Multiple Choice. Choose the one that best completes the statement or answers the question.

Similar documents
j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

PROBABILITY PRIMER. Exercise Solutions

Lecture 3: Probability Distributions

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y)

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

/ n ) are compared. The logic is: if the two

CS-433: Simulation and Modeling Modeling and Probability Review

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

Composite Hypotheses testing

Expected Value and Variance

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours

Methods in Epidemiology. Medical statistics 02/11/2014

Probability and Random Variable Primer

Limited Dependent Variables

Lecture 6: Introduction to Linear Regression

Cathy Walker March 5, 2010

Lecture 3 Stat102, Spring 2007

princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 3: Large deviations bounds and applications Lecturer: Sanjeev Arora

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

ANSWERS CHAPTER 9. TIO 9.2: If the values are the same, the difference is 0, therefore the null hypothesis cannot be rejected.

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

ISQS 6348 Final Open notes, no books. Points out of 100 in parentheses. Y 1 ε 2

Statistics and Quantitative Analysis U4320. Segment 3: Probability Prof. Sharyn O Halloran

Basically, if you have a dummy dependent variable you will be estimating a probability.

Economics 130. Lecture 4 Simple Linear Regression Continued

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Chapter 9: Statistical Inference and the Relationship between Two Variables

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

Chapter 3 Describing Data Using Numerical Measures

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

Goodness of fit and Wilks theorem

Stat 642, Lecture notes for 01/27/ d i = 1 t. n i t nj. n j

Statistics and Probability Theory in Civil, Surveying and Environmental Engineering

Chapter 11: Simple Linear Regression and Correlation

Chapter 1. Probability

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

On mutual information estimation for mixed-pair random variables

e i is a random error

EGR 544 Communication Theory

Chapter 5 Multilevel Models

As is less than , there is insufficient evidence to reject H 0 at the 5% level. The data may be modelled by Po(2).

First Year Examination Department of Statistics, University of Florida

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

Engineering Risk Benefit Analysis

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)

Introduction to Regression

U-Pb Geochronology Practical: Background

PhysicsAndMathsTutor.com

Linear Approximation with Regularization and Moving Least Squares

Linear Regression Analysis: Terminology and Notation

Convergence of random processes

Negative Binomial Regression

ST2352. Working backwards with conditional probability. ST2352 Week 8 1

Biostatistics. Chapter 11 Simple Linear Correlation and Regression. Jing Li

MIMA Group. Chapter 2 Bayesian Decision Theory. School of Computer Science and Technology, Shandong University. Xin-Shun SDU

Marginal Effects in Probit Models: Interpretation and Testing. 1. Interpreting Probit Coefficients

Exercises of Chapter 2

Properties of Least Squares

DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR. Introductory Econometrics 1 hour 30 minutes

Computation of Higher Order Moments from Two Multinomial Overdispersion Likelihood Models

Chapter 4: Regression With One Regressor

BIO Lab 2: TWO-LEVEL NORMAL MODELS with school children popularity data

Lecture 20: Hypothesis testing

Econ107 Applied Econometrics Topic 9: Heteroskedasticity (Studenmund, Chapter 10)

Q1: Calculate the mean, median, sample variance, and standard deviation of 25, 40, 05, 70, 05, 40, 70.

Stat 543 Exam 2 Spring 2016

Primer on High-Order Moment Estimators

STATISTICS QUESTIONS. Step by Step Solutions.

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

Stat 543 Exam 2 Spring 2016

Probability Theory (revisited)

Chapter 20 Duration Analysis

January Examinations 2015

Chapter 14 Simple Linear Regression

1 Binary Response Models

Statistics II Final Exam 26/6/18

Topic 23 - Randomized Complete Block Designs (RCBD)

x = , so that calculated

Modeling and Simulation NETW 707

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

SIMPLE LINEAR REGRESSION

Department of Computer Science Artificial Intelligence Research Laboratory. Iowa State University MACHINE LEARNING

Statistics for Economics & Business

Interval Estimation in the Classical Normal Linear Regression Model. 1. Introduction

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Simulation and Random Number Generation

= z 20 z n. (k 20) + 4 z k = 4

Outline. Zero Conditional mean. I. Motivation. 3. Multiple Regression Analysis: Estimation. Read Wooldridge (2013), Chapter 3.

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Chat eld, C. and A.J.Collins, Introduction to multivariate analysis. Chapman & Hall, 1980

Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010

7. Multivariate Probability

Lecture 4 Hypothesis Testing

Chapter 15 - Multiple Regression

Tests of Exclusion Restrictions on Regression Coefficients: Formulation and Interpretation

Transcription:

ECON 56 Homework Multple Choce Choose the one that best completes the statement or answers the queston ) The probablty of an event A or B (Pr(A or B)) to occur equals a Pr(A) Pr(B) b Pr(A) + Pr(B) f A and B are mutually exclusve Pr( A) c Pr( B ) d Pr(A) + Pr(B) even f A and B are not mutually exclusve ) The expected value of a dscrete random varable a s the outcome that s most lkely to occur b can be found by determnng the 50% value n the cdf c equals the populaton medan d s computed as a weghted average of the possble outcome of that random varable, where the weghts are the probabltes of that outcome 3) Let Y be a random varable Then var(y) equals a E[( Y µ Y ) ] b E[ ( Y µ Y ) ] c E[( Y µ Y ) ] d E[( Y µ Y )] 4) The condtonal dstrbuton of Y gven X = x, Pr( Y = y X = x), s a b c d Pr( Y = y) Pr( X = x) l Pr( X = x, Y = y) = Pr( X= xy, = y) Pr( Y = y) Pr( X= xy, = y) Pr( X = x)

5) The condtonal expectaton of Y gven X, EY ( X= x), s calculated as follows: a k = y Pr( X = x Y = y) b EEY [ ( X )] c d k = l = y Pr( Y = y X = x) E( Y X = x ) Pr( X = x ) 6) Two random varables X and Y are ndependently dstrbuted f all of the followng condtons hold, wth the excepton of a Pr( Y = y X = x) = Pr( Y = y) b knowng the value of one of the varables provdes no nformaton about the other c f the condtonal dstrbuton of Y gven X equals the margnal dstrbuton of Y d EY ( ) = EEY [ ( X)] 7) The correlaton between X and Y a cannot be negatve snce varances are always postve b s the covarance squared c can be calculated by dvdng the covarance between X and Y by the product of the two standard devatons cov( XY, ) d s gven by corr( X, Y ) = var( X) var( Y) 8) var( ax + by ) = a b a σ + b σ X Y σx + σ XY + σy a ab b σ + µ µ c XY X Y d aσx + bσy

9) Assume that Y s normally dstrbuted N ( µσ, ) Movng from the mean ( µ ) 96 standard devatons to the left and 96 standard devatons to the rght, then the area under the normal pdf s a 067 b 005 c 095 d 033 0) Assume that Y s normally dstrbuted N ( µσ, ) To fnd Pr( c Y c), where c < c c µ and d =, you need to calculate Pr( d Z d) = σ a Φ d Φ d ( ) ( ) b Φ(96) Φ( 96) c Φ( d) ( Φ( d)) ( Φ( d ) Φ( d )) d ) The Student t dstrbuton s a the dstrbuton of the sum of m squared ndependent standard normal random varables b the dstrbuton of a random varable wth a ch-squared dstrbuton wth m degrees of freedom, dvded by m c always well approxmated by the standard normal dstrbuton d the dstrbuton of the rato of a standard normal random varable, dvded by the square root of an ndependently dstrbuted ch-squared random varable wth m degrees of freedom dvded by m ) When there are degrees of freedom, the t dstrbuton a can no longer be calculated b equals the standard normal dstrbuton c has a bell shape smlar to that of the normal dstrbuton, but wth fatter tals d equals the χ dstrbuton 3

3) To nfer the poltcal tendences of the students at your college/unversty, you sample 50 of them Only one of the followng s a smple random sample: You a make sure that the proporton of mnortes are the same n your sample as n the entre student body b call every ffteth person n the student drectory at 9 am If the person does not answer the phone, you pck the next name lsted, and so on c go to the man dnng hall on campus and ntervew students randomly there d have your statstcal package generate 50 random numbers n the range from to the total number of students n your academc nsttuton, and then choose the correspondng names n the student telephone drectory 4) In econometrcs, we typcally do not rely on exact or fnte sample dstrbutons because a we have approxmately an nfnte number of observatons (thnk of re-samplng) b varables typcally are normally dstrbuted c the covarance of Y, Yjare typcally not zero d asymptotc dstrbutons can be counted on to provde good approxmatons to the exact samplng dstrbuton (gven the number of observatons avalable n most cases) Essays and Longer Questons 5) Followng Alfred Nobel s wll, there are fve Nobel Przes awarded each year These are for outstandng achevements n Chemstry, Physcs, Physology or Medcne, Lterature, and Peace In 968, the Bank of Sweden added a prze n Economc Scences n memory of Alfred Nobel You thnk of the data as descrbng a populaton, rather than a sample from whch you want to nfer behavor of a larger populaton The accompanyng table lsts the jont probablty dstrbuton between recpents n economcs and the other fve przes, and the ctzenshp of the recpents, based on the 969-00 perod Jont Dstrbuton of Nobel Prze Wnners n Economcs and Non-Economcs Dscplnes, and Ctzenshp, 969-00 US Ctzen Non-US Ctzen Total ( Y = 0 ) ( Y = ) Economcs Nobel 08 0049 067 Prze ( X = 0 ) Physcs, Chemstry, 0345 0488 0833 Medcne, Lterature, and Peace Nobel Prze ( X = ) Total 0463 0537 00 (a) Compute EY ( ) and nterpret the resultng number 4

(b) Calculate and nterpret EY ( X= ) and EY ( X= 0) (c) A randomly selected Nobel Prze wnner reports that he s a non-us ctzen What s the probablty that ths genus has won the Economcs Nobel Prze? A Nobel Prze n the other fve dscplnes? 6) The heght of male students at your college/unversty s normally dstrbuted wth a mean of 70 nches and a standard devaton of 35 nches If you had a lst of telephone numbers for male students for the purpose of conductng a survey, what would be the probablty of randomly callng one of these students whose heght s (a) taller than 6'0"? (b) between 5'3" and 6'5"? (c) shorter than 5'7", the mean heght of female students? (d) shorter than 5'0"? (e) taller than Shaq O Neal, the center of the Mam Heat, who s 7'" tall? Compare ths to the probablty of a woman beng pregnant for 0 months (300 days), where days of pregnancy s normally dstrbuted wth a mean of 66 days and a standard devaton of 6 days 5