Experiments! Benjamin Graham

Size: px
Start display at page:

Download "Experiments! Benjamin Graham"

Transcription

1 Experiments! Benjamin Graham IR 211: Lecture 15 Benjamin Graham

2 Internal vs. External Validity Internal Validity: What was the effect of this particular treatment on these particular subjects? External Validity: About what population can we make a valid inference based on the results we observe?

3 Establishing the Counterfactual What would the world look like if we changed the value of our independent variable of interest, but held everything else constant? Introducing the planet Htrae! Independent variable of interest: Dummy variable for good or evil. We hold everything else constant, including super-hero powers.

4 Randomized Control Trials: The next best thing to a parallel universe We need things ceteris paribus. With randomization, treatment group is identical to the control group. Extraneous (omitted) variables have the same value in each group. The differences are random selection error. We calculate the size of the errors. No causation without manipulation Observational data vs. Experimental data Experiments have awesome internal validity Their external validity depends on sampling and context.

5 Experiments (AKA RCTs) True experiments have these three things: 1. At least two groups (treatment and control) Treatment group gets the drug, control group gets the sugar pill (the placebo) 2. Variation in the independent variable that precedes measurement of the dependent variable We give them the drug BEFORE we measure whether they got better 3. Random assignment between treatment and control (or between levels of treatment) This is how we know that the only difference between the two control groups is whether they got the treatment or not. If we really want to make good causal inference we also need to: Understand the causal mechanism Make sure the context of the experiment matches the context of the population we re making an inference about.

6 Random Sampling vs. Random Assignment Random Sampling: Makes sure our sample looks like a miniature version of our population All about external validity: the population you sample from is the population you can make inferences about. Random Assignment: Makes sure our control group looks just like our treatment group Think parallel universes: everything is the same in these two groups except for the treatment itself All about internal validity: Random assignment solves the extraneous variable problem.

7 Clicker Question Is this random assignment or random sampling? Treatment Group Whole Population Our Sample Control Group A. Random Sampling B. Random Assignment

8 Clicker Question Treatment Group Whole Population Our Sample Is this random assignment or random sampling? Control Group A. Random Sampling B. Random Assignment

9 Pretest and Posttest Pretest Treatment Group Treatment Posttest Pretest Control Group No Treatment Posttest

10 Pretest and Posttest Pretest Treatment Group Treatment Posttest Should these scores be the same or different? Pretest Control Group No Treatment Posttest

11 Pretest and Posttest Pretest Treatment Group Treatment Posttest If these scores are different, what does that tell us? Pretest Control Group No Treatment Posttest

12 An IPE Example Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the country-pairs in the world-- a census rather than a sample

13 Clicker Question Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the country-pairs in the world-- a census rather than a sample What is the null hypothesis? 1. Bilateral investment treaties have either no effect or a negative effect on bilateral FDI flows. 2. Bilateral FDI flows increase the likelihood of signing a bilateral investment treaty.

14 Clicker Question Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the country-pairs in the world-- a census rather than a sample What is our unit of analysis? A. The country B. The dyad (i.e. country pair) C. The world D. The individual

15 Clicker Question Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the dyads (country-pairs) in the world-- a census rather than a sample What is our treatment? 1. Bilateral flows of foreign direct investment 2. Having a bilateral investment treaty in place or not 3. Economic Growth 4. Regime Type 5. The country-pair

16 Clicker Question Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the dyads (country-pairs) in the world-- a census rather than a sample What is our dependent variable? 1. Bilateral flows of foreign direct investment 2. Having a bilateral investment treaty in place or not 3. Economic Growth 4. Regime Type 5. The country-pair

17 Clicker Question Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the dyads (country-pairs) in the world-- a census rather than a sample If we ran a true experiment, how would we decide who is in the treatment group and who is in the control group? 1. We could pick 10 rich countries and have them sign a treaty with 10 poor countries 2. Random Assignment: We would randomly assign country parents to either treatment or control. We would force the treatment country pairs to sign a bilateral investment treaty, and tell the control pairs that they can t sign a treaty. 3. Allow countries to sign treaties with whomever they like.

18 Not A Clicker Question Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the dyads (country-pairs) in the world-- a census rather than a sample

19 Clicker Question Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the country-pairs in the world-- a census rather than a sample 1.If we use random assignment, and then we give a pretest, should the scores be the same between the treatment and control groups? 1. Yes 2. No

20 Another Example Hypothesis: Signing a bilateral investment treaty increases bilateral flows of foreign direct investment. Selection: Lets use all the country-pairs in the world-- a census rather than a sample 1.When we give the posttest, what kind of results would lead me to fail to reject the null hypothesis? 1. More FDI flows in treatment pairs 2. Less FDI flows in treatment pairs 3. The same FDI flows in both treatment and control pairs 4. 1&3 5. 2&3

21 Dealing with context How much is our lab like the real world? What kind of validity does this effect?

Math 361. Day 8 Binomial Random Variables pages 27 and 28 Inv Do you have ESP? Inv. 1.3 Tim or Bob?

