Descriptive Statistics (60 points)
|
|
- Shavonne Collins
- 7 years ago
- Views:
Transcription
1 Economcs 30330: Statstcs for Economcs Problem Set 2 Unversty of otre Dame Instructor: Julo Garín Sprng 2012 Descrptve Statstcs (60 ponts) 1. Followng a recent government shutdown, Mnnesota Governor Mark Dayton proposes to gve all state employees a $500 rase. (a) What would ths do to the average monthly salary of state employees? To the SD? Explan. Suppose there are n state employees n Mnnesota. Let x denote the wage for employee before the rase, and let y denote the wage after the rase. Then we have y x Therefore, µ y y (x + 500) x x µ x Therefore, the mean ncreases by $500. In other words, f employees across the entre salary dstrbuton receve the same rase, ths s smply a lnear transformaton of the mean, and the entre dstrbuton wll shft up by $500. Smlarly, for the varance: σ y (y µ y ) 2 [(x + 500) (µ x + 500)] 2 [(x + 500) (µ x + 500)] 2 (x µ x ) 2 σ x Hence, the standard devaton does not change. If employees across the entre salary dstrbuton receve the same rase, the dstrbuton tself wll shft up but wll not change shape. 1
2 (b) What would a 5 percent ncrease n salares of all state employees do to the average monthly salary? To the SD? Explan. y 1.05x Hence, y µ y (1.05x ) µ x x Smlarly, (y µ y ) 2 (1.05x 1.05µ x ) 2 [1.05(x µ x )] (x µ x ) (x µ x ) σ x Therefore, both the mean and standard devaton ncrease by 5%. A 5% ncrease n salares of all employees wth both change the shape of and shft the salary dstrbuton. Ths s because even though the percentage ncrease n salares s the same for all employees, the level salary change s not (e.g., an employee wth a $30,000 salary wll receve a $1500 rase whle an employee wth a $0,000 salary wll receve a $2000 rase). 2. The Assocated Press Team Marketng Report lsted the Dallas Cowboys at the team wth the hghest tcket prces n the atonal Football League (USA Today, October 20, 2009). Data showng the average tcket prce for a sample of 1 teams n the FL are as follows. Team Tcket Prce z -score Team Tcket Prce z -score ($) ($) Atlanta Falcons Green Bay Packers Buffalo Blls Indanapols Colts Calforna Panthers ew Orleans Sants Chcago Bears ew York Jets Cleveland Browns Pttsburgh Steelers Dallas Cowboys Seattle Seahawks Denver Broncos Tennessee Ttans
3 (a) Compute the range, nterquartle range, and the medan tcket prce. Max: $160 (Dallas Cowboys); Mn: $51 (Buffalo Blls): Range $160 - $51 $109 Q 1 : $61 (Tennessee Ttans); Q 3 : $83 (Indanapols Colts): IQR $83 - $61 $22 7th poston: Green Bay Packers ($63); 8th poston Pttsburgh Steelers ($67): Medan 65 2 (b) Compute the mean tcket prce. The prevous year, the mean tcket prce was $ What was the percentage ncrease n the mean tcket prce for the one-year perod? x x 1050 n 1 75 Percentage ncrease: Hence, tcket prces ncreased by 3.88%. (c) Compute the sample varance and sample standard devaton. Interpret. s 2 (x x) s s The tcket prce of each team s, n average, $27.0 away from the mean of the sample. (d) Compute the standardzed values (z-scores) for the lsted tcket prces n the table above. Should the Dallas Cowboys tcket prce be consdered an outler? Explan. The z-score for the Cowboys tcket prce s 3.1. Snce ths s greater than 3 standard devatons from the mean, t would be consdered an outler. (e) What are the mean and standard devaton of the standardzed tcket prces? Explan. The mean of the standardzed tcket prces s zero and the standard devaton s one. Ths s because the mean of any standardzed values (.e, z-scores) s always zero and the standard devaton s always one. 3. The flashlght batteres produced by a Uruguayan manufacturer are known to have an average lfe of 60 hours wth a standard devaton of hours. Use Chebyshev s theorem to answer parts (a) through (c). (a) At least what percentage of batteres wll have a lfe of 5 to 66 hours? x 60 and s [5, 66] 60 ± x ± 1.5s By Chebyshev s theorem, at least 55.55% of batteres are wthn 1.5 standard devatons of the mean. (b) At least what percentage of the batteres wll have a lfe of 52 to 68 hours? [52, 68] 60 ± 8 x ± 2s
