NPTEL Project. Econometric Modelling. Module 14: Heteroscedasticity Problem. Module 16: Heteroscedasticity Problem. Vinod Gupta School of Management

Size: px
Start display at page:

Download "NPTEL Project. Econometric Modelling. Module 14: Heteroscedasticity Problem. Module 16: Heteroscedasticity Problem. Vinod Gupta School of Management"

Transcription

1 1 P age NPTEL Project Econometric Modelling Vinod Gupta School of Management Module 14: Heteroscedasticity Problem Module 16: Heteroscedasticity Problem Rudra P. Pradhan Vinod Gupta School of Management Indian Institute of Technology Kharagpur, India rudrap@vgsom.iitkgp.ernet 1

2 2 P age 1. MODULE OBJECTIVE This module attempts to explore the possibilities of volatility of error variables. The estimated model by the application of OLS, discussed earlier, is based on the assumption that error variance should be uniform over time/ cross sectional units. That is covariance between two errors variables should equal to a constant [i.e. Cov (U i, U j ) = 0 for i = j]. If this assumption is violated, then it is heteroskedasticity; otherwise, it is the situation of homoskedasticity (i.e. equal error variance). In this module, we deal with the followings: 1. WHAT IS HETEROSKEDASTICITY AND HOW IS ITS NATURE? 2. WHAT ARE ITS CONSEQUENCES? 3. DOES IT REALLY A PROBLEM? 4. DETECTION CRITERIA 5. CAUSES OF HETEROSKEDASTICITY 6. REMEDIAL MEASURES 2

3 3 P age WHAT IS HETEROSKEDASTICITY? In general, heteroskedasticity means unequal error variance. If it is present in the estimated model, it is the violation of OLS technique and hence, the estimated model cannot be used for prediction and forecasting. The structure of heteroskedasticity is as follows: Y t = β 0 + β 1 X t + U t and where CONSEQUENCES OF HETEROSKEDASTICITY The estimation process requires that OLS applications of estimated parameters should follow the BLUE theorem. If not, there is problem on model reliability. In specific, the presence of heteroskedasticity makes the estimated parameters unbiased and their standard errors are relatively high. So, it drastically affects the minimum variance property and hence, affects the model reliability. DOES IT REALLY PROBLEM? On the first instance, any estimated parameters whose value does not follow BLUE theorem means it is really a problem. However, in the case of heteroskedasticity, it depends upon the objective specification. If the objective is model reliability, then it is serious issue, even if it is at the minor level. 3

4 4 P age DETECTION OF HETEROSCEDASTICITY The detection of heteroskedasticity can be done only after the estimation process. So, first we should have estimated model and then we can have the error term. Once we get the error term, the process of detecting heteroskedasticity is feasible. The residuals in case of heteroskedasticity can be calculated by plotting them in the time sequence plot or alternatively we can plot the standardized residuals against time. Apart from these there are several quantitative tests that one can apply in order to supplement the pure qualitative approach. These are as follows: PARK TEST GLESJER TEST SPEARMAN S RANK CORRELATION TEST GOLDFELD QUANDT TEST BREUSCH PAGAN GODFREY TEST WHITE GENERAL TEST Among them, Goldfeld- Quandt test and Spearman Rank Correlation is very popular. So, we briefly highlight these two tests here. 4

5 5 P age GOLDFELD QUANDT TEST In this method we do the following steps: rank the observations from lowest value Omit the central c observations and divide the rest of the observations in two halves Find the RSS of the two sets of observations and RSS of the original model Compute F-stat, which is as follows: It can be further shown that the follows the F distribution and if it is significant, then there is presence of heteroskedasticity; otherwise there is no heteroskedasticity in the system. Spearman Rank Correlation TEST In this test, we first assign rank to error term and any of the variables and then find out the rank correlation. If the correlation coefficient is statistically significant then there is presence of heteroskedasticity; otherwise there is no heteroskedasticity in the system. CAUSES OF HETEROSKEDASTICITY Error improvement model Growth and trend factors Misspecification of the random term An over determined model An under-determined model 5

6 6 P age Omission of variables Natural tendency of the variables Outliers problem Skewness in the distribution Wrong functional form Data improvement REMEDIAL MEASURES As we have seen, heteroscedasticity does not destroy the unbiasedness and consistency properties of the OLS estimators, but they are no longer efficient, not even asymptotically (i.e., large sample size). This lack of efficiency makes the usual hypothesis-testing procedure of dubious value. Documenting the consequences of heteroscedasticity is easier than detecting it. There are several diagnostic tests available, but one cannot tell for sure which will work in a given situation. Even if heteroscedasticity is suspected and detected, it is not easy to correct the problem. If the sample is large, one can obtain White s heteroskedasticity corrected standard errors of OLS estimators and conduct statistical inference based on these standard errors. Otherwise, on the basis of OLS residuals, one can make educated guesses of the likely pattern of heteroscedasticity and transform the original data in such a way that in the transformed data there is no heteroskedasticity. 6

Example 7.2. Autocorrelation. Pilar González and Susan Orbe. Dpt. Applied Economics III (Econometrics and Statistics)

Example 7.2. Autocorrelation. Pilar González and Susan Orbe. Dpt. Applied Economics III (Econometrics and Statistics) Example 7.2 Autocorrelation Pilar González and Susan Orbe Dpt. Applied Economics III (Econometrics and Statistics) Pilar González and Susan Orbe OCW 2014 Example 7.2. Autocorrelation 1 / 17 Questions.

