optimization_machine_probit_bush106.c
|
|
|
- Martha O’Connor’
- 5 years ago
- Views:
Transcription
1 optimization_machine_probit_bush106.c. probit ybush black00 south hispanic00 income owner00 dwnom1n dwnom2n Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Iteration 6: log likelihood = Probit regression Number of obs = 432 LR chi2(7) = Prob > chi2 = Log likelihood = Pseudo R2 = ybush Coef. Std. Err. z P> z [95% Conf. Interval] black south hispanic income owner dwnom1n dwnom2n _cons double keithrules(double x[]) int i, j, k1, k0, l1, l0; double sum=0; double phi=0.0; double xphi=0.0; double sumsquared=0; k1=0; k0=0; for(i=0;i<nrowx;i++) sum=0.0; Kludge for Intercept Term sum=x[1]; sum=sum+x[i+j*nrowx]*x[j+2]; Note Change Here phi = (erf(fabs(sum)/sqrt(2.0)))/ ; if(sum < 0.0)phi=1.0-xphi; 1
2 Voted for Bush if(y[i] == 1.0) l1=1; l0=0; k1=k1+1; Voted for Gore if(y[i]!= 1.0) l1=0; l0=1; k0=k0+1; sumsquared = sumsquared + log(1.0 - phi); printf("%lf\n",-sumsquared); return -sumsquared; 2
3 optimization_machine_logit_bush106.c. logit ybush black00 south hispanic00 income owner00 dwnom1n dwnom2n Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Iteration 6: log likelihood = Logistic regression Number of obs = 432 LR chi2(7) = Prob > chi2 = Log likelihood = Pseudo R2 = ybush Coef. Std. Err. z P> z [95% Conf. Interval] black south hispanic income owner dwnom1n dwnom2n _cons double keithrules(double x[]) int i, j; double sum=0; double phi=0.0; double xphi=0.0; double sumsquared=0; k1=0; k0=0; for(i=0;i<nrowx;i++) Kludge for Intercept Term sum=x[1]; sum=sum+x[i+j*nrowx]*x[j+2]; Note Change Here Logit Setup phi=1.0/(1.0 + exp(-sum)); Voted for Bush if(y[i] == 1.0) 3
4 Voted for Gore if(y[i]!= 1.0) sumsquared = sumsquared + log(1.0 - phi); printf("%lf\n",-sumsquared); return -sumsquared; 4
5 junk_probit_2.c. summ Variable Obs Mean Std. Dev. Min Max partyid incomecat incomequint race sex south education age voted oprobit partyid incomequint race sex south education age Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Ordered probit regression Number of obs = 1421 LR chi2(6) = Prob > chi2 = Log likelihood = Pseudo R2 = partyid Coef. Std. Err. z P> z [95% Conf. Interval] incomequint race sex south education age /cut /cut /cut /cut /cut /cut Set up for Ordered Probit -- N-chotomous Probit double keithrules(double x[]) int i, j, jj; double sum=0; double phi=0.0; double erfarg; double xphi=0.0; double sumsquared=0; double *phicuts; double *phicutsmu; phicuts = (double *) malloc ((nkotp+1)*sizeof(double)); phicutsmu = (double *) malloc ((nkotp+1)*sizeof(double)); for(i=0;i<nrowx;i++) 5
6 /* Setup For Ordinary dichotomous Probit here * */ if(nkotp==2) /* 2-Choice Probit Uses an Intercept Term*/ sum=x[1]; sum=sum+x[i+j*nrowx]*x[j+2]; Note Change Here phi = (erf(fabs(sum)/sqrt(2.0)))/ ; if(sum < 0.0)phi=1.0-xphi; Choice 1 if(y[i] == 1.0) Choice 0 if(y[i]!= 1.0) sumsquared = sumsquared + log(1.0 - phi); There is No intercept Term in ordered Probit -- the first ncolx entries in x[] -- x[1] to x[ncolx] -- are the coefficients on the independent variables if(nkotp > 2) phicutsmu[1] = ; phicutsmu[2] = ; phicutsmu[3] = ; phicutsmu[4] = ; phicutsmu[5] = ; phicutsmu[6] = ; sum=0.0; sum=sum+x[i+j*nrowx]*x[j+1]; Note Change Here In Ordered Probit y_hat is treated as the MEAN of the normal -- the cutpoints are treated like "x's" for(jj=1;jj<nkotp;jj++) /* Compute Probability from -oo to (cutpoint - Y_hat) * */ erfarg = (phicutsmu[jj]-sum); phi = (erf(fabs(erfarg)/sqrt(2.0)))/ ; if(erfarg < 0.0)phi=1.0-xphi; phicuts[jj]=phi; phicuts[nkotp]=1.0; if(y[i] == 0)phi = phicuts[1]; 6
7 phicuts[(int)y[i]]; if(1 <= Y[i] <= nkotp-1)phi = phicuts[(int)y[i]+1]- printf("%lf\n",-sumsquared); free(phicuts); free(phicutsmu); return -sumsquared; 7
Xamoeba_xpowell_probit.c
Xamoeba_xpowell_probit.c /* /* /* xamoeba_xpowell.c -- C code is from NUMERICAL RECIPES IN C -- * Additional code written by Keith Poole * September - October 2009 * Amoeba -- Uses Nedler-Mead downhill