4 By Chebyshev s theorem, at least 75% of batteres are wthn 2 standard devatons of the mean. (c) Determne an nterval for the battery lves that wll be true for at least 80% of the batteres. 1 1 z z 5 x ± 5 60 ± 5 [51.06, 68.9] (d) Suppose we know that the battery lves have a normal dstrbuton. Approxmately what percentage of batteres wll have a lfe of 50 to 70 hours? Why? [50, 70] 60 ± 2.5s Snce 2.5s>2s, by the Emprcal Rule, more than 95% of values wll have a lfe of 50 to 70 hours. (e) Explan Chebyshev s Theorem n your own words. One example: Chebyshev s Theorem tells us that n any dstrbuton of data, almost all values wll be close to the mean.. The followng s the frequency dstrbuton for the speeds of a sample of otre Dame students drvng from South Bend to Chcago. Speed (MPH) f M f M (M x) 2 f (M x) (a) Complete the table. (b) Compute the sample mean, varance, and standard devaton of the grouped sample. x f M 235 n s 2 f (M x) s s (c) What does M represent and why do we use t? M denotes the mdpont of each class. We use t because we do not know the ndvdual values for all observatons and therefore cannot calculate the mean values.
5 5. The followng table lsts the study tme and exam scores for a sample of 5 students n a college statstcs class. Score Mnutes Spent Studyng Hours Spent Studyng (a) Calculate the sample covarance between test score and mnutes spent studyng. Let x denote score and y denote mnutes spent studyng. s xy (x x) (y ȳ) (b) Calculate the sample covarance between test score and hours spent studyng. Let x denote score and z denote hours spent studyng. s xy (x x) (y ȳ) (c) Calculate the correlaton coeffcent between test score and mnutes spent studyng. s x (x x) s y (y ȳ) 2 r xy s xy s x s y (12.9)(80.5) (d) Calculate the correlaton coeffcent between test score and hours spent studyng. s x (x x) s z (y z) 2 r xz s xy s x s y (12.9)(1.3) (e) Why dd we calculate two dfferent measures to descrbe the relatonshp between tme spent studyng and test scores? Is one measure more useful than the other? Explan. Covarance depends on the unt of measurement, so our values for the covarance between tme spent studyng and test scores dffered dependng on whether we used hours or mnutes to measure tme. We calculated the correlaton coeffcent because ths measure of the relatonshp between two varables does not depend on unts of measurement. Snce our nterest s n the relatonshp between tme spent studyng and test scores, regardless of how we measure tme, the correlaton coeffcent s a better measure of the relatonshp between these two varables. 5
6 6. You wll need Excel, Stata, or another statstcal programmng software to complete ths part of the assgnment. Please calculate the answers n the software of your choce and report them here. You do not need to turn n your actual Excel spreadsheet, code, etc. BA teams are ncreasngly usng statstcs to nform ther decsons on the court. One example s the analyss of bad players. Davd Berr defnes a bad player as one that has half the Wns Produced per 8 mnutes (WP8) value of a good player, or a WP8 of or less; an average player has a WP8 of The dataset named BA contans data on bad players for BA teams n (a) Are we workng wth a sample or a populaton here? Justfy your answer. Snce our data nclude all teams n the BA, we are workng wth the populaton of BA teams. (b) Calculate and nterpret the followng measures for Bad Players: mode, medan, mean, and standard devaton. Mode: 8 Mean: 8.63 Medan: 8 Standard Devaton: 2.63 (c) Comment on the skewness of the data for Bad Players based on the measures you calculated n (a) (note: you do not actually need to calculate the skewness coeffcent). What does the skewness tell us about the dstrbuton of bad players? The mean s greater than the medan, so the data are slghtly skewed rght. (d) Create a scatterplot of Total Bad Player Mnutes and Total Wns (prnt and attach a copy of the scatterplot to your assgnment; be sure to label the axes). Based on the plot, what do you expect the drecton and approxmate magntude of the correlaton between these two varables to be? Explan. The plot shows a negatve relatonshp between Total Bad Player Mnutes and Total Wns, but the values are more dspersed as the number of Total Wns ncreases. We should therefore expect that the correlaton wll be about n the mddle of 0 and -1. 1,000 BA Total Bad Player Mnutes and Total Wns 12,000 10,000 Total Bad Player Mnutes 8,000 6,000,000 2, Total Wns (e) Calculate the covarance and correlaton between Bad Player Mnutes and Total Wns. What do you expect would happen to the covarance f you nstead calculated Bad Player Hours nstead of Mnutes? Covarance: -23,911 Correlaton: If we calculated the covarance of Bad Player Hours nstead of Mnutes, the covarance should decrease. 6
Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
More information1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationSIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
More informationQuestion 2: What is the variance and standard deviation of a dataset?