Math 361. Day 8 Binomial Random Variables pages 27 and 28 Inv Do you have ESP? Inv. 1.3 Tim or Bob? Math 361 Day 8 Binomial Random Variables pages 27 and 28 Inv. 1.2 - Do you have ESP? Inv. 1.3 Tim or Bob? Inv. 1.1: Friend or Foe Review Is a particular study result consistent with the null model? Learning

More information

How to Get $35,000 (By Improving Your Credit Score)

How to Get $35,000 (By Improving Your Credit Score) 1 How to Get $35,000 (By Improving Your Credit Score) EMAIL I JUST GOT Hi! I have been following you for years. I been here before the Basic Box and the Super 6 programs. Let me tell you that it has been

More information

Universidade NOVA de Lisboa Faculdade de Economia

Universidade NOVA de Lisboa Faculdade de Economia Universidade NOVA de Lisboa Faculdade de Economia 2009/2010 Principles of Econometrics Introduction 1 Textbook Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach, 3 rd Edition, Thomson

More information

Value (x) probability Example A-2: Construct a histogram for population Ψ.

Value (x) probability Example A-2: Construct a histogram for population Ψ. Calculus 111, section 08.x The Central Limit Theorem notes by Tim Pilachowski If you haven t done it yet, go to the Math 111 page and download the handout: Central Limit Theorem supplement. Today s lecture

More information

Homework: (Due Wed) Chapter 10: #5, 22, 42

Homework: (Due Wed) Chapter 10: #5, 22, 42 Announcements: Discussion today is review for midterm, no credit. You may attend more than one discussion section. Bring 2 sheets of notes and calculator to midterm. We will provide Scantron form. Homework:

More information

Chapter 11: Inference for Distributions Inference for Means of a Population 11.2 Comparing Two Means

Chapter 11: Inference for Distributions Inference for Means of a Population 11.2 Comparing Two Means Chapter 11: Inference for Distributions 11.1 Inference for Means of a Population 11.2 Comparing Two Means 1 Population Standard Deviation In the previous chapter, we computed confidence intervals and performed

More information

Lecture 21: Logit Models for Multinomial Responses Continued

Lecture 21: Logit Models for Multinomial Responses Continued Lecture 21: Logit Models for Multinomial Responses Continued Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University

More information

HOMEWORK: Due Mon 11/8, Chapter 9: #15, 25, 37, 44

HOMEWORK: Due Mon 11/8, Chapter 9: #15, 25, 37, 44 This week: Chapter 9 (will do 9.6 to 9.8 later, with Chap. 11) Understanding Sampling Distributions: Statistics as Random Variables ANNOUNCEMENTS: Shandong Min will give the lecture on Friday. See website

More information

a. Explain why the coefficients change in the observed direction when switching from OLS to Tobit estimation.

a. Explain why the coefficients change in the observed direction when switching from OLS to Tobit estimation. 1. Using data from IRS Form 5500 filings by U.S. pension plans, I estimated a model of contributions to pension plans as ln(1 + c i ) = α 0 + U i α 1 + PD i α 2 + e i Where the subscript i indicates the

More information

RANDOMIZED TRIALS Technical Track Session II Sergio Urzua University of Maryland

RANDOMIZED TRIALS Technical Track Session II Sergio Urzua University of Maryland RANDOMIZED TRIALS Technical Track Session II Sergio Urzua University of Maryland Randomized trials o Evidence about counterfactuals often generated by randomized trials or experiments o Medical trials

More information

Econ Spring 2016 Section 12

Econ Spring 2016 Section 12 Econ 140 - Spring 2016 Section 12 GSI: Fenella Carpena April 28, 2016 1 Experiments and Quasi-Experiments Exercise 1.0. Consider the STAR Experiment discussed in lecture where students were randomly assigned

More information

Evaluation of Public Policy

Evaluation of Public Policy Università degli Studi di Ferrara a.a. 2017-2018 The main objective of this course is to evaluate the effect of Public Policy changes on the budget of public entities. Effect of changes in electoral rules

More information

Equivalence Tests for the Difference of Two Proportions in a Cluster- Randomized Design

Equivalence Tests for the Difference of Two Proportions in a Cluster- Randomized Design Chapter 240 Equivalence Tests for the Difference of Two Proportions in a Cluster- Randomized Design Introduction This module provides power analysis and sample size calculation for equivalence tests of

More information

Data Analysis and Statistical Methods Statistics 651

Data Analysis and Statistical Methods Statistics 651 Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 7 (MWF) Analyzing the sums of binary outcomes Suhasini Subba Rao Introduction Lecture 7 (MWF)

More information

Chapter 10 Estimating Proportions with Confidence

Chapter 10 Estimating Proportions with Confidence Chapter 10 Estimating Proportions with Confidence Copyright 2011 Brooks/Cole, Cengage Learning Principle Idea: Confidence interval: an interval of estimates that is likely to capture the population value.

