Discrete Dependent Variables Linear Regression, Dummy Variables If discrete dependent variable: need new technique Examples: Firm join Energy Star or not.

Slides:

Advertisements

Similar presentations

Dummy Dependent variable Models

Advertisements

Qualitative and Limited Dependent Variable Models Chapter 18.

Radiation Benefit and Risk Assessment ©Health Physics Society.

Probit The two most common error specifications yield the logit and probit models. The probit model results if the are distributed as normal variates,

Linear Regression.

Brief introduction on Logistic Regression

Logistic Regression Psy 524 Ainsworth.

Logistic Regression I Outline Introduction to maximum likelihood estimation (MLE) Introduction to Generalized Linear Models The simplest logistic regression.

FIN822 Li11 Binary independent and dependent variables.

Nguyen Ngoc Anh Nguyen Ha Trang

Confidence Intervals Underlying model: Unknown parameter We know how to calculate point estimates E.g. regression analysis But different data would change.

Regression with a Binary Dependent Variable

Binary Response Lecture 22 Lecture 22.

In previous lecture, we highlighted 3 shortcomings of the LPM. The most serious one is the unboundedness problem, i.e., the LPM may make the nonsense predictions.

QUALITATIVE AND LIMITED DEPENDENT VARIABLE MODELS.

GRA 6020 Multivariate Statistics; The Linear Probability model and The Logit Model (Probit) Ulf H. Olsson Professor of Statistics.

SLIDE 1IS 240 – Spring 2010 Logistic Regression The logistic function: The logistic function is useful because it can take as an input any.

Regression with a Binary Dependent Variable. Introduction What determines whether a teenager takes up smoking? What determines if a job applicant is successful.

Introduction to Logistic Regression. Simple linear regression Table 1 Age and systolic blood pressure (SBP) among 33 adult women.

FIN357 Li1 Binary Dependent Variables Chapter 12 P(y = 1|x) = G(  0 + x  )

1 Econ 240A Power Outline Review Projects 3 Review: Big Picture 1 #1 Descriptive Statistics –Numerical central tendency: mean, median, mode dispersion:

1 BA 275 Quantitative Business Methods Simple Linear Regression Introduction Case Study: Housing Prices Agenda.

Ulf H. Olsson Professor of Statistics

In previous lecture, we dealt with the unboundedness problem of LPM using the logit model. In this lecture, we will consider another alternative, i.e.

GRA 6020 Multivariate Statistics Probit and Logit Models Ulf H. Olsson Professor of Statistics.

Generalized Linear Models

1 Regression Models with Binary Response Regression: “Regression is a process in which we estimate one variable on the basis of one or more other variables.”

1 BINARY CHOICE MODELS: PROBIT ANALYSIS In the case of probit analysis, the sigmoid function is the cumulative standardized normal distribution.

Logistic Regression STA2101/442 F 2014 See last slide for copyright information.

9-1 MGMG 522 : Session #9 Binary Regression (Ch. 13)

LOGISTIC REGRESSION A statistical procedure to relate the probability of an event to explanatory variables Used in epidemiology to describe and evaluate.

Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

April 4 Logistic Regression –Lee Chapter 9 –Cody and Smith 9:F.

Forecasting Choices. Types of Variable Variable Quantitative Qualitative Continuous Discrete (counting) Ordinal Nominal.

Assessing Binary Outcomes: Logistic Regression Peter T. Donnan Professor of Epidemiology and Biostatistics Statistics for Health Research.

Copyright © 2006 The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Dummy Variable Regression Models chapter ten.

Chapter 13: Limited Dependent Vars. Zongyi ZHANG College of Economics and Business Administration.

Logistic Regression. Linear Regression Purchases vs. Income.

1 Everyday is a new beginning in life. Every moment is a time for self vigilance.

9.1 Chapter 9: Dummy Variables A Dummy Variable: is a variable that can take on only 2 possible values: yes, no up, down male, female union member, non-union.

CHAPTER 10: Logistic Regression. Binary classification Two classes Y = {0,1} Goal is to learn how to correctly classify the input into one of these two.

Qualitative and Limited Dependent Variable Models ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes.

Analysis of Experimental Data IV Christoph Engel.

Dates Presentations Wed / Fri Ex. 4, logistic regression, Monday Dec 7 th Final Tues. Dec 8 th, 3:30.

Linear Models Binary Logistic Regression. One-Way IVR 2 Hawaiian Bats Examine data.frame on HO Section 1.1 Questions –Is subspecies related to canine.

Binary logistic regression. Characteristic Regression model for target categorized variable explanatory variables – continuous and categorical Estimate.

1 Introduction to Modeling Beyond the Basics (Chapter 7)

4. Binary dependent variable Sometimes it is not possible to quantify the y’s Ex. To work or not? To vote one or other party, etc. Some difficulties: 1.Heteroskedasticity.

Logistic Regression Hal Whitehead BIOL4062/5062.

Economics 310 Lecture 22 Limited Dependent Variables.

Roger B. Hammer Assistant Professor Department of Sociology Oregon State University Conducting Social Research Logistic Regression Categorical Data Analysis.

The Probit Model Alexander Spermann University of Freiburg SS 2008.

Econometric methods of analysis and forecasting of financial markets Lecture 6. Models of restricted dependent variables.

Logistic Regression Logistic Regression - Binary Response variable and numeric and/or categorical explanatory variable(s) –Goal: Model the probability.

Instructor: R. Makoto 1richard makoto UZ Econ313 Lecture notes.

Logit VS Probit Pseudo R 2 LogitProbit Gain Loss Consistent Prospect

The Probit Model Alexander Spermann University of Freiburg SoSe 2009

Chapter 13 Logistic regression.

Discrete Choice Models:

M.Sc. in Economics Econometrics Module I

THE LOGIT AND PROBIT MODELS

Determination of toxic doses from log normal and log logistic models

LOGISTIC REGRESSION 1.

Financial Econometrics Lecture Notes 4

Generalized Linear Models

Introduction to logistic regression a.k.a. Varbrul

Introduction to Logistic Regression

THE LOGIT AND PROBIT MODELS

Microeconometric Modeling

MPHIL AdvancedEconometrics

Presentation transcript:

Discrete Dependent Variables Linear Regression, Dummy Variables If discrete dependent variable: need new technique Examples: Firm join Energy Star or not. Parcel of land developed as urban, agriculture, or open space. Species goes extinct or not. We’ll focus on: “Binary Choice Estimators”

Example: Tumors and ETU Big question: How does exposure affect chance of contracting disease? Treated foods contain ETU – may be harmful to health. Some rats exposed to ETU contracted tumors. 1. How does prob of tumor depend on dose? 2. What dose associated with 10% tumor rate (To advise on regulation)?

Evidence 6 dose groups (0,5,25,125,250,500) ~70 rats per group.

How ‘bout a Linear Model? Linear Model: Y=+X+

Problems with Linear Model How do we interpret dependent variable? (“chance of tumor?”) If Dose=0, chance of tumor < 0. If Dose large, chance of tumor > 1. Doesn’t make sense, and chance is linear in dose.

Binary Choice Models Logit (based on logistic cdf) and Probit (based on Normal cdf). Logistic cdf: Draw on board.

Adding Explanatory Variables Interpretation of Binary Choice Estimator: Probability of “Yes”. Replace “X” with function of explanatory variables:

Probability of Tumor

10% Tumor Chance What dose gives a 10% chance of contracting a tumor? After a bunch of math (see handout), D=170.24