Queston 2: What s the varance and standard devaton of a dataset? The varance of the data uses all of the data to compute a measure of the spread n the data. The varance may be computed for a sample of
More informationHow To Calculate The Accountng Perod Of Nequalty
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems
More informationCHAPTER 14 MORE ABOUT REGRESSION
CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp
More informationStress test for measuring insurance risks in non-life insurance
PROMEMORIA Datum June 01 Fnansnspektonen Författare Bengt von Bahr, Younes Elonq and Erk Elvers Stress test for measurng nsurance rsks n non-lfe nsurance Summary Ths memo descrbes stress testng of nsurance
More informationTHE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
More informationCHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang
More informationPRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB.
PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. INDEX 1. Load data usng the Edtor wndow and m-fle 2. Learnng to save results from the Edtor wndow. 3. Computng the Sharpe Rato 4. Obtanng the Treynor Rato
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More information7.5. Present Value of an Annuity. Investigate
7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on
More informationAnswer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationPSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12
14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationPortfolio Loss Distribution
Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment
More informationMarginal Returns to Education For Teachers
The Onlne Journal of New Horzons n Educaton Volume 4, Issue 3 MargnalReturnstoEducatonForTeachers RamleeIsmal,MarnahAwang ABSTRACT FacultyofManagementand Economcs UnverstPenddkanSultan Idrs ramlee@fpe.ups.edu.my
More informationUnderstanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment
A research and educaton ntatve at the MT Sloan School of Management Understandng the mpact of Marketng Actons n Tradtonal Channels on the nternet: Evdence from a Large Scale Feld Experment Paper 216 Erc
More informationThe Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading
The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn & Ln Wen Arzona State Unversty Introducton Electronc Brokerage n Foregn Exchange Start from a base of zero n 1992
More informationProduction. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.
Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s non-empty If Y s empty, we have nothng to talk about 2. Y s closed A set
More informationNPAR TESTS. One-Sample Chi-Square Test. Cell Specification. Observed Frequencies 1O i 6. Expected Frequencies 1EXP i 6
PAR TESTS If a WEIGHT varable s specfed, t s used to replcate a case as many tmes as ndcated by the weght value rounded to the nearest nteger. If the workspace requrements are exceeded and samplng has
More informationSTATISTICAL DATA ANALYSIS IN EXCEL
Mcroarray Center STATISTICAL DATA ANALYSIS IN EXCEL Lecture 6 Some Advanced Topcs Dr. Petr Nazarov 14-01-013 petr.nazarov@crp-sante.lu Statstcal data analyss n Ecel. 6. Some advanced topcs Correcton for
More informationHedging Interest-Rate Risk with Duration
FIXED-INCOME SECURITIES Chapter 5 Hedgng Interest-Rate Rsk wth Duraton Outlne Prcng and Hedgng Prcng certan cash-flows Interest rate rsk Hedgng prncples Duraton-Based Hedgng Technques Defnton of duraton
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,
More informationTraffic-light a stress test for life insurance provisions
MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax
More informationOnline Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses
Onlne Appendx Supplemental Materal for Market Mcrostructure Invarance: Emprcal Hypotheses Albert S. Kyle Unversty of Maryland akyle@rhsmth.umd.edu Anna A. Obzhaeva New Economc School aobzhaeva@nes.ru Table
More informationExhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation
Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The
More informationThe Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading
The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn Arzona State Unversty & Ln Wen Unversty of Redlands MARKET PARTICIPANTS: Customers End-users Multnatonal frms Central
More informationThe impact of hard discount control mechanism on the discount volatility of UK closed-end funds
Investment Management and Fnancal Innovatons, Volume 10, Issue 3, 2013 Ahmed F. Salhn (Egypt) The mpact of hard dscount control mechansm on the dscount volatlty of UK closed-end funds Abstract The mpact
More informationthe Manual on the global data processing and forecasting system (GDPFS) (WMO-No.485; available at http://www.wmo.int/pages/prog/www/manuals.
Gudelne on the exchange and use of EPS verfcaton results Update date: 30 November 202. Introducton World Meteorologcal Organzaton (WMO) CBS-XIII (2005) recommended that the general responsbltes for a Lead
More informationProceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001
Proceedngs of the Annual Meetng of the Amercan Statstcal Assocaton, August 5-9, 2001 LIST-ASSISTED SAMPLING: THE EFFECT OF TELEPHONE SYSTEM CHANGES ON DESIGN 1 Clyde Tucker, Bureau of Labor Statstcs James
More informationEvaluating the Effects of FUNDEF on Wages and Test Scores in Brazil *
Evaluatng the Effects of FUNDEF on Wages and Test Scores n Brazl * Naérco Menezes-Flho Elane Pazello Unversty of São Paulo Abstract In ths paper we nvestgate the effects of the 1998 reform n the fundng
More informationStaff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall
SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent
More informationProblem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative.
Queston roblem Set 3 a) We are asked how people wll react, f the nterest rate on bonds s negatve. When
More information1 De nitions and Censoring
De ntons and Censorng. Survval Analyss We begn by consderng smple analyses but we wll lead up to and take a look at regresson on explanatory factors., as n lnear regresson part A. The mportant d erence
More informationInterest Rate Futures
Interest Rate Futures Chapter 6 6.1 Day Count Conventons n the U.S. (Page 129) Treasury Bonds: Corporate Bonds: Money Market Instruments: Actual/Actual (n perod) 30/360 Actual/360 The day count conventon
More informationCalibration and Linear Regression Analysis: A Self-Guided Tutorial
Calbraton and Lnear Regresson Analyss: A Self-Guded Tutoral Part The Calbraton Curve, Correlaton Coeffcent and Confdence Lmts CHM314 Instrumental Analyss Department of Chemstry, Unversty of Toronto Dr.
More informationTexas Instruments 30X IIS Calculator
Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the
More informationStatistical Methods to Develop Rating Models
Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and
More information14.74 Lecture 5: Health (2)
14.74 Lecture 5: Health (2) Esther Duflo February 17, 2004 1 Possble Interventons Last tme we dscussed possble nterventons. Let s take one: provdng ron supplements to people, for example. From the data,
More informationUsing Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More informationLinear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits
Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.