More information

Planning Sample Size for Randomized Evaluations Esther Duflo J-PAL

Planning Sample Size for Randomized Evaluations Esther Duflo J-PAL Planning Sample Size for Randomized Evaluations Esther Duflo J-PAL povertyactionlab.org Planning Sample Size for Randomized Evaluations General question: How large does the sample need to be to credibly

More information

Independent-Samples t Test

Independent-Samples t Test Chapter 14 Aplia week 8 (Two independent samples) Testing hypotheses about means of two populations naturally occurring populations introverts vs. extroverts neuroticism experimentally defined (random

More information

Review questions for Multinomial Logit/Probit, Tobit, Heckit, Quantile Regressions

Review questions for Multinomial Logit/Probit, Tobit, Heckit, Quantile Regressions 1. I estimated a multinomial logit model of employment behavior using data from the 2006 Current Population Survey. The three possible outcomes for a person are employed (outcome=1), unemployed (outcome=2)

More information

STA2601. Tutorial letter 105/2/2018. Applied Statistics II. Semester 2. Department of Statistics STA2601/105/2/2018 TRIAL EXAMINATION PAPER

STA2601. Tutorial letter 105/2/2018. Applied Statistics II. Semester 2. Department of Statistics STA2601/105/2/2018 TRIAL EXAMINATION PAPER STA2601/105/2/2018 Tutorial letter 105/2/2018 Applied Statistics II STA2601 Semester 2 Department of Statistics TRIAL EXAMINATION PAPER Define tomorrow. university of south africa Dear Student Congratulations

More information

PASS Sample Size Software

PASS Sample Size Software Chapter 850 Introduction Cox proportional hazards regression models the relationship between the hazard function λ( t X ) time and k covariates using the following formula λ log λ ( t X ) ( t) 0 = β1 X1

More information

Probability Binomial Distributions. SOCY601 Alan Neustadtl

Probability Binomial Distributions. SOCY601 Alan Neustadtl Probability Binomial Distributions SOCY601 Alan Neustadtl In the 1870s, Sir Francis Galton created a device he called a quincunx for studying probability. The device was made up of a vertical board with

More information

Fall 2004 Social Sciences 7418 University of Wisconsin-Madison Problem Set 5 Answers

Fall 2004 Social Sciences 7418 University of Wisconsin-Madison Problem Set 5 Answers Economics 310 Menzie D. Chinn Fall 2004 Social Sciences 7418 University of Wisconsin-Madison Problem Set 5 Answers This problem set is due in lecture on Wednesday, December 15th. No late problem sets will

More information

LABOR SUPPLY RESPONSES TO TAXES AND TRANSFERS: PART I (BASIC APPROACHES) Henrik Jacobsen Kleven London School of Economics

LABOR SUPPLY RESPONSES TO TAXES AND TRANSFERS: PART I (BASIC APPROACHES) Henrik Jacobsen Kleven London School of Economics LABOR SUPPLY RESPONSES TO TAXES AND TRANSFERS: PART I (BASIC APPROACHES) Henrik Jacobsen Kleven London School of Economics Lecture Notes for MSc Public Finance (EC426): Lent 2013 AGENDA Efficiency cost

More information

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 - Would the correlation between x and y in the table above be positive or negative? The correlation is negative. -

More information

Two-Sample Z-Tests Assuming Equal Variance

Two-Sample Z-Tests Assuming Equal Variance Chapter 426 Two-Sample Z-Tests Assuming Equal Variance Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample z-tests when the variances of the two groups

More information

Lecture 39 Section 11.5

Lecture 39 Section 11.5 on Lecture 39 Section 11.5 Hampden-Sydney College Mon, Nov 10, 2008 Outline 1 on 2 3 on 4 on Exercise 11.27, page 715. A researcher was interested in comparing body weights for two strains of laboratory

More information

STAT Lab#5 Binomial Distribution & Midterm Review

STAT Lab#5 Binomial Distribution & Midterm Review STAT 22000 Lab# Binomial Distribution & Midterm Review Binomial Distributions For X Bin(n, p), Assumptions: P (X = k) = n p k (1 p) n k k Only two possible outcomes The number of trials n must be fixed

More information

Chapter 7: Estimation Sections

Chapter 7: Estimation Sections 1 / 31 : Estimation Sections 7.1 Statistical Inference Bayesian Methods: 7.2 Prior and Posterior Distributions 7.3 Conjugate Prior Distributions 7.4 Bayes Estimators Frequentist Methods: 7.5 Maximum Likelihood

