Multiple Choice Test. Chapter Adequacy of Models for Regression

Similar documents
Objectives of Multiple Regression

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

ENGI 3423 Simple Linear Regression Page 12-01

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

STA302/1001-Fall 2008 Midterm Test October 21, 2008

2SLS Estimates ECON In this case, begin with the assumption that E[ i

Random Variables and Probability Distributions

ε. Therefore, the estimate

ENGI 4421 Propagation of Error Page 8-01

Transforming Numerical Methods Education for the STEM Undergraduate Torque (N-m)

Correlation and Regression Analysis

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018

Econometric Methods. Review of Estimation

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

Example: Multiple linear regression. Least squares regression. Repetition: Simple linear regression. Tron Anders Moger

Statistics MINITAB - Lab 5

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

Chapter Two. An Introduction to Regression ( )

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

residual. (Note that usually in descriptions of regression analysis, upper-case

Probability and. Lecture 13: and Correlation

: At least two means differ SST

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

ESS Line Fitting

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model

Chapter 13 Student Lecture Notes 13-1

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

Correlation and Simple Linear Regression

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.

Chapter Statistics Background of Regression Analysis

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Wu-Hausman Test: But if X and ε are independent, βˆ. ECON 324 Page 1


Simple Linear Regression

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes

Summary of the lecture in Biostatistics

MEASURES OF DISPERSION

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

STA 105-M BASIC STATISTICS (This is a multiple choice paper.)

Lecture 3 Probability review (cont d)

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Analyzing Two-Dimensional Data. Analyzing Two-Dimensional Data

Regression and the LMS Algorithm

Module 7. Lecture 7: Statistical parameter estimation

Multiple Linear Regression Analysis

Third handout: On the Gini Index

The equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use.

Lecture 07: Poles and Zeros

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Simple Linear Regression

4. Standard Regression Model and Spatial Dependence Tests

Lecture Notes Types of economic variables

Maximum Likelihood Estimation

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Functions of Random Variables

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

Linear Regression with One Regressor

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01

Multivariate Transformation of Variables and Maximum Likelihood Estimation

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

Evaluating Polynomials

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

Answer key to problem set # 2 ECON 342 J. Marcelo Ochoa Spring, 2009

ρ < 1 be five real numbers. The

Special Instructions / Useful Data

CHAPTER VI Statistical Analysis of Experimental Data

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Previous lecture. Lecture 8. Learning outcomes of this lecture. Today. Statistical test and Scales of measurement. Correlation

CSE 5526: Introduction to Neural Networks Linear Regression

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs

Point Estimation: definition of estimators

Statistics: Unlocking the Power of Data Lock 5

Module 7: Probability and Statistics

Physics 114 Exam 2 Fall Name:

Lecture 1: Introduction to Regression

Regression. Linear Regression. A Simple Data Display. A Batch of Data. The Mean is 220. A Value of 474. STAT Handout Module 15 1 st of June 2009

Topic 4: Simple Correlation and Regression Analysis

CHAPTER 2. = y ˆ β x (.1022) So we can write

Fundamentals of Regression Analysis

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model

Chapter 14 Logistic Regression Models

IFYMB002 Mathematics Business Appendix C Formula Booklet

L5 Polynomial / Spline Curves

Example. Row Hydrogen Carbon

The Mathematics of Portfolio Theory

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

LINEAR REGRESSION ANALYSIS

Lecture 9: Tolerant Testing

Lecture 2: Linear Least Squares Regression

Lecture 8: Linear Regression

Homework Solution (#5)

Transcription:

Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to (A) % (B) 0% (C) 90% (D) 9% oluto The correct aswer s (D). 9% of the scaled resduals eed to be the [-,] rage to cosder a lear regresso model to be adequate. Note that ths s oe of the several cosderatos amogst others, for example, hgh coeffcet of determato, etc.

. Gve vs. x data of 0 pars are regressed to a straght le. The straght le regresso model s gve b 9 x ad the coeffcet of determato s foud to be 0.9. The correlato coeffcet s (A) 0.768 (B) 0.348 (C) 0.768 (D) 0.348 oluto The correct aswer s (A). The coeffcet of determato r s gve as 0.9, that s, r 0.9 r ±0.768 The sg of the correlato coeffcet r s determed b the slope of the regresso le. ce the slope of the regresso le 9 x s egatve, the correlato coeffcet s egatve. Hece, r 0.768

3. The followg vs. x data s regressed to a straght le. x 6 7 8 9 0.3 0.4 0.7 0.6.77 The lear regresso model s foud to be.4700 + 0. 340x. The coeffcet of determato s (A) 0.3046 (B) 0.439 (C) 0.696 (D) 0.8339 oluto The correct aswer s (C). The sum of the square of the resduals for the lear regresso model r ( a a x 0 ) ( +.4700 0.340x ) (0.3 +.4700 0.340()) + (0.4 +.4700 0.340(6)) + (0.7 +.4700 0.340(7)) + (0.6 +.4700 0.340(8)) + (.77 +.4700 0.340(9)) 0.439 + 0 a a x s gve b The sum of the squares of the dfferece betwee the depedet varable ad ts average s gve b t ( ) Now the average of the depedet varables s gve b 0.3 + 0.4 + 0.7 + 0.6 +.77 0.780

The ) ( t 0.780) ( 0.780) (.77 0.780) (0.6 0.780) (0.7 0.780) (0.4 0.780) 0.3 ( + + + +.48 Hece, the coeffcet of determato s t r t r.48 0.439.48 0.694

4. Ma tmes ou ma ot kow what regresso model to use for gve dscrete data. I such cases, a suggesto ma be to use a polomal model. But the questo remas what order of polomal to use? For example, f ou are gve 0 data pots, ou ca regress the data to a polomal order 0,,, 3, 4,, 6, 7, 8, or 9. Below s the questo ou are asked to aswer. If s the sum of the squares of the resduals ad p s the order of the polomal, the crtero ou would use to fd the optmum order of the polomal would be to fd the mmum of for all possble polomal orders. If ou have 30 data pots, the the value of m the m p formula s (A) 0 (B) 9 (C) 30 (D) 0 oluto The correct aswer s (B). The sum of the square of the resduals geerall decreases as the order of polomal s creased, ad wll fact become zero f the order of the polomal s oe less tha the umber of data pots. To fd a optmum regresso polomal order, we fd the rato of the sum of the squares of the resduals ad the dfferece betwee umber of data pots ad umber of regresso costats, that s, r Varace () ( l +) where umber of data pots, l order of polomal. Note that the umerator, that s sum of square of resduals, s zero whe f the deomator, the umber of data pots s same as the umber of regresso coeffcets (order of polomal+). The optmum order of polomal s chose whe there s a sgfcat decrease the varace as gve b equato ().

Varace 3 4 Order of Polomal, l Fgure. Varace are fucto of order of polomal. Gog back to equato () ad what s gve the questo, r r m p ( l +) Hece r ( ) l m 30 9

. O regressg data pars ( x, ),..., ( x, ) to a lear regresso model a + 0 ax, a scetst fds the regresso model to have zero slope. The regresso model the s gve b (A) (B) (C) 0 (D) x ( oluto The correct aswer s (B) ) ce for a geeral regresso model, a0 ax ad t s foud that a 0 we get a 0 0 x Hece the regresso model s gve b a + 0 ax 0x

6. Whch of the followg patters of resduals s acceptable for a lear regresso model? (A) Resduals Idepedet Varable (B) Resduals Ideoedet Varable (C) Resduals Idepedet Varable (D) Resduals Idepedet Varable oluto The correct aswer s (A). The ol acceptable patter for the resdual plots s the fgure choce (A) as t s radom. Other fgures show a patter. The fgure choce (B) ad (D) show olear behavor, whle choce (C) the fgure shows a double bow.