More informationThe Effects of Tax Rate Changes on Tax Bases and the Marginal Cost of Public Funds for Canadian Provincial Governments
The Effects of Tax Rate Changes on Tax Bases and the Margnal Cost of Publc Funds for Canadan Provncal Governments Bev Dahlby a and Ergete Ferede b a Department of Economcs, Unversty of Alberta, Edmonton,
More informationAn Analysis of the relationship between WTI term structure and oil market fundamentals in 2002-2009
MPRA Munch Personal RePEc Archve An Analyss of the relatonshp between WTI term structure and ol market fundamentals n 00-009 Mleno Cavalcante Petrobras S.A., Unversdade de Fortaleza. August 00 Onlne at
More informationTwo Faces of Intra-Industry Information Transfers: Evidence from Management Earnings and Revenue Forecasts
Two Faces of Intra-Industry Informaton Transfers: Evdence from Management Earnngs and Revenue Forecasts Yongtae Km Leavey School of Busness Santa Clara Unversty Santa Clara, CA 95053-0380 TEL: (408) 554-4667,
More information2011 AMERICAN CONFERENCE. East Division W L T Pct. Pts. OP New England# 13 3 0.813 513 342. Washington 5 11 0.313 288 367 North Division
2011 New England# 13 3 0.813 513 342 New York Giants 9 7 0.563 394 400 New York Jets 8 8 0.500 377 363 Philadelphia 8 8 0.500 396 328 Miami 6 10 0.375 329 313 Dallas 8 8 0.500 369 347 Buffalo 6 10 0.375
More informationKiel Institute for World Economics Duesternbrooker Weg 120 24105 Kiel (Germany) Kiel Working Paper No. 1120
Kel Insttute for World Economcs Duesternbrooker Weg 45 Kel (Germany) Kel Workng Paper No. Path Dependences n enture Captal Markets by Andrea Schertler July The responsblty for the contents of the workng
More informationAnalysis of Premium Liabilities for Australian Lines of Business
Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton
More informationBinomial Link Functions. Lori Murray, Phil Munz
Bnomal Lnk Functons Lor Murray, Phl Munz Bnomal Lnk Functons Logt Lnk functon: ( p) p ln 1 p Probt Lnk functon: ( p) 1 ( p) Complentary Log Log functon: ( p) ln( ln(1 p)) Motvatng Example A researcher
More informationMethod for assessment of companies' credit rating (AJPES S.BON model) Short description of the methodology
Method for assessment of companes' credt ratng (AJPES S.BON model) Short descrpton of the methodology Ljubljana, May 2011 ABSTRACT Assessng Slovenan companes' credt ratng scores usng the AJPES S.BON model
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationInternational University of Japan Public Management & Policy Analysis Program
Internatonal Unversty of Japan Publc Management & Polcy Analyss Program Practcal Gudes To Panel Data Modelng: A Step by Step Analyss Usng Stata * Hun Myoung Park, Ph.D. kucc65@uj.ac.jp 1. Introducton.
More information1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationTypes of Injuries. (20 minutes) LEARNING OBJECTIVES MATERIALS NEEDED
U N I T 3 Types of Injures (20 mnutes) PURPOSE: To help coaches learn how to recognze the man types of acute and chronc njures. LEARNING OBJECTIVES In ths unt, coaches wll learn how most njures occur,
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationTrade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity
Trade Adjustment Productvty n Large Crses Gta Gopnath Department of Economcs Harvard Unversty NBER Brent Neman Booth School of Busness Unversty of Chcago NBER Onlne Appendx May 2013 Appendx A: Dervaton
More informationForecasting and Stress Testing Credit Card Default using Dynamic Models
Forecastng and Stress Testng Credt Card Default usng Dynamc Models Tony Bellott and Jonathan Crook Credt Research Centre Unversty of Ednburgh Busness School Verson 4.5 Abstract Typcally models of credt
More informationRotation Kinematics, Moment of Inertia, and Torque
Rotaton Knematcs, Moment of Inerta, and Torque Mathematcally, rotaton of a rgd body about a fxed axs s analogous to a lnear moton n one dmenson. Although the physcal quanttes nvolved n rotaton are qute
More informationThis study examines whether the framing mode (narrow versus broad) influences the stock investment decisions
MANAGEMENT SCIENCE Vol. 54, No. 6, June 2008, pp. 1052 1064 ssn 0025-1909 essn 1526-5501 08 5406 1052 nforms do 10.1287/mnsc.1070.0845 2008 INFORMS How Do Decson Frames Influence the Stock Investment Choces
More informationLabor Supply. Where we re going:
Labor Supply Where we re gong: I m gong to spend about 4 lectures talkng about labor supply. Along the way, I m gong to ntroduce some econometrc ssues and tools that we commonly use. Today s lecture and
More informationTraffic-light extended with stress test for insurance and expense risks in life insurance