More information

DIME WORKSHOP OCTOBER 13-17, 2014 LISBON, PORTUGAL

DIME WORKSHOP OCTOBER 13-17, 2014 LISBON, PORTUGAL DIME WORKSHOP OCTOBER 13-17, 2014 LISBON, PORTUGAL Impact Evaluation Workshop Experimental Methods Daniel Stein Economist, DIME 16 20 June 2014 Kigali, Rwanda What we will learn... Impact Evaluation looks

More information

Economics 345 Applied Econometrics

Economics 345 Applied Econometrics Economics 345 Applied Econometrics Problem Set 4--Solutions Prof: Martin Farnham Problem sets in this course are ungraded. An answer key will be posted on the course website within a few days of the release

More information

Name: CS3130: Probability and Statistics for Engineers Practice Final Exam Instructions: You may use any notes that you like, but no calculators or computers are allowed. Be sure to show all of your work.

More information

Chapter 9 Chapter Friday, June 4 th

Chapter 9 Chapter Friday, June 4 th Chapter 9 Chapter 10 Sections 9.1 9.5 and 10.1 10.5 Friday, June 4 th Parameter and Statisticti ti Parameter is a number that is a summary characteristic of a population Statistic, is a number that is

More information

Final/Exam #3 Form B - Statistics 211 (Fall 1999)

Final/Exam #3 Form B - Statistics 211 (Fall 1999) Final/Exam #3 Form B - Statistics 211 (Fall 1999) This test consists of nine numbered pages. Make sure you have all 9 pages. It is your responsibility to inform me if a page is missing!!! You have at least

More information

Nonparametric Statistics Notes

Nonparametric Statistics Notes Nonparametric Statistics Notes Chapter 3: Some Tests Based on the Binomial Distribution Jesse Crawford Department of Mathematics Tarleton State University (Tarleton State University) Ch 3: Tests Based

More information

Superiority by a Margin Tests for the Ratio of Two Proportions

Superiority by a Margin Tests for the Ratio of Two Proportions Chapter 06 Superiority by a Margin Tests for the Ratio of Two Proportions Introduction This module computes power and sample size for hypothesis tests for superiority of the ratio of two independent proportions.

More information

CH 5 Normal Probability Distributions Properties of the Normal Distribution

CH 5 Normal Probability Distributions Properties of the Normal Distribution Properties of the Normal Distribution Example A friend that is always late. Let X represent the amount of minutes that pass from the moment you are suppose to meet your friend until the moment your friend

More information

Entrance Exam Wiskunde A

Entrance Exam Wiskunde A CENTRALE COMMISSIE VOORTENTAMEN WISKUNDE Entrance Exam Wiskunde A Date: 19 December 2018 Time: 13.30 16.30 hours Questions: 6 Please read the instructions below carefully before answering the questions.

More information

Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency Programs: Issues and Recommendations

Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency Programs: Issues and Recommendations Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency Programs: Issues and Recommendations November 13, 2012 Michael Li U.S. Department of Energy Annika Todd

More information

STA Module 3B Discrete Random Variables

STA Module 3B Discrete Random Variables STA 2023 Module 3B Discrete Random Variables Learning Objectives Upon completing this module, you should be able to 1. Determine the probability distribution of a discrete random variable. 2. Construct

More information

The Marginal Propensity to Consume Out of Credit. Lorenz Kueng

The Marginal Propensity to Consume Out of Credit. Lorenz Kueng Discussion of Aydin (2017) The Marginal Propensity to Consume Out of Credit Lorenz Kueng Northwestern University and NBER Very interesting paper! Lots to think about. I applaud Deniz - for getting access

More information

The Two-Sample Independent Sample t Test

The Two-Sample Independent Sample t Test Department of Psychology and Human Development Vanderbilt University 1 Introduction 2 3 The General Formula The Equal-n Formula 4 5 6 Independence Normality Homogeneity of Variances 7 Non-Normality Unequal

More information

INFERENTIAL STATISTICS REVISION

INFERENTIAL STATISTICS REVISION INFERENTIAL STATISTICS REVISION PREMIUM VERSION PREVIEW WWW.MATHSPOINTS.IE/SIGN-UP/ 2016 LCHL Paper 2 Question 9 (a) (i) Data on earnings were published for a particular country. The data showed that the

More information

DIFFERENCE DIFFERENCES

DIFFERENCE DIFFERENCES DIFFERENCE IN DIFFERENCES & PANEL DATA Technical Track Session III Céline Ferré The World Bank Structure of this session 1 When do we use Differences-in- Differences? (Diff-in-Diff or DD) 2 Estimation