More information

Carrying out an Empirical Project

Carrying out an Empirical Project Carrying out an Empirical Project Empirical Analysis & Style Hint Special program: Pre-training 1 Carrying out an Empirical Project 1. Posing a Question 2. Literature Review 3. Data Collection 4. Econometric

More information

EC352 Econometric Methods: Week 07

EC352 Econometric Methods: Week 07 EC352 Econometric Methods: Week 07 Gordon Kemp Department of Economics, University of Essex 1 / 25 Outline Panel Data (continued) Random Eects Estimation and Clustering Dynamic Models Validity & Threats

More information

Validity, Reliability and Classical Assumptions

Validity, Reliability and Classical Assumptions , Reliability and Classical Assumptions Presented by Mahendra AN Sources: www-psych.stanford.edu/~bigopp/.ppt http://ets.mnsu.edu/darbok/ethn402-502/reliability.ppt http://5martconsultingbandung.blogspot.com/2011/01/uji-asumsi-klasik.html

More information

MEA DISCUSSION PAPERS

MEA DISCUSSION PAPERS Inference Problems under a Special Form of Heteroskedasticity Helmut Farbmacher, Heinrich Kögel 03-2015 MEA DISCUSSION PAPERS mea Amalienstr. 33_D-80799 Munich_Phone+49 89 38602-355_Fax +49 89 38602-390_www.mea.mpisoc.mpg.de

More information

INTRODUCTION TO ECONOMETRICS (EC212)

INTRODUCTION TO ECONOMETRICS (EC212) INTRODUCTION TO ECONOMETRICS (EC212) Course duration: 54 hours lecture and class time (Over three weeks) LSE Teaching Department: Department of Economics Lead Faculty (session two): Dr Taisuke Otsu and

More information

Getting started with Eviews 9 (Volume IV)

Getting started with Eviews 9 (Volume IV) Getting started with Eviews 9 (Volume IV) Afees A. Salisu 1 5.0 RESIDUAL DIAGNOSTICS Before drawing conclusions/policy inference from any of the above estimated regressions, it is important to perform

More information

Part 1. Online Session: Math Review and Math Preparation for Course 5 minutes Introduction 45 minutes Reading and Practice Problem Assignment

Part 1. Online Session: Math Review and Math Preparation for Course 5 minutes Introduction 45 minutes Reading and Practice Problem Assignment Course Schedule PREREQUISITE (Pre-Class) Advanced Education Diagnostic Test 10 minutes Excel 2007 Exercise SECTION 1. (Completed before face-to-face sections begin) (2 hours) Part 1. Online Session: Math

More information

Inference with Difference-in-Differences Revisited

Inference with Difference-in-Differences Revisited Inference with Difference-in-Differences Revisited M. Brewer, T- F. Crossley and R. Joyce Journal of Econometric Methods, 2018 presented by Federico Curci February 22nd, 2018 Brewer, Crossley and Joyce

More information

Citation for published version (APA): Ebbes, P. (2004). Latent instrumental variables: a new approach to solve for endogeneity s.n.

Citation for published version (APA): Ebbes, P. (2004). Latent instrumental variables: a new approach to solve for endogeneity s.n. University of Groningen Latent instrumental variables Ebbes, P. IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document

More information

Session 1: Dealing with Endogeneity

Session 1: Dealing with Endogeneity Niehaus Center, Princeton University GEM, Sciences Po ARTNeT Capacity Building Workshop for Trade Research: Behind the Border Gravity Modeling Thursday, December 18, 2008 Outline Introduction 1 Introduction

More information

Pitfalls in Linear Regression Analysis

Pitfalls in Linear Regression Analysis Pitfalls in Linear Regression Analysis Due to the widespread availability of spreadsheet and statistical software for disposal, many of us do not really have a good understanding of how to use regression

More information

Propensity Score Methods for Estimating Causality in the Absence of Random Assignment: Applications for Child Care Policy Research

Propensity Score Methods for Estimating Causality in the Absence of Random Assignment: Applications for Child Care Policy Research 2012 CCPRC Meeting Methodology Presession Workshop October 23, 2012, 2:00-5:00 p.m. Propensity Score Methods for Estimating Causality in the Absence of Random Assignment: Applications for Child Care Policy

More information

1.4 - Linear Regression and MS Excel

1.4 - Linear Regression and MS Excel 1.4 - Linear Regression and MS Excel Regression is an analytic technique for determining the relationship between a dependent variable and an independent variable. When the two variables have a linear

More information

Dr. Kelly Bradley Final Exam Summer {2 points} Name

Dr. Kelly Bradley Final Exam Summer {2 points} Name {2 points} Name You MUST work alone no tutors; no help from classmates. Email me or see me with questions. You will receive a score of 0 if this rule is violated. This exam is being scored out of 00 points.

More information

IS BEER CONSUMPTION IN IRELAND ACYCLICAL?

IS BEER CONSUMPTION IN IRELAND ACYCLICAL? IS BEER CONSUMPTION IN IRELAND ACYCLICAL? GEARÓID GIBBS Senior Sophister In this econometric investigation, Gearóid Gibbs examines beer consumption in Ireland and its relation to the business cycle. Citing

More information

Marno Verbeek Erasmus University, the Netherlands. Cons. Pros

Marno Verbeek Erasmus University, the Netherlands. Cons. Pros Marno Verbeek Erasmus University, the Netherlands Using linear regression to establish empirical relationships Linear regression is a powerful tool for estimating the relationship between one variable

More information

Heteroskedasticity in Multiple Regression Analysis: What it is, How to Detect it and How to Solve it with Applications in R and SPSS

Heteroskedasticity in Multiple Regression Analysis: What it is, How to Detect it and How to Solve it with Applications in R and SPSS A peer-reviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to Practical Assessment, Research & Evaluation. Permission is granted to distribute

More information

Session 3: Dealing with Reverse Causality

Session 3: Dealing with Reverse Causality Principal, Developing Trade Consultants Ltd. ARTNeT Capacity Building Workshop for Trade Research: Gravity Modeling Thursday, August 26, 2010 Outline Introduction 1 Introduction Overview Endogeneity and

More information

Various Approaches to Szroeter s Test for Regression Quantiles

Various Approaches to Szroeter s Test for Regression Quantiles The International Scientific Conference INPROFORUM 2017, November 9, 2017, České Budějovice, 361-365, ISBN 978-80-7394-667-8. Various Approaches to Szroeter s Test for Regression Quantiles Jan Kalina,

More information

Mantel-Haenszel Procedures for Detecting Differential Item Functioning

Mantel-Haenszel Procedures for Detecting Differential Item Functioning A Comparison of Logistic Regression and Mantel-Haenszel Procedures for Detecting Differential Item Functioning H. Jane Rogers, Teachers College, Columbia University Hariharan Swaminathan, University of

More information

Practical Regression: Convincing Empirical Research in Ten Steps

Practical Regression: Convincing Empirical Research in Ten Steps DAVID DRANOVE 7-112-001 Practical Regression: Convincing Empirical Research in Ten Steps This is one in a series of notes entitled Practical Regression. These notes supplement the theoretical content of

More information

MS&E 226: Small Data

MS&E 226: Small Data MS&E 226: Small Data Lecture 10: Introduction to inference (v2) Ramesh Johari ramesh.johari@stanford.edu 1 / 17 What is inference? 2 / 17 Where did our data come from? Recall our sample is: Y, the vector

More information

Econometric Game 2012: infants birthweight?

Econometric Game 2012: infants birthweight? Econometric Game 2012: How does maternal smoking during pregnancy affect infants birthweight? Case A April 18, 2012 1 Introduction Low birthweight is associated with adverse health related and economic

More information

Assessing Studies Based on Multiple Regression. Chapter 7. Michael Ash CPPA

Assessing Studies Based on Multiple Regression. Chapter 7. Michael Ash CPPA Assessing Studies Based on Multiple Regression Chapter 7 Michael Ash CPPA Assessing Regression Studies p.1/20 Course notes Last time External Validity Internal Validity Omitted Variable Bias Misspecified

More information

26:010:557 / 26:620:557 Social Science Research Methods

26:010:557 / 26:620:557 Social Science Research Methods 26:010:557 / 26:620:557 Social Science Research Methods Dr. Peter R. Gillett Associate Professor Department of Accounting & Information Systems Rutgers Business School Newark & New Brunswick 1 Overview

More information

Performance of Median and Least Squares Regression for Slightly Skewed Data

Performance of Median and Least Squares Regression for Slightly Skewed Data World Academy of Science, Engineering and Technology 9 Performance of Median and Least Squares Regression for Slightly Skewed Data Carolina Bancayrin - Baguio Abstract This paper presents the concept of

More information

Profile Analysis. Intro and Assumptions Psy 524 Andrew Ainsworth

Profile Analysis. Intro and Assumptions Psy 524 Andrew Ainsworth Profile Analysis Intro and Assumptions Psy 524 Andrew Ainsworth Profile Analysis Profile analysis is the repeated measures extension of MANOVA where a set of DVs are commensurate (on the same scale). Profile

More information

Chapter 11: Advanced Remedial Measures. Weighted Least Squares (WLS)

Chapter 11: Advanced Remedial Measures. Weighted Least Squares (WLS) Chapter : Advanced Remedial Measures Weighted Least Squares (WLS) When the error variance appears nonconstant, a transformation (of Y and/or X) is a quick remedy. But it may not solve the problem, or it

More information

HW 1 - Bus Stat. Student:

HW 1 - Bus Stat. Student: HW 1 - Bus Stat Student: 1. An identification of police officers by rank would represent a(n) level of measurement. A. Nominative C. Interval D. Ratio 2. A(n) variable is a qualitative variable such that

More information

Introduction to Econometrics

Introduction to Econometrics Introduction to Econometrics James H. Stock HARVARD UNIVERSITY Mark W. Watson PRINCETON UNIVERSITY... ~ Boston San Francisco New York London Toronco Sydney ToJ..:yo Singapore Madrid Mexico City Munich

More information

2.75: 84% 2.5: 80% 2.25: 78% 2: 74% 1.75: 70% 1.5: 66% 1.25: 64% 1.0: 60% 0.5: 50% 0.25: 25% 0: 0%

2.75: 84% 2.5: 80% 2.25: 78% 2: 74% 1.75: 70% 1.5: 66% 1.25: 64% 1.0: 60% 0.5: 50% 0.25: 25% 0: 0% Capstone Test (will consist of FOUR quizzes and the FINAL test grade will be an average of the four quizzes). Capstone #1: Review of Chapters 1-3 Capstone #2: Review of Chapter 4 Capstone #3: Review of

More information

Excel 2007 Exercise. SECTION 1. (Completed before face-to-face sections begin) (2 hours)

Excel 2007 Exercise. SECTION 1. (Completed before face-to-face sections begin) (2 hours) Course Schedule PREREQUISITE (Pre-Class) Advanced Education Diagnostic Test Excel 2007 Exercise SECTION 1. (Completed before face-to-face sections begin) (2 hours) Part 1. Online Session: Math Review and

More information

Estimating Heterogeneous Choice Models with Stata

Estimating Heterogeneous Choice Models with Stata Estimating Heterogeneous Choice Models with Stata Richard Williams Notre Dame Sociology rwilliam@nd.edu West Coast Stata Users Group Meetings October 25, 2007 Overview When a binary or ordinal regression

More information

AP Psych - Stat 2 Name Period Date. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

AP Psych - Stat 2 Name Period Date. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. AP Psych - Stat 2 Name Period Date MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) In a set of incomes in which most people are in the $15,000

More information

Dan Byrd UC Office of the President

Dan Byrd UC Office of the President Dan Byrd UC Office of the President 1. OLS regression assumes that residuals (observed value- predicted value) are normally distributed and that each observation is independent from others and that the

More information

Analyzing binary outcomes, going beyond logistic regression

Analyzing binary outcomes, going beyond logistic regression Analyzing binary outcomes, going beyond logistic regression 2018 EHE Forum presentation James O. Uanhoro Department of Educational Studies Premise Obtaining relative risk using Poisson regression Obtaining

More information

Lecture 14: Adjusting for between- and within-cluster covariates in the analysis of clustered data May 14, 2009

Lecture 14: Adjusting for between- and within-cluster covariates in the analysis of clustered data May 14, 2009 Measurement, Design, and Analytic Techniques in Mental Health and Behavioral Sciences p. 1/3 Measurement, Design, and Analytic Techniques in Mental Health and Behavioral Sciences Lecture 14: Adjusting

More information

Biostatistics. Donna Kritz-Silverstein, Ph.D. Professor Department of Family & Preventive Medicine University of California, San Diego

Biostatistics. Donna Kritz-Silverstein, Ph.D. Professor Department of Family & Preventive Medicine University of California, San Diego Biostatistics Donna Kritz-Silverstein, Ph.D. Professor Department of Family & Preventive Medicine University of California, San Diego (858) 534-1818 dsilverstein@ucsd.edu Introduction Overview of statistical

More information

The preceding five chapters explain how to use multiple regression to analyze the

The preceding five chapters explain how to use multiple regression to analyze the CHAPTER 9 Assessing Studies Based on Multiple Regression The preceding five chapters explain how to use multiple regression to analyze the relationship among variables in a data set. In this chapter, we

More information

Introduction to Econometrics

Introduction to Econometrics Global edition Introduction to Econometrics Updated Third edition James H. Stock Mark W. Watson MyEconLab of Practice Provides the Power Optimize your study time with MyEconLab, the online assessment and

More information

Study Guide for the Final Exam

Study Guide for the Final Exam Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make

More information

WELCOME! Lecture 11 Thommy Perlinger

WELCOME! Lecture 11 Thommy Perlinger Quantitative Methods II WELCOME! Lecture 11 Thommy Perlinger Regression based on violated assumptions If any of the assumptions are violated, potential inaccuracies may be present in the estimated regression

More information

A GUIDE TO ROBUST STATISTICAL METHODS IN NEUROSCIENCE. Keywords: Non-normality, heteroscedasticity, skewed distributions, outliers, curvature.

A GUIDE TO ROBUST STATISTICAL METHODS IN NEUROSCIENCE. Keywords: Non-normality, heteroscedasticity, skewed distributions, outliers, curvature. A GUIDE TO ROBUST STATISTICAL METHODS IN NEUROSCIENCE Authors: Rand R. Wilcox 1, Guillaume A. Rousselet 2 1. Dept. of Psychology, University of Southern California, Los Angeles, CA 90089-1061, USA 2. Institute

More information

Midterm STAT-UB.0003 Regression and Forecasting Models. I will not lie, cheat or steal to gain an academic advantage, or tolerate those who do.

Midterm STAT-UB.0003 Regression and Forecasting Models. I will not lie, cheat or steal to gain an academic advantage, or tolerate those who do. Midterm STAT-UB.0003 Regression and Forecasting Models The exam is closed book and notes, with the following exception: you are allowed to bring one letter-sized page of notes into the exam (front and

More information

Chapter 5: Field experimental designs in agriculture

Chapter 5: Field experimental designs in agriculture Chapter 5: Field experimental designs in agriculture Jose Crossa Biometrics and Statistics Unit Crop Research Informatics Lab (CRIL) CIMMYT. Int. Apdo. Postal 6-641, 06600 Mexico, DF, Mexico Introduction

More information

Problem Set 3 ECN Econometrics Professor Oscar Jorda. Name. ESSAY. Write your answer in the space provided.

Problem Set 3 ECN Econometrics Professor Oscar Jorda. Name. ESSAY. Write your answer in the space provided. Problem Set 3 ECN 140 - Econometrics Professor Oscar Jorda Name ESSAY. Write your answer in the space provided. 1) Sir Francis Galton, a cousin of James Darwin, examined the relationship between the height

More information

Correlation and Regression

Correlation and Regression Dublin Institute of Technology ARROW@DIT Books/Book Chapters School of Management 2012-10 Correlation and Regression Donal O'Brien Dublin Institute of Technology, donal.obrien@dit.ie Pamela Sharkey Scott

More information

Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study

Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study STATISTICAL METHODS Epidemiology Biostatistics and Public Health - 2016, Volume 13, Number 1 Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation

More information

USING STATCRUNCH TO CONSTRUCT CONFIDENCE INTERVALS and CALCULATE SAMPLE SIZE

USING STATCRUNCH TO CONSTRUCT CONFIDENCE INTERVALS and CALCULATE SAMPLE SIZE USING STATCRUNCH TO CONSTRUCT CONFIDENCE INTERVALS and CALCULATE SAMPLE SIZE Using StatCrunch for confidence intervals (CI s) is super easy. As you can see in the assignments, I cover 9.2 before 9.1 because

More information

Psychologist use statistics for 2 things

Psychologist use statistics for 2 things Psychologist use statistics for 2 things O Summarize the information from the study/experiment O Measures of central tendency O Mean O Median O Mode O Make judgements and decisions about the data O See

More information

Safecity Policy Project: Railway Stations Audit Report. Safecity is an initiative of Red Dot Foundation which aims to make cities safer by

Safecity Policy Project: Railway Stations Audit Report. Safecity is an initiative of Red Dot Foundation which aims to make cities safer by Safecity Policy Project: Railway Stations Audit Report Safecity is an initiative of Red Dot Foundation which aims to make cities safer by encouraging equal access to public spaces for everyone, especially

More information

DRAFT (Final) Concept Paper On choosing appropriate estimands and defining sensitivity analyses in confirmatory clinical trials

DRAFT (Final) Concept Paper On choosing appropriate estimands and defining sensitivity analyses in confirmatory clinical trials DRAFT (Final) Concept Paper On choosing appropriate estimands and defining sensitivity analyses in confirmatory clinical trials EFSPI Comments Page General Priority (H/M/L) Comment The concept to develop

More information

Influence of overweight and obesity on the diabetes in the world on adult people using spatial regression

Influence of overweight and obesity on the diabetes in the world on adult people using spatial regression International Journal of Advances in Intelligent Informatics ISSN: 2442-6571 149 Influence of overweight and obesity on the diabetes in the world on adult people using spatial regression Tuti Purwaningsih

More information

Multiple Regression Analysis

Multiple Regression Analysis Multiple Regression Analysis Basic Concept: Extend the simple regression model to include additional explanatory variables: Y = β 0 + β1x1 + β2x2 +... + βp-1xp + ε p = (number of independent variables

More information

Basic Statistics 01. Describing Data. Special Program: Pre-training 1

Basic Statistics 01. Describing Data. Special Program: Pre-training 1 Basic Statistics 01 Describing Data Special Program: Pre-training 1 Describing Data 1. Numerical Measures Measures of Location Measures of Dispersion Correlation Analysis 2. Frequency Distributions (Relative)

More information

AP Psych - Stat 1 Name Period Date. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

AP Psych - Stat 1 Name Period Date. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. AP Psych - Stat 1 Name Period Date MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) In a set of incomes in which most people are in the $15,000

More information

Overview of Non-Parametric Statistics

Overview of Non-Parametric Statistics Overview of Non-Parametric Statistics LISA Short Course Series Mark Seiss, Dept. of Statistics April 7, 2009 Presentation Outline 1. Homework 2. Review of Parametric Statistics 3. Overview Non-Parametric

More information

Chapter 1: Explaining Behavior

Chapter 1: Explaining Behavior Chapter 1: Explaining Behavior GOAL OF SCIENCE is to generate explanations for various puzzling natural phenomenon. - Generate general laws of behavior (psychology) RESEARCH: principle method for acquiring

More information

LAB ASSIGNMENT 4 INFERENCES FOR NUMERICAL DATA. Comparison of Cancer Survival*

LAB ASSIGNMENT 4 INFERENCES FOR NUMERICAL DATA. Comparison of Cancer Survival* LAB ASSIGNMENT 4 1 INFERENCES FOR NUMERICAL DATA In this lab assignment, you will analyze the data from a study to compare survival times of patients of both genders with different primary cancers. First,

More information

Students will understand the definition of mean, median, mode and standard deviation and be able to calculate these functions with given set of

Students will understand the definition of mean, median, mode and standard deviation and be able to calculate these functions with given set of Students will understand the definition of mean, median, mode and standard deviation and be able to calculate these functions with given set of numbers. Also, students will understand why some measures

More information

Stepwise method Modern Model Selection Methods Quantile-Quantile plot and tests for normality

Stepwise method Modern Model Selection Methods Quantile-Quantile plot and tests for normality Week 9 Hour 3 Stepwise method Modern Model Selection Methods Quantile-Quantile plot and tests for normality Stat 302 Notes. Week 9, Hour 3, Page 1 / 39 Stepwise Now that we've introduced interactions,

More information

Comparison of Some Almost Unbiased Ratio Estimators

Comparison of Some Almost Unbiased Ratio Estimators Comparison of Some Almost Unbiased Ratio Estimators Priyaranjan Dash Department of Statistics, Tripura University, India. ---------------------------------------------------------------------***---------------------------------------------------------------------

More information

Ec331: Research in Applied Economics Spring term, Panel Data: brief outlines

Ec331: Research in Applied Economics Spring term, Panel Data: brief outlines Ec331: Research in Applied Economics Spring term, 2014 Panel Data: brief outlines Remaining structure Final Presentations (5%) Fridays, 9-10 in H3.45. 15 mins, 8 slides maximum Wk.6 Labour Supply - Wilfred

More information

Assignment #6. Chapter 10: 14, 15 Chapter 11: 14, 18. Due tomorrow Nov. 6 th by 2pm in your TA s homework box

Assignment #6. Chapter 10: 14, 15 Chapter 11: 14, 18. Due tomorrow Nov. 6 th by 2pm in your TA s homework box Assignment #6 Chapter 10: 14, 15 Chapter 11: 14, 18 Due tomorrow Nov. 6 th by 2pm in your TA s homework box Assignment #7 Chapter 12: 18, 24 Chapter 13: 28 Due next Friday Nov. 13 th by 2pm in your TA

More information

11/18/2013. Correlational Research. Correlational Designs. Why Use a Correlational Design? CORRELATIONAL RESEARCH STUDIES

11/18/2013. Correlational Research. Correlational Designs. Why Use a Correlational Design? CORRELATIONAL RESEARCH STUDIES Correlational Research Correlational Designs Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are

More information

Limited dependent variable regression models

Limited dependent variable regression models 181 11 Limited dependent variable regression models In the logit and probit models we discussed previously the dependent variable assumed values of 0 and 1, 0 representing the absence of an attribute and

More information

Write your identification number on each paper and cover sheet (the number stated in the upper right hand corner on your exam cover).

Write your identification number on each paper and cover sheet (the number stated in the upper right hand corner on your exam cover). STOCKHOLM UNIVERSITY Department of Economics Course name: Empirical methods 2 Course code: EC2402 Examiner: Per Pettersson-Lidbom Number of credits: 7,5 credits Date of exam: Sunday 21 February 2010 Examination

More information

Psychology Research Process

Psychology Research Process Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:

More information

Sociology 63993, Exam1 February 12, 2015 Richard Williams, University of Notre Dame,

Sociology 63993, Exam1 February 12, 2015 Richard Williams, University of Notre Dame, Sociology 63993, Exam1 February 12, 2015 Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ I. True-False. (20 points) Indicate whether the following statements are true or false.

More information

The University of North Carolina at Chapel Hill School of Social Work

The University of North Carolina at Chapel Hill School of Social Work The University of North Carolina at Chapel Hill School of Social Work SOWO 918: Applied Regression Analysis and Generalized Linear Models Spring Semester, 2014 Instructor Shenyang Guo, Ph.D., Room 524j,

More information

Content. Basic Statistics and Data Analysis for Health Researchers from Foreign Countries. Research question. Example Newly diagnosed Type 2 Diabetes

Content. Basic Statistics and Data Analysis for Health Researchers from Foreign Countries. Research question. Example Newly diagnosed Type 2 Diabetes Content Quantifying association between continuous variables. Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General

More information

AP Stats Review for Midterm

AP Stats Review for Midterm AP Stats Review for Midterm NAME: Format: 10% of final grade. There will be 20 multiple-choice questions and 3 free response questions. The multiple-choice questions will be worth 2 points each and the

More information

Abdul Latif Jameel Poverty Action Lab Executive Training: Evaluating Social Programs Spring 2009

Abdul Latif Jameel Poverty Action Lab Executive Training: Evaluating Social Programs Spring 2009 MIT OpenCourseWare http://ocw.mit.edu Abdul Latif Jameel Poverty Action Lab Executive Training: Evaluating Social Programs Spring 2009 For information about citing these materials or our Terms of Use,

More information

Meta-Analysis. Zifei Liu. Biological and Agricultural Engineering

Meta-Analysis. Zifei Liu. Biological and Agricultural Engineering Meta-Analysis Zifei Liu What is a meta-analysis; why perform a metaanalysis? How a meta-analysis work some basic concepts and principles Steps of Meta-analysis Cautions on meta-analysis 2 What is Meta-analysis

More information

Problem set 2: understanding ordinary least squares regressions

Problem set 2: understanding ordinary least squares regressions Problem set 2: understanding ordinary least squares regressions September 12, 2013 1 Introduction This problem set is meant to accompany the undergraduate econometrics video series on youtube; covering

More information

Statistical Summaries. Kerala School of MathematicsCourse in Statistics for Scientists. Descriptive Statistics. Summary Statistics

Statistical Summaries. Kerala School of MathematicsCourse in Statistics for Scientists. Descriptive Statistics. Summary Statistics Kerala School of Mathematics Course in Statistics for Scientists Statistical Summaries Descriptive Statistics T.Krishnan Strand Life Sciences, Bangalore may be single numerical summaries of a batch, such

More information

Regression Analysis II

Regression Analysis II Regression Analysis II Lee D. Walker University of South Carolina e-mail: walker23@gwm.sc.edu COURSE OVERVIEW This course focuses on the theory, practice, and application of linear regression. As Agresti

More information

PRACTICAL STATISTICS FOR MEDICAL RESEARCH

PRACTICAL STATISTICS FOR MEDICAL RESEARCH PRACTICAL STATISTICS FOR MEDICAL RESEARCH Douglas G. Altman Head of Medical Statistical Laboratory Imperial Cancer Research Fund London CHAPMAN & HALL/CRC Boca Raton London New York Washington, D.C. Contents

More information

investigate. educate. inform.

investigate. educate. inform. investigate. educate. inform. Research Design What drives your research design? The battle between Qualitative and Quantitative is over Think before you leap What SHOULD drive your research design. Advanced

More information

CLASSICAL AND. MODERN REGRESSION WITH APPLICATIONS

CLASSICAL AND. MODERN REGRESSION WITH APPLICATIONS - CLASSICAL AND. MODERN REGRESSION WITH APPLICATIONS SECOND EDITION Raymond H. Myers Virginia Polytechnic Institute and State university 1 ~l~~l~l~~~~~~~l!~ ~~~~~l~/ll~~ Donated by Duxbury o Thomson Learning,,

More information

STATISTICS AND RESEARCH DESIGN

STATISTICS AND RESEARCH DESIGN Statistics 1 STATISTICS AND RESEARCH DESIGN These are subjects that are frequently confused. Both subjects often evoke student anxiety and avoidance. To further complicate matters, both areas appear have

More information

On Regression Analysis Using Bivariate Extreme Ranked Set Sampling

On Regression Analysis Using Bivariate Extreme Ranked Set Sampling On Regression Analysis Using Bivariate Extreme Ranked Set Sampling Atsu S. S. Dorvlo atsu@squ.edu.om Walid Abu-Dayyeh walidan@squ.edu.om Obaid Alsaidy obaidalsaidy@gmail.com Abstract- Many forms of ranked

More information

Title: New Perspectives on the Synthetic Control Method. Authors: Eli Ben-Michael, UC Berkeley,

Title: New Perspectives on the Synthetic Control Method. Authors: Eli Ben-Michael, UC Berkeley, Title: New Perspectives on the Synthetic Control Method Authors: Eli Ben-Michael, UC Berkeley, ebenmichael@berkeley.edu Avi Feller, UC Berkeley, afeller@berkeley.edu [presenting author] Jesse Rothstein,

More information

Statistical Techniques. Meta-Stat provides a wealth of statistical tools to help you examine your data. Overview

Statistical Techniques. Meta-Stat provides a wealth of statistical tools to help you examine your data. Overview 7 Applying Statistical Techniques Meta-Stat provides a wealth of statistical tools to help you examine your data. Overview... 137 Common Functions... 141 Selecting Variables to be Analyzed... 141 Deselecting

More information

Mark J. Anderson, Patrick J. Whitcomb Stat-Ease, Inc., Minneapolis, MN USA

Mark J. Anderson, Patrick J. Whitcomb Stat-Ease, Inc., Minneapolis, MN USA Journal of Statistical Science and Application (014) 85-9 D DAV I D PUBLISHING Practical Aspects for Designing Statistically Optimal Experiments Mark J. Anderson, Patrick J. Whitcomb Stat-Ease, Inc., Minneapolis,

More information

The Effects of Autocorrelated Noise and Biased HRF in fmri Analysis Error Rates

The Effects of Autocorrelated Noise and Biased HRF in fmri Analysis Error Rates The Effects of Autocorrelated Noise and Biased HRF in fmri Analysis Error Rates Ariana Anderson University of California, Los Angeles Departments of Psychiatry and Behavioral Sciences David Geffen School

More information

Chapter 7: Descriptive Statistics

Chapter 7: Descriptive Statistics Chapter Overview Chapter 7 provides an introduction to basic strategies for describing groups statistically. Statistical concepts around normal distributions are discussed. The statistical procedures of

More information

Statistical data preparation: management of missing values and outliers

Statistical data preparation: management of missing values and outliers KJA Korean Journal of Anesthesiology Statistical Round pissn 2005-6419 eissn 2005-7563 Statistical data preparation: management of missing values and outliers Sang Kyu Kwak 1 and Jong Hae Kim 2 Departments

More information

Kidane Tesfu Habtemariam, MASTAT, Principle of Stat Data Analysis Project work

Kidane Tesfu Habtemariam, MASTAT, Principle of Stat Data Analysis Project work 1 1. INTRODUCTION Food label tells the extent of calories contained in the food package. The number tells you the amount of energy in the food. People pay attention to calories because if you eat more

More information

You must answer question 1.

You must answer question 1. Research Methods and Statistics Specialty Area Exam October 28, 2015 Part I: Statistics Committee: Richard Williams (Chair), Elizabeth McClintock, Sarah Mustillo You must answer question 1. 1. Suppose

More information

An Introduction to Modern Econometrics Using Stata

An Introduction to Modern Econometrics Using Stata An Introduction to Modern Econometrics Using Stata CHRISTOPHER F. BAUM Department of Economics Boston College A Stata Press Publication StataCorp LP College Station, Texas Contents Illustrations Preface

More information

11/24/2017. Do not imply a cause-and-effect relationship

11/24/2017. Do not imply a cause-and-effect relationship Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are highly extraverted people less afraid of rejection

More information

Quantitative Data and Measurement. POLI 205 Doing Research in Politics. Fall 2015

Quantitative Data and Measurement. POLI 205 Doing Research in Politics. Fall 2015 Quantitative Fall 2015 Theory and We need to test our theories with empirical data Inference : Systematic observation and representation of concepts Quantitative: measures are numeric Qualitative: measures

More information

The Exposure-Stratified Retrospective Study: Application to High-Incidence Diseases

The Exposure-Stratified Retrospective Study: Application to High-Incidence Diseases The Exposure-Stratified Retrospective Study: Application to High-Incidence Diseases Peng T. Liu and Debra A. Street Division of Public Health and Biostatistics, CFSAN, FDA 5100 Paint Branch Pkwy, College

More information

IAPT: Regression. Regression analyses

IAPT: Regression. Regression analyses Regression analyses IAPT: Regression Regression is the rather strange name given to a set of methods for predicting one variable from another. The data shown in Table 1 and come from a student project

More information

The Impact of Melamine Spiking on the Gel Strength and Viscosity of Gelatin

The Impact of Melamine Spiking on the Gel Strength and Viscosity of Gelatin The Impact of Melamine Spiking on the and of atin Introduction The primary purpose of this research was to assess the impact of melamine spiking on the gel strength and viscosity of gelatin. A secondary

More information

Statistical Techniques. Masoud Mansoury and Anas Abulfaraj

Statistical Techniques. Masoud Mansoury and Anas Abulfaraj Statistical Techniques Masoud Mansoury and Anas Abulfaraj What is Statistics? https://www.youtube.com/watch?v=lmmzj7599pw The definition of Statistics The practice or science of collecting and analyzing

More information

1 Simple and Multiple Linear Regression Assumptions

1 Simple and Multiple Linear Regression Assumptions 1 Simple and Multiple Linear Regression Assumptions The assumptions for simple are in fact special cases of the assumptions for multiple: Check: 1. What is external validity? Which assumption is critical

More information

Unit 1 Exploring and Understanding Data

Unit 1 Exploring and Understanding Data Unit 1 Exploring and Understanding Data Area Principle Bar Chart Boxplot Conditional Distribution Dotplot Empirical Rule Five Number Summary Frequency Distribution Frequency Polygon Histogram Interquartile

More information