PROMEMORIA Datum 0 July 007 FI Dnr 07-1171-30 Fnansnspetonen Författare Bengt von Bahr, Göran Ronge Traffc-lght extended wth stress test for nsurance and expense rss n lfe nsurance Summary Ths memorandum
More informationRate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process
Dsadvantages of cyclc TDDB47 Real Tme Systems Manual scheduler constructon Cannot deal wth any runtme changes What happens f we add a task to the set? Real-Tme Systems Laboratory Department of Computer
More informationArizona Cardinals Yellow
Arizona Cardinals Cardinal Red,, Blue 1996 through 2001 / 2000 Season 194 C Arizona Cardinals Red "Cardinal Red" Secondary Colors: 280 C Additional Colors: Black 6 C 116 C Additional Colors 2: 201 C 159
More informationAn Empirical Study of Search Engine Advertising Effectiveness
An Emprcal Study of Search Engne Advertsng Effectveness Sanjog Msra, Smon School of Busness Unversty of Rochester Edeal Pnker, Smon School of Busness Unversty of Rochester Alan Rmm-Kaufman, Rmm-Kaufman
More informationSimple Interest Loans (Section 5.1) :
Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationProperties of Indoor Received Signal Strength for WLAN Location Fingerprinting
Propertes of Indoor Receved Sgnal Strength for WLAN Locaton Fngerprntng Kamol Kaemarungs and Prashant Krshnamurthy Telecommuncatons Program, School of Informaton Scences, Unversty of Pttsburgh E-mal: kakst2,prashk@ptt.edu
More informationLatent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006
Latent Class Regresson Statstcs for Psychosocal Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson (LCR) What s t and when do we use t? Recall the standard latent class model
More informationAn Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services
An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao
More informationStudent Performance in Online Quizzes as a Function of Time in Undergraduate Financial Management Courses
Student Performance n Onlne Quzzes as a Functon of Tme n Undergraduate Fnancal Management Courses Olver Schnusenberg The Unversty of North Florda ABSTRACT An nterestng research queston n lght of recent
More informationTime Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money
Ch. 6 - The Tme Value of Money Tme Value of Money The Interest Rate Smple Interest Compound Interest Amortzng a Loan FIN21- Ahmed Y, Dasht TIME VALUE OF MONEY OR DISCOUNTED CASH FLOW ANALYSIS Very Important
More information+ + + - - This circuit than can be reduced to a planar circuit
MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to
More informationMacro Factors and Volatility of Treasury Bond Returns
Macro Factors and Volatlty of Treasury Bond Returns Jngzh Huang Department of Fnance Smeal Colleage of Busness Pennsylvana State Unversty Unversty Park, PA 16802, U.S.A. Le Lu School of Fnance Shangha
More informationADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET *
ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER * We are grateful to Jeffrey Brown, Perre-Andre
More informationSection 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
More informationQuantization Effects in Digital Filters
Quantzaton Effects n Dgtal Flters Dstrbuton of Truncaton Errors In two's complement representaton an exact number would have nfntely many bts (n general). When we lmt the number of bts to some fnte value
More informationWORKING PAPER. C.D. Howe Institute. The Effects of Tax Rate Changes on Tax Bases and the Marginal Cost of Public Funds for Provincial Governments
MARCH 211 C.D. Howe Insttute WORKING PAPER FISCAL AND TAX COMPETITIVENESS The Effects of Tax Rate Changes on Tax Bases and the Margnal Cost of Publc Funds for Provncal Governments Bev Dahlby Ergete Ferede
More informationTransaction Costs and Strategic Trading of German Investment Management Firms: Empirical Evidence from European Stock Markets
Transacton Costs and Strategc Tradng of German Investment Management Frms: Emprcal Evdence from European Stock Markets Lutz Johannng* Endowed Char for Asset Management European Busness School Schloß Rechartshausen
More informationScale Dependence of Overconfidence in Stock Market Volatility Forecasts
Scale Dependence of Overconfdence n Stoc Maret Volatlty Forecasts Marus Glaser, Thomas Langer, Jens Reynders, Martn Weber* June 7, 007 Abstract In ths study, we analyze whether volatlty forecasts (judgmental
More informationMortgage Default and Prepayment Risks among Moderate and Low Income Households. Roberto G. Quercia. University of North Carolina at Chapel Hill
Mortgage Default and Prepayment Rsks among Moderate and Low Income Households Roberto G. Querca Unversty of North Carolna at Chapel Hll querca@emal.unc.edu Anthony Pennngton-Cross Marquette Unversty anthony.pennngton-cross@marquette.edu
More informationwhere the coordinates are related to those in the old frame as follows.
Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product
More informationSection 5.3 Annuities, Future Value, and Sinking Funds
Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme
More informationSurvival analysis methods in Insurance Applications in car insurance contracts
Survval analyss methods n Insurance Applcatons n car nsurance contracts Abder OULIDI 1 Jean-Mare MARION 2 Hervé GANACHAUD 3 Abstract In ths wor, we are nterested n survval models and ther applcatons on
More informationESTIMATING THE MARKET VALUE OF FRANKING CREDITS: EMPIRICAL EVIDENCE FROM AUSTRALIA
ESTIMATING THE MARKET VALUE OF FRANKING CREDITS: EMPIRICAL EVIDENCE FROM AUSTRALIA Duc Vo Beauden Gellard Stefan Mero Economc Regulaton Authorty 469 Wellngton Street, Perth, WA 6000, Australa Phone: (08)
More informationHOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA*
HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* Luísa Farnha** 1. INTRODUCTION The rapd growth n Portuguese households ndebtedness n the past few years ncreased the concerns that debt
More informationGender differences in revealed risk taking: evidence from mutual fund investors
Economcs Letters 76 (2002) 151 158 www.elsever.com/ locate/ econbase Gender dfferences n revealed rsk takng: evdence from mutual fund nvestors a b c, * Peggy D. Dwyer, James H. Glkeson, John A. Lst a Unversty
More informationBrigid Mullany, Ph.D University of North Carolina, Charlotte
Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte
More informationTransition Matrix Models of Consumer Credit Ratings
Transton Matrx Models of Consumer Credt Ratngs Abstract Although the corporate credt rsk lterature has many studes modellng the change n the credt rsk of corporate bonds over tme, there s far less analyss
More informationA statistical approach to determine Microbiologically Influenced Corrosion (MIC) Rates of underground gas pipelines.
A statstcal approach to determne Mcrobologcally Influenced Corroson (MIC) Rates of underground gas ppelnes. by Lech A. Grzelak A thess submtted to the Delft Unversty of Technology n conformty wth the requrements
More informationEDUCATION AND RELIGION
DUCATION AND RLIGION by dward L. Glaeser Harvard Unversty and NR and ruce I. Sacerdote 1 Dartmouth College and NR February 14, 2002 Abstract In the Unted States, relgous attendance rses sharply wth educaton
More informationWhen Talk is Free : The Effect of Tariff Structure on Usage under Two- and Three-Part Tariffs
0 When Talk s Free : The Effect of Tarff Structure on Usage under Two- and Three-Part Tarffs Eva Ascarza Ana Lambrecht Naufel Vlcassm July 2012 (Forthcomng at Journal of Marketng Research) Eva Ascarza
More informationFixed income risk attribution
5 Fxed ncome rsk attrbuton Chthra Krshnamurth RskMetrcs Group chthra.krshnamurth@rskmetrcs.com We compare the rsk of the actve portfolo wth that of the benchmark and segment the dfference between the two
More informationHousing Liquidity, Mobility and the Labour Market
Housng Lqudty, Moblty and the Labour Market Allen Head Huw Lloyd-Ells January 29, 2009 Abstract The relatonshps among geographcal moblty, unemployment and the value of owner-occuped housng are studed n
More informationUnderwriting Risk. Glenn Meyers. Insurance Services Office, Inc.
Underwrtng Rsk By Glenn Meyers Insurance Servces Offce, Inc. Abstract In a compettve nsurance market, nsurers have lmted nfluence on the premum charged for an nsurance contract. hey must decde whether
More informationInternational Commodity Prices and the Australian Stock Market
Internatonal Commodty Prces and the Australan Stock Market Chrs Heaton, George Mlunovch and Anthony Passé-de Slva Abstract We propose a method for estmatng the earlest tme durng the tradng day when overnght
More information