More information

Chapter 7: Estimation Sections

Chapter 7: Estimation Sections 1 / 40 Chapter 7: Estimation Sections 7.1 Statistical Inference Bayesian Methods: Chapter 7 7.2 Prior and Posterior Distributions 7.3 Conjugate Prior Distributions 7.4 Bayes Estimators Frequentist Methods:

More information

Quasi-Experimental Methods. Technical Track

Quasi-Experimental Methods. Technical Track Quasi-Experimental Methods Technical Track East Asia Regional Impact Evaluation Workshop Seoul, South Korea Joost de Laat, World Bank Randomized Assignment IE Methods Toolbox Discontinuity Design Difference-in-

More information

ECON FINANCIAL ECONOMICS

ECON FINANCIAL ECONOMICS ECON 337901 FINANCIAL ECONOMICS Peter Ireland Boston College February 19, 2019 These lecture notes by Peter Ireland are licensed under a Creative Commons Attribution-NonCommerical-ShareAlike 4.0 International

More information

STA Rev. F Learning Objectives. What is a Random Variable? Module 5 Discrete Random Variables

STA Rev. F Learning Objectives. What is a Random Variable? Module 5 Discrete Random Variables STA 2023 Module 5 Discrete Random Variables Learning Objectives Upon completing this module, you should be able to: 1. Determine the probability distribution of a discrete random variable. 2. Construct

More information

(# of die rolls that satisfy the criteria) (# of possible die rolls)

(# of die rolls that satisfy the criteria) (# of possible die rolls) BMI 713: Computational Statistics for Biomedical Sciences Assignment 2 1 Random variables and distributions 1. Assume that a die is fair, i.e. if the die is rolled once, the probability of getting each

More information

Mendelian Randomization with a Binary Outcome

Mendelian Randomization with a Binary Outcome Chapter 851 Mendelian Randomization with a Binary Outcome Introduction This module computes the sample size and power of the causal effect in Mendelian randomization studies with a binary outcome. This

More information

9. Logit and Probit Models For Dichotomous Data

9. Logit and Probit Models For Dichotomous Data Sociology 740 John Fox Lecture Notes 9. Logit and Probit Models For Dichotomous Data Copyright 2014 by John Fox Logit and Probit Models for Dichotomous Responses 1 1. Goals: I To show how models similar

More information

Review of the Topics for Midterm I

Review of the Topics for Midterm I Review of the Topics for Midterm I STA 100 Lecture 9 I. Introduction The objective of statistics is to make inferences about a population based on information contained in a sample. A population is the

More information

PubPol 201. Module 1: International Trade Policy. Class 3 Trade Deficits; Currency Manipulation

PubPol 201. Module 1: International Trade Policy. Class 3 Trade Deficits; Currency Manipulation PubPol 201 Module 1: International Trade Policy Class 3 Trade Deficits; Currency Manipulation Class 3 Outline Trade Deficits; Currency Manipulation Trade deficits Definitions What they do and do not mean

More information

Using Randomized Evaluations to Improve Policy

Using Randomized Evaluations to Improve Policy Daniel Stein (DIME) Using Randomized Evaluations to Improve Policy Development Impact Evaluation Initiative innovations & solutions in infrastructure, agriculture & environment naivasha, april 23-27, 2011

More information

Theory vs Practice of Mastery Learning in the Cognitive Tutor: Principal Stratification on a Latent Variable

Theory vs Practice of Mastery Learning in the Cognitive Tutor: Principal Stratification on a Latent Variable Theory vs Practice of Mastery Learning in the Cognitive Tutor: Principal Stratification on a Latent Variable Adam C Sales John F Pane University of Texas College of Education RAND Corporation SREE 3/1/2018

More information

* Source:

* Source: Problem: A recent report from Gallup stated that most teachers don t want to be armed in school. Gallup asked K-12 teachers if they would be willing to be trained so they could carry a gun at school. Eighteen

More information

12.1 One-Way Analysis of Variance. ANOVA - analysis of variance - used to compare the means of several populations.

12.1 One-Way Analysis of Variance. ANOVA - analysis of variance - used to compare the means of several populations. 12.1 One-Way Analysis of Variance ANOVA - analysis of variance - used to compare the means of several populations. Assumptions for One-Way ANOVA: 1. Independent samples are taken using a randomized design.