Health Disparities (HD): It s just about comparing two groups
A review of modern methods of estimating the size of health disparities May 24, 2017 Emil Coman 1 Helen Wu 2 1 UConn Health Disparities Institute, 2 UConn Health Modern Modeling conference, May 22-24,
Panel Data 4: Fixed Effects vs Random Effects Models
Panel Data 4: Fixed Effects vs Random Effects Models Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised April 4, 2017 These notes borrow very heavily, sometimes verbatim,
schooling.log 7/5/2006
----------------------------------- log: C:\dnb\schooling.log log type: text opened on: 5 Jul 2006, 09:03:57. /* schooling.log */ > use schooling;. gen age2=age76^2;. /* OLS (inconsistent) */ > reg lwage76
Bivariate (Simple) Regression Analysis
Revised July 2018 Bivariate (Simple) Regression Analysis This set of notes shows how to use Stata to estimate a simple (two-variable) regression equation. It assumes that you have set Stata up on your
THE LINEAR PROBABILITY MODEL: USING LEAST SQUARES TO ESTIMATE A REGRESSION EQUATION WITH A DICHOTOMOUS DEPENDENT VARIABLE
PLS 802 Spring 2018 Professor Jacoby THE LINEAR PROBABILITY MODEL: USING LEAST SQUARES TO ESTIMATE A REGRESSION EQUATION WITH A DICHOTOMOUS DEPENDENT VARIABLE This handout shows the log of a Stata session
. predict mod1. graph mod1 ed, connect(l) xlabel ylabel l1(model1 predicted income) b1(years of education)
DUMMY VARIABLES AND INTERACTIONS Let's start with an example in which we are interested in discrimination in income. We have a dataset that includes information for about 16 people on their income, their
texdoc 2.0 An update on creating LaTeX documents from within Stata Example 2
texdoc 20 An update on creating LaTeX documents from within Stata Contents Example 2 Ben Jann University of Bern, benjann@sozunibech 2016 German Stata Users Group Meeting GESIS, Cologne, June 10, 2016
Creating LaTeX and HTML documents from within Stata using texdoc and webdoc. Example 2
Creating LaTeX and HTML documents from within Stata using texdoc and webdoc Contents Example 2 Ben Jann University of Bern, benjann@sozunibech Nordic and Baltic Stata Users Group meeting Oslo, September
Dr. Barbara Morgan Quantitative Methods
Dr. Barbara Morgan Quantitative Methods 195.650 Basic Stata This is a brief guide to using the most basic operations in Stata. Stata also has an on-line tutorial. At the initial prompt type tutorial. In
Linear regression Number of obs = 6,866 F(16, 326) = Prob > F = R-squared = Root MSE =
- /*** To demonstrate use of 2SLS ***/ * Case: In the early 1990's Tanzania implemented a FP program to reduce fertility, which was among the highest in the world * The FP program had two main components:
Computing Murphy Topel-corrected variances in a heckprobit model with endogeneity
The Stata Journal (2010) 10, Number 2, pp. 252 258 Computing Murphy Topel-corrected variances in a heckprobit model with endogeneity Juan Muro Department of Statistics, Economic Structure, and International
Stata Training. AGRODEP Technical Note 08. April Manuel Barron and Pia Basurto
AGRODEP Technical Note 08 April 2013 Stata Training Manuel Barron and Pia Basurto AGRODEP Technical Notes are designed to document state-of-the-art tools and methods. They are circulated in order to help
Introduction to STATA 6.0 ECONOMICS 626
Introduction to STATA 6.0 ECONOMICS 626 Bill Evans Fall 2001 This handout gives a very brief introduction to STATA 6.0 on the Economics Department Network. In a few short years, STATA has become one of
ECON Introductory Econometrics Seminar 4
ECON4150 - Introductory Econometrics Seminar 4 Stock and Watson EE8.2 April 28, 2015 Stock and Watson EE8.2 ECON4150 - Introductory Econometrics Seminar 4 April 28, 2015 1 / 20 Current Population Survey
Page 1. Notes: MB allocated to data 2. Stata running in batch mode. . do 2-simpower-varests.do. . capture log close. .
tm / / / / / / / / / / / / 101 Copyright 1984-2009 Statistics/Data Analysis StataCorp 4905 Lakeway Drive College Station, Texas 77845 USA 800-STATA-PC http://wwwstatacom 979-696-4600 stata@statacom 979-696-4601
Week 9: Modeling II. Marcelo Coca Perraillon. Health Services Research Methods I HSMP University of Colorado Anschutz Medical Campus
Week 9: Modeling II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Taking the log Retransformation
Programming MLE models in STATA 1
Programming MLE models in STATA 1 Andreas Beger 30 November 2008 1 Overview You have learned about a lot of different MLE models so far, and most of them are available as pre-defined commands in STATA.
Week 4: Simple Linear Regression III
Week 4: Simple Linear Regression III Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Goodness of
Missing Data Part II: Multiple Imputation Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 24, 2015
Missing Data Part II: Multiple Imputation Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 24, 2015 Warning: I teach about Multiple Imputation with some trepidation.
Regression. Dr. G. Bharadwaja Kumar VIT Chennai
Regression Dr. G. Bharadwaja Kumar VIT Chennai Introduction Statistical models normally specify how one set of variables, called dependent variables, functionally depend on another set of variables, called
MODULE ONE, PART THREE: READING DATA INTO STATA, CREATING AND RECODING VARIABLES, AND ESTIMATING AND TESTING MODELS IN STATA
MODULE ONE, PART THREE: READING DATA INTO STATA, CREATING AND RECODING VARIABLES, AND ESTIMATING AND TESTING MODELS IN STATA This Part Three of Module One provides a cookbook-type demonstration of the
Missing Data Part II: Multiple Imputation & Maximum Likelihood
Missing Data Part II: Multiple Imputation & Maximum Likelihood Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 12, 2017 Warning: I teach about Multiple
Stata versions 12 & 13 Week 4 Practice Problems
Stata versions 12 & 13 Week 4 Practice Problems SOLUTIONS 1 Practice Screen Capture a Create a word document Name it using the convention lastname_lab1docx (eg bigelow_lab1docx) b Using your browser, go
Linear Model Selection and Regularization. especially usefull in high dimensions p>>100.
Linear Model Selection and Regularization especially usefull in high dimensions p>>100. 1 Why Linear Model Regularization? Linear models are simple, BUT consider p>>n, we have more features than data records
Multiple-imputation analysis using Stata s mi command
Multiple-imputation analysis using Stata s mi command Yulia Marchenko Senior Statistician StataCorp LP 2009 UK Stata Users Group Meeting Yulia Marchenko (StataCorp) Multiple-imputation analysis using mi
Week 11: Interpretation plus
Week 11: Interpretation plus Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline A bit of a patchwork
range: [1,20] units: 1 unique values: 20 missing.: 0/20 percentiles: 10% 25% 50% 75% 90%
![range: [1,20] units: 1 unique values: 20 missing.: 0/20 percentiles: 10% 25% 50% 75% 90%](/thumbs/95/123687023.jpg "range: [1,20] units: 1 unique values: 20 missing.: 0/20 percentiles: 10% 25% 50% 75% 90%")
------------------ log: \Term 2\Lecture_2s\regression1a.log log type: text opened on: 22 Feb 2008, 03:29:09. cmdlog using " \Term 2\Lecture_2s\regression1a.do" (cmdlog \Term 2\Lecture_2s\regression1a.do
Week 4: Simple Linear Regression II
Week 4: Simple Linear Regression II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Algebraic properties
Lab 2: OLS regression
Lab 2: OLS regression Andreas Beger February 2, 2009 1 Overview This lab covers basic OLS regression in Stata, including: multivariate OLS regression reporting coefficients with different confidence intervals
An Introductory Guide to Stata
An Introductory Guide to Stata Scott L. Minkoff Assistant Professor Department of Political Science Barnard College sminkoff@barnard.edu Updated: July 9, 2012 1 TABLE OF CONTENTS ABOUT THIS GUIDE... 4
Logistic Regression. (Dichotomous predicted variable) Tim Frasier
Logistic Regression (Dichotomous predicted variable) Tim Frasier Copyright Tim Frasier This work is licensed under the Creative Commons Attribution 4.0 International license. Click here for more information.
Show how the LG-Syntax can be generated from a GUI model. Modify the LG-Equations to specify a different LC regression model
Tutorial #S1: Getting Started with LG-Syntax DemoData = 'conjoint.sav' This tutorial introduces the use of the LG-Syntax module, an add-on to the Advanced version of Latent GOLD. In this tutorial we utilize
Instrumental variables, bootstrapping, and generalized linear models
The Stata Journal (2003) 3, Number 4, pp. 351 360 Instrumental variables, bootstrapping, and generalized linear models James W. Hardin Arnold School of Public Health University of South Carolina Columbia,
Week 5: Multiple Linear Regression II
Week 5: Multiple Linear Regression II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Adjusted R
Introduction to Computing for Sociologists Neustadtl
Introduction to Computing for Sociologists Neustadtl Using Regression Regression has a lot of parts, most of it pre- and post- analysis. Do you understand your data? How are your variables measured? Are
Chapter 7 Heteroscedastic Tobit Model Code
Ch 7 Heteroscedastic Tobit Model 1 Chapter 7 Heteroscedastic Tobit Model Code The code for estimating a heteroscedastic Tobit model in R and Stata is provided in this document, along with an annotated
Week 10: Heteroskedasticity II
Week 10: Heteroskedasticity II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Dealing with heteroskedasticy
Introduction to Bayesian Analysis in Stata
tools Introduction to Bayesian Analysis in Gustavo Sánchez Corp LLC September 15, 2017 Porto, Portugal tools 1 Bayesian analysis: 2 Basic Concepts The tools 14: The command 15: The bayes prefix Postestimation
Package intreg. R topics documented: February 15, Version Date Title Interval Regression
Package intreg February 15, 2013 Version 0.1-2 Date 2012-01-12 Title Interval Regression Author Ott Toomet Maintainer Ott Toomet Depends R (>= 2.7.0), maxlik (>=
run ld50 /* Plot the onserved proportions and the fitted curve */ DATA SETR1 SET SETR1 PROB=X1/(X1+X2) /* Use this to create graphs in Windows */ gopt
/* This program is stored as bliss.sas */ /* This program uses PROC LOGISTIC in SAS to fit models with logistic, probit, and complimentary log-log link functions to the beetle mortality data collected
API-202 Empirical Methods II Spring 2004 A SHORT INTRODUCTION TO STATA 8.0
API-202 Empirical Methods II Spring 2004 A SHORT INTRODUCTION TO STATA 8.0 Course materials and data sets will assume that you are using Stata to complete the analysis. Stata is available on all of the
25 Working with categorical data and factor variables
25 Working with categorical data and factor variables Contents 25.1 Continuous, categorical, and indicator variables 25.1.1 Converting continuous variables to indicator variables 25.1.2 Converting continuous
Getting Started Using Stata
Quant II 2011: Categorical Data Analysis Getting Started Using Stata Shawna Rohrman 2010 05 09 Revised by Trisha Tiamzon 2010 12 26 Note: Parts of this guide were adapted from Stata s Getting Started with
Heteroskedasticity and Homoskedasticity, and Homoskedasticity-Only Standard Errors
Heteroskