AND CONFIDENCE INTERVALS. 1. Estimation NOTES ON ESTIMATION AND CONFIDENCE INTERVALS MICHAEL N. KATEHAKIS 1. Estimatio Estimatio is a brach of statistics that deals with estimatig the values of parameters of a uderlyig distributio based o observed/empirical

More information

Calculation of the Annual Equivalent Rate (AER)

Calculation of the Annual Equivalent Rate (AER) Appedix to Code of Coduct for the Advertisig of Iterest Bearig Accouts. (31/1/0) Calculatio of the Aual Equivalet Rate (AER) a) The most geeral case of the calculatio is the rate of iterest which, if applied

More information

physicsandmathstutor.com

physicsandmathstutor.com physicsadmathstutor.com 9. (a) A geometric series has first term a ad commo ratio r. Prove that the sum of the first terms of the series is a(1 r ). 1 r (4) Mr. Kig will be paid a salary of 35 000 i the

More information

5 Statistical Inference

5 Statistical Inference 5 Statistical Iferece 5.1 Trasitio from Probability Theory to Statistical Iferece 1. We have ow more or less fiished the probability sectio of the course - we ow tur attetio to statistical iferece. I statistical

More information

Institute of Actuaries of India Subject CT5 General Insurance, Life and Health Contingencies

Institute of Actuaries of India Subject CT5 General Insurance, Life and Health Contingencies Istitute of Actuaries of Idia Subject CT5 Geeral Isurace, Life ad Health Cotigecies For 2017 Examiatios Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical techiques which

More information

CAPITAL ASSET PRICING MODEL

CAPITAL ASSET PRICING MODEL CAPITAL ASSET PRICING MODEL RETURN. Retur i respect of a observatio is give by the followig formula R = (P P 0 ) + D P 0 Where R = Retur from the ivestmet durig this period P 0 = Curret market price P

More information

Dr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory

Dr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory Dr Maddah ENMG 64 Fiacial Eg g I 03//06 Chapter 6 Mea-Variace Portfolio Theory Sigle Period Ivestmets Typically, i a ivestmet the iitial outlay of capital is kow but the retur is ucertai A sigle-period

More information

14.30 Introduction to Statistical Methods in Economics Spring 2009

14.30 Introduction to Statistical Methods in Economics Spring 2009 MIT OpeCourseWare http://ocwmitedu 430 Itroductio to Statistical Methods i Ecoomics Sprig 009 For iformatio about citig these materials or our Terms of Use, visit: http://ocwmitedu/terms 430 Itroductio

More information

1 The Power of Compounding

1 The Power of Compounding 1 The Power of Compoudig 1.1 Simple vs Compoud Iterest You deposit $1,000 i a bak that pays 5% iterest each year. At the ed of the year you will have eared $50. The bak seds you a check for $50 dollars.

More information

Using Math to Understand Our World Project 5 Building Up Savings And Debt

Using Math to Understand Our World Project 5 Building Up Savings And Debt Usig Math to Uderstad Our World Project 5 Buildig Up Savigs Ad Debt Note: You will have to had i aswers to all umbered questios i the Project Descriptio See the What to Had I sheet for additioal materials

More information

5. Best Unbiased Estimators

5. Best Unbiased Estimators Best Ubiased Estimators http://www.math.uah.edu/stat/poit/ubiased.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 5. Best Ubiased Estimators Basic Theory Cosider agai

More information

We learned: $100 cash today is preferred over $100 a year from now

We learned: $100 cash today is preferred over $100 a year from now Recap from Last Week Time Value of Moey We leared: $ cash today is preferred over $ a year from ow there is time value of moey i the form of willigess of baks, busiesses, ad people to pay iterest for its

More information

Chapter 10 Statistical Inference About Means and Proportions With Two Populations. Learning objectives

Chapter 10 Statistical Inference About Means and Proportions With Two Populations. Learning objectives Chater 0 Statistical Iferece About Meas ad Proortios With Two Poulatios Slide Learig objectives. Uderstad ifereces About the Differece Betwee Two Poulatio Meas: σ ad σ Kow. Uderstad Ifereces About the

More information

These characteristics are expressed in terms of statistical properties which are estimated from the sample data.

These characteristics are expressed in terms of statistical properties which are estimated from the sample data. 0. Key Statistical Measures of Data Four pricipal features which characterize a set of observatios o a radom variable are: (i) the cetral tedecy or the value aroud which all other values are buched, (ii)

More information

r i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i

r i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i The iformatio required by the mea-variace approach is substatial whe the umber of assets is large; there are mea values, variaces, ad )/2 covariaces - a total of 2 + )/2 parameters. Sigle-factor model:

More information

Estimating possible rate of injuries in coal mines

Estimating possible rate of injuries in coal mines A.G. MNUKHIN B.B. KOBYLANSKY Natioal Academy of Scieces of Ukraie Estimatig possible rate of ijuries i coal mies The article presets methods to calculate the values of ijury rates i mies. The authors demostrated

More information

0.1 Valuation Formula:

0.1 Valuation Formula: 0. Valuatio Formula: 0.. Case of Geeral Trees: q = er S S S 3 S q = er S S 4 S 5 S 4 q 3 = er S 3 S 6 S 7 S 6 Therefore, f (3) = e r [q 3 f (7) + ( q 3 ) f (6)] f () = e r [q f (5) + ( q ) f (4)] = f ()

More information

0.07. i PV Qa Q Q i n. Chapter 3, Section 2

0.07. i PV Qa Q Q i n. Chapter 3, Section 2 Chapter 3, Sectio 2 1. (S13HW) Calculate the preset value for a auity that pays 500 at the ed of each year for 20 years. You are give that the aual iterest rate is 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01

More information

Summary of Benefits RRD

Summary of Benefits RRD Summary of Beefits RRD All Eligible Employees Basic Term Life, Optioal Term Life, Optioal Depedet Term Life ad Optioal Accidetal Death & Dismembermet Issued by The Prudetial Isurace Compay of America Effective:

More information

Binomial Model. Stock Price Dynamics. The Key Idea Riskless Hedge

Binomial Model. Stock Price Dynamics. The Key Idea Riskless Hedge Biomial Model Stock Price Dyamics The value of a optio at maturity depeds o the price of the uderlyig stock at maturity. The value of the optio today depeds o the expected value of the optio at maturity

More information

DOWLING COLLEGE: School of Education Department of Educational Administration, Leadership, and Technology

DOWLING COLLEGE: School of Education Department of Educational Administration, Leadership, and Technology 1. Doe 2. Doe 3. Doe 4. Doe DOWLING COLLEGE: School of Educatio Departmet of Educatioal Admiistratio, Leadership, ad Techology 5. Calculate meas ad stadard deviatios for per capita icome ad total reveues

More information

CAPITAL PROJECT SCREENING AND SELECTION

CAPITAL PROJECT SCREENING AND SELECTION CAPITAL PROJECT SCREEIG AD SELECTIO Before studyig the three measures of ivestmet attractiveess, we will review a simple method that is commoly used to scree capital ivestmets. Oe of the primary cocers

More information

Summary of Benefits THE SCRIPPS RESEARCH INSTITUTE

Summary of Benefits THE SCRIPPS RESEARCH INSTITUTE Summary of Beefits THE SCRIPPS RESEARCH INSTITUTE All Active Full Time Beefit Eligible Employees Workig i Califoria Basic Term Life, Basic Accidetal Death & Dismembermet, Buy-Up Term Life, Buy-Up Depedet

More information

1031 Tax-Deferred Exchanges

1031 Tax-Deferred Exchanges 1031 Tax-Deferred Exchages About the Authors Arold M. Brow Seior Maagig Director, Head of 1031 Tax-Deferred Exchage Services, MB Fiacial Deferred Exchage Corporatio Arold M. Brow is the Seior Maagig Director

More information

MARYLAND ASSOCIATION OF BOARDS OF EDUCATION. Basic Term Life. Basic Accidental Death & Dismemberment

MARYLAND ASSOCIATION OF BOARDS OF EDUCATION. Basic Term Life. Basic Accidental Death & Dismemberment Summary of Beefits MARYLAND ASSOCIATION OF BOARDS OF EDUCATION All Somerset Couty Employees Basic Term Life, Basic Accidetal Death & Dismembermet, Optioal Term Life ad Depedet Term Life Issued by The Prudetial

More information

Just Lucky? A Statistical Test for Option Backdating

Just Lucky? A Statistical Test for Option Backdating Workig Paper arch 27, 2007 Just Lucky? A Statistical Test for Optio Backdatig Richard E. Goldberg James A. Read, Jr. The Brattle Group Abstract The literature i fiacial ecoomics provides covicig evidece

More information

guaranteed universal life express (gule)

guaranteed universal life express (gule) guarateed uiversal life express (gule) Guaratee tomorrow. Focus o today. Uited of Omaha Life Isurace Compay A Mutual of Omaha Compay product guide L7464_0314 For Producer use oly. Not for use with the

More information

ISBN Copyright 2015 The Continental Press, Inc.

ISBN Copyright 2015 The Continental Press, Inc. TABLE OF CONTENTS Itroductio 3 Format of Books 4 Suggestios for Use 7 Aotated Aswer Key ad Extesio Activities 9 Reproducible Tool Set 183 ISBN 978-0-8454-7897-4 Copyright 2015 The Cotietal Press, Ic. Exceptig

More information

Chapter 3. Compound interest

Chapter 3. Compound interest Chapter 3 Compoud iterest 1 Simple iterest ad compoud amout formula Formula for compoud amout iterest is: S P ( 1 Where : S: the amout at compoud iterest P: the pricipal i: the rate per coversio period

More information

Labour Force Survey in Belarus: determination of sample size, sample design, statistical weighting

Labour Force Survey in Belarus: determination of sample size, sample design, statistical weighting Labour Force urvey i Belarus: determiatio of sample size, sample desig, statistical weightig Natallia Boku Belarus tate Ecoomic Uiversity, e-mail: ataliaboku@rambler.ru Abstract The first experiece of

More information

Forecasting bad debt losses using clustering algorithms and Markov chains

Forecasting bad debt losses using clustering algorithms and Markov chains Forecastig bad debt losses usig clusterig algorithms ad Markov chais Robert J. Till Experia Ltd Lambert House Talbot Street Nottigham NG1 5HF {Robert.Till@uk.experia.com} Abstract Beig able to make accurate

More information

Indice Comit 30 Ground Rules. Intesa Sanpaolo Research Department December 2017

Indice Comit 30 Ground Rules. Intesa Sanpaolo Research Department December 2017 Idice Comit 30 Groud Rules Itesa Sapaolo Research Departmet December 2017 Comit 30 idex Characteristics of the Comit 30 idex 1) Securities icluded i the idices The basket used to calculate the Comit 30

More information

1 Estimating the uncertainty attached to a sample mean: s 2 vs.

1 Estimating the uncertainty attached to a sample mean: s 2 vs. Political Sciece 100a/200a Fall 2001 Cofidece itervals ad hypothesis testig, Part I 1 1 Estimatig the ucertaity attached to a sample mea: s 2 vs. σ 2 Recall the problem of descriptive iferece: We wat to

More information

Research Article The Probability That a Measurement Falls within a Range of n Standard Deviations from an Estimate of the Mean

Research Article The Probability That a Measurement Falls within a Range of n Standard Deviations from an Estimate of the Mean Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 70806, 8 pages doi:0.540/0/70806 Research Article The Probability That a Measuremet Falls withi a Rage of Stadard Deviatios

More information

2. Find the annual percentage yield (APY), to the nearest hundredth of a %, for an account with an APR of 12% with daily compounding.

2. Find the annual percentage yield (APY), to the nearest hundredth of a %, for an account with an APR of 12% with daily compounding. 1. Suppose that you ivest $4,000 i a accout that ears iterest at a of 5%, compouded mothly, for 58 years. `Show the formula that you would use to determie the accumulated balace, ad determie the accumulated

More information

Introduction to Econometrics (3 rd Updated Edition) Solutions to Odd- Numbered End- of- Chapter Exercises: Chapter 2

Introduction to Econometrics (3 rd Updated Edition) Solutions to Odd- Numbered End- of- Chapter Exercises: Chapter 2 Itroductio to Ecoometrics (3 rd Updated Editio) by James H. Stock ad Mark W. Watso Solutios to Odd- Numbered Ed- of- Chapter Exercises: Chapter 2 (This versio August 7, 204) Stock/Watso - Itroductio to

More information

Quantitative Analysis

Quantitative Analysis EduPristie www.edupristie.com Modellig Mea Variace Skewess Kurtosis Mea: X i = i Mode: Value that occurs most frequetly Media: Midpoit of data arraged i ascedig/ descedig order s Avg. of squared deviatios

More information

Subject CT5 Contingencies Core Technical. Syllabus. for the 2011 Examinations. The Faculty of Actuaries and Institute of Actuaries.

Subject CT5 Contingencies Core Technical. Syllabus. for the 2011 Examinations. The Faculty of Actuaries and Institute of Actuaries. Subject CT5 Cotigecies Core Techical Syllabus for the 2011 Examiatios 1 Jue 2010 The Faculty of Actuaries ad Istitute of Actuaries Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical

More information

MA Lesson 11 Section 1.3. Solving Applied Problems with Linear Equations of one Variable

MA Lesson 11 Section 1.3. Solving Applied Problems with Linear Equations of one Variable MA 15200 Lesso 11 Sectio 1. I Solvig Applied Problems with Liear Equatios of oe Variable 1. After readig the problem, let a variable represet the ukow (or oe of the ukows). Represet ay other ukow usig

More information

SOLUTION QUANTITATIVE TOOLS IN BUSINESS NOV 2011

SOLUTION QUANTITATIVE TOOLS IN BUSINESS NOV 2011 SOLUTION QUANTITATIVE TOOLS IN BUSINESS NOV 0 (i) Populatio: Collectio of all possible idividual uits (persos, objects, experimetal outcome whose characteristics are to be studied) Sample: A part of populatio

More information

Insurance and Production Function Xingze Wang, Ying Hsuan Lin, and Frederick Jao (2007)

Insurance and Production Function Xingze Wang, Ying Hsuan Lin, and Frederick Jao (2007) Isurace ad Productio Fuctio Xigze Wag, Yig Hsua Li, ad Frederick Jao (2007) 14.01 Priciples of Microecoomics, Fall 2007 Chia-Hui Che September 28, 2007 Lecture 10 Isurace ad Productio Fuctio Outlie 1.

More information

Unbiased estimators Estimators

Unbiased estimators Estimators 19 Ubiased estimators I Chapter 17 we saw that a dataset ca be modeled as a realizatio of a radom sample from a probability distributio ad that quatities of iterest correspod to features of the model distributio.

More information