More information

MLLunsford 1. Activity: Central Limit Theorem Theory and Computations

MLLunsford 1. Activity: Central Limit Theorem Theory and Computations MLLunsford 1 Activity: Central Limit Theorem Theory and Computations Concepts: The Central Limit Theorem; computations using the Central Limit Theorem. Prerequisites: The student should be familiar with

More information

Normal Table Gymnastics

Normal Table Gymnastics Overview Normal Table Gymnastics Dr Tom Ilvento Department of Food and Resource Economics Let s continue working with the normal table And I will show you how to do some table gymnastics to solve for:

More information

Lecture 18. Ingo Ruczinski. October 31, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University

Lecture 18. Ingo Ruczinski. October 31, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University Lecture 18 Department of Bios Johns Hopkins Bloomberg School of Public Health Johns Hopkins University October 31, 2015 1 2 3 4 5 6 1 Tests for a binomial proportion 2 Score test versus Wald 3 Exact binomial

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the problem.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the problem. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or Solve the problem. 1. Find forα=0.01. A. 1.96 B. 2.575 C. 1.645 D. 2.33 2.Whatistheconfidencelevelofthefolowingconfidenceintervalforμ?

More information

Equivalence Tests for Two Correlated Proportions

Equivalence Tests for Two Correlated Proportions Chapter 165 Equivalence Tests for Two Correlated Proportions Introduction The two procedures described in this chapter compute power and sample size for testing equivalence using differences or ratios

More information

Chapter 5. Sampling Distributions

Chapter 5. Sampling Distributions Lecture notes, Lang Wu, UBC 1 Chapter 5. Sampling Distributions 5.1. Introduction In statistical inference, we attempt to estimate an unknown population characteristic, such as the population mean, µ,

More information

The mathematical definitions are given on screen.

The mathematical definitions are given on screen. Text Lecture 3.3 Coherent measures of risk and back- testing Dear all, welcome back. In this class we will discuss one of the main drawbacks of Value- at- Risk, that is to say the fact that the VaR, as

More information

6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013 Transcript Lecture 23

6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013 Transcript Lecture 23 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013 Transcript Lecture 23 The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare

More information

Tests for Two Variances

Tests for Two Variances Chapter 655 Tests for Two Variances Introduction Occasionally, researchers are interested in comparing the variances (or standard deviations) of two groups rather than their means. This module calculates

More information

Stat 139 Homework 2 Solutions, Fall 2016

Stat 139 Homework 2 Solutions, Fall 2016 Stat 139 Homework 2 Solutions, Fall 2016 Problem 1. The sum of squares of a sample of data is minimized when the sample mean, X = Xi /n, is used as the basis of the calculation. Define g(c) as a function

More information

Sampling and sampling distribution

Sampling and sampling distribution Sampling and sampling distribution September 12, 2017 STAT 101 Class 5 Slide 1 Outline of Topics 1 Sampling 2 Sampling distribution of a mean 3 Sampling distribution of a proportion STAT 101 Class 5 Slide

More information

Econometrics and Economic Data

Econometrics and Economic Data Econometrics and Economic Data Chapter 1 What is a regression? By using the regression model, we can evaluate the magnitude of change in one variable due to a certain change in another variable. For example,

More information

STAT 1220 FALL 2010 Common Final Exam December 10, 2010

STAT 1220 FALL 2010 Common Final Exam December 10, 2010 STAT 1220 FALL 2010 Common Final Exam December 10, 2010 PLEASE PRINT THE FOLLOWING INFORMATION: Name: Instructor: Student ID #: Section/Time: THIS EXAM HAS TWO PARTS. PART I. Part I consists of 30 multiple

More information

Non-Inferiority Tests for the Odds Ratio of Two Proportions

Non-Inferiority Tests for the Odds Ratio of Two Proportions Chapter Non-Inferiority Tests for the Odds Ratio of Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority tests of the odds ratio in twosample

More information

1. Variability in estimates and CLT

1. Variability in estimates and CLT Unit3: Foundationsforinference 1. Variability in estimates and CLT Sta 101 - Fall 2015 Duke University, Department of Statistical Science Dr. Çetinkaya-Rundel Slides posted at http://bit.ly/sta101_f15

More information

Non-Inferiority Tests for the Ratio of Two Means

Non-Inferiority Tests for the Ratio of Two Means Chapter 455 Non-Inferiority Tests for the Ratio of Two Means Introduction This procedure calculates power and sample size for non-inferiority t-tests from a parallel-groups design in which the logarithm

More information

Molecular Phylogenetics

Molecular Phylogenetics Mole_Oce Lecture # 16: Molecular Phylogenetics Maximum Likelihood & Bahesian Statistics Optimality criterion: a rule used to decide which of two trees is best. Four optimality criteria are currently widely

More information

TAXES, TRANSFERS, AND LABOR SUPPLY. Henrik Jacobsen Kleven London School of Economics. Lecture Notes for PhD Public Finance (EC426): Lent Term 2012

TAXES, TRANSFERS, AND LABOR SUPPLY. Henrik Jacobsen Kleven London School of Economics. Lecture Notes for PhD Public Finance (EC426): Lent Term 2012 TAXES, TRANSFERS, AND LABOR SUPPLY Henrik Jacobsen Kleven London School of Economics Lecture Notes for PhD Public Finance (EC426): Lent Term 2012 AGENDA Why care about labor supply responses to taxes and

More information

Study of one-way ANOVA with a fixed-effect factor

Study of one-way ANOVA with a fixed-effect factor Study of one-way ANOVA with a fixed-effect factor In the last blog on Introduction to ANOVA, we mentioned that in the oneway ANOVA study, the factor contributing to a possible source of variation that

More information

5-1 pg ,4,5, EOO,39,47,50,53, pg ,5,9,13,17,19,21,22,25,30,31,32, pg.269 1,29,13,16,17,19,20,25,26,28,31,33,38

5-1 pg ,4,5, EOO,39,47,50,53, pg ,5,9,13,17,19,21,22,25,30,31,32, pg.269 1,29,13,16,17,19,20,25,26,28,31,33,38 5-1 pg. 242 3,4,5, 17-37 EOO,39,47,50,53,56 5-2 pg. 249 9,10,13,14,17,18 5-3 pg. 257 1,5,9,13,17,19,21,22,25,30,31,32,34 5-4 pg.269 1,29,13,16,17,19,20,25,26,28,31,33,38 5-5 pg. 281 5-14,16,19,21,22,25,26,30

More information

Every data set has an average and a standard deviation, given by the following formulas,

Every data set has an average and a standard deviation, given by the following formulas, Discrete Data Sets A data set is any collection of data. For example, the set of test scores on the class s first test would comprise a data set. If we collect a sample from the population we are interested

More information

FIGURE A1.1. Differences for First Mover Cutoffs (Round one to two) as a Function of Beliefs on Others Cutoffs. Second Mover Round 1 Cutoff.

FIGURE A1.1. Differences for First Mover Cutoffs (Round one to two) as a Function of Beliefs on Others Cutoffs. Second Mover Round 1 Cutoff. APPENDIX A. SUPPLEMENTARY TABLES AND FIGURES A.1. Invariance to quantitative beliefs. Figure A1.1 shows the effect of the cutoffs in round one for the second and third mover on the best-response cutoffs

More information

Algebra Success. LESSON 14: Discovering y = mx + b

Algebra Success. LESSON 14: Discovering y = mx + b T282 Algebra Success [OBJECTIVE] The student will determine the slope and y-intercept of a line by examining the equation for the line written in slope-intercept form. [MATERIALS] Student pages S7 S Transparencies

More information

Expectations & Randomization Normal Form Games Dominance Iterated Dominance. Normal Form Games & Dominance

Expectations & Randomization Normal Form Games Dominance Iterated Dominance. Normal Form Games & Dominance Normal Form Games & Dominance Let s play the quarters game again We each have a quarter. Let s put them down on the desk at the same time. If they show the same side (HH or TT), you take my quarter. If

More information

Discussion of Limited Partners and the LB0 Process by Paul Schultz and Sophie Shive

Discussion of Limited Partners and the LB0 Process by Paul Schultz and Sophie Shive Discussion of Limited Partners and the LB0 Process by Paul Schultz and Sophie Shive Discussion by Adair Morse University of California, Berkeley Southern California Private Equity Conference 2017 Overview

More information

Determining the Quantity Demanded of an Asset

Determining the Quantity Demanded of an Asset Determining the Quantity Demanded of an Asset Wealth the total resources owned by the individual, including all assets Expected Return the return expected over the next period on one asset relative to

More information

Empirical Methods for Corporate Finance. Panel Data, Fixed Effects, and Standard Errors

Empirical Methods for Corporate Finance. Panel Data, Fixed Effects, and Standard Errors Empirical Methods for Corporate Finance Panel Data, Fixed Effects, and Standard Errors The use of panel datasets Source: Bowen, Fresard, and Taillard (2014) 4/20/2015 2 The use of panel datasets Source:

More information

Planning Sample Size for Randomized Evaluations

Planning Sample Size for Randomized Evaluations Planning Sample Size for Randomized Evaluations Jed Friedman, World Bank SIEF Regional Impact Evaluation Workshop Beijing, China July 2009 Adapted from slides by Esther Duflo, J-PAL Planning Sample Size

More information

Supplementary Appendix Punishment strategies in repeated games: Evidence from experimental markets

Supplementary Appendix Punishment strategies in repeated games: Evidence from experimental markets Supplementary Appendix Punishment strategies in repeated games: Evidence from experimental markets Julian Wright May 13 1 Introduction This supplementary appendix provides further details, results and

More information

LECTURE 11 Monetary Policy at the Zero Lower Bound: Quantitative Easing. November 2, 2016

LECTURE 11 Monetary Policy at the Zero Lower Bound: Quantitative Easing. November 2, 2016 Economics 210c/236a Fall 2016 Christina Romer David Romer LECTURE 11 Monetary Policy at the Zero Lower Bound: Quantitative Easing November 2, 2016 I. OVERVIEW Monetary Policy at the Zero Lower Bound: Expectations

More information

22S:105 Statistical Methods and Computing. Two independent sample problems. Goal of inference: to compare the characteristics of two different

22S:105 Statistical Methods and Computing. Two independent sample problems. Goal of inference: to compare the characteristics of two different 22S:105 Statistical Methods and Computing Two independent-sample t-tests Lecture 17 Apr. 5, 2013 1 2 Two independent sample problems Goal of inference: to compare the characteristics of two different populations

More information

ECONOMIC DEVELOPMENT. K. Harris: Candidate M.A. Applied Economic Analysis

ECONOMIC DEVELOPMENT. K. Harris: Candidate M.A. Applied Economic Analysis ECONOMIC DEVELOPMENT K. Harris: Candidate M.A. Applied Economic Analysis Introduction PRESENTATION Section 1 Data Driven Section II Replication Section III - Extension Section IV Summary Section V Food

More information

Binomial distribution

Binomial distribution Binomial distribution Jon Michael Gran Department of Biostatistics, UiO MF9130 Introductory course in statistics Tuesday 24.05.2010 1 / 28 Overview Binomial distribution (Aalen chapter 4, Kirkwood and

More information

(Negative Frame Subjects' Instructions) INSTRUCTIONS WELCOME.

(Negative Frame Subjects' Instructions) INSTRUCTIONS WELCOME. (Negative Frame Subjects' Instructions) INSTRUCTIONS WELCOME. This experiment is a study of group and individual investment behavior. The instructions are simple. If you follow them carefully and make

More information

Statistical Models of Stocks and Bonds. Zachary D Easterling: Department of Economics. The University of Akron

Statistical Models of Stocks and Bonds. Zachary D Easterling: Department of Economics. The University of Akron Statistical Models of Stocks and Bonds Zachary D Easterling: Department of Economics The University of Akron Abstract One of the key ideas in monetary economics is that the prices of investments tend to

More information

EconS Supply and Demand

EconS Supply and Demand EconS 305 - Supply and Demand Eric Dunaway Washington State University eric.dunaway@wsu.edu August 28, 2015 Eric Dunaway (WSU) EconS 305 - Lecture 2 August 28, 2015 1 / 54 Introduction When people talk

More information

Encouraging fund choice in KiwiSaver

Encouraging fund choice in KiwiSaver 8 June 2017 Encouraging fund choice in KiwiSaver This report is for KiwiSaver providers and for providers of any Managed Investment Scheme. It will also be of interest to behavioural insight researchers.

More information

HUDM4122 Probability and Statistical Inference. March 4, 2015

HUDM4122 Probability and Statistical Inference. March 4, 2015 HUDM4122 Probability and Statistical Inference March 4, 2015 First things first The Exam Due to Monday s class cancellation Today s lecture on the Normal Distribution will not be covered on the Midterm

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distribution Patrick Breheny February 16 Patrick Breheny STA 580: Biostatistics I 1/38 Random variables The Binomial Distribution Random variables The binomial coefficients The binomial distribution

More information

Chapter 27: More Tests for Averages

Chapter 27: More Tests for Averages Chapter 27: More Tests for Averages If we have two independent simple random samples from two populations, the SE for the difference between the sample percentages is SE diff % = the SE for the difference

More information

MATH 112 Section 7.3: Understanding Chance

MATH 112 Section 7.3: Understanding Chance MATH 112 Section 7.3: Understanding Chance Prof. Jonathan Duncan Walla Walla University Autumn Quarter, 2007 Outline 1 Introduction to Probability 2 Theoretical vs. Experimental Probability 3 Advanced

More information

Non-Inferiority Tests for the Ratio of Two Proportions

Non-Inferiority Tests for the Ratio of Two Proportions Chapter Non-Inferiority Tests for the Ratio of Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority tests of the ratio in twosample designs in

More information

ONLINE APPENDIX (NOT FOR PUBLICATION) Appendix A: Appendix Figures and Tables

ONLINE APPENDIX (NOT FOR PUBLICATION) Appendix A: Appendix Figures and Tables ONLINE APPENDIX (NOT FOR PUBLICATION) Appendix A: Appendix Figures and Tables 34 Figure A.1: First Page of the Standard Layout 35 Figure A.2: Second Page of the Credit Card Statement 36 Figure A.3: First

More information

C.10 Exercises. Y* =!1 + Yz

C.10 Exercises. Y* =!1 + Yz C.10 Exercises C.I Suppose Y I, Y,, Y N is a random sample from a population with mean fj. and variance 0'. Rather than using all N observations consider an easy estimator of fj. that uses only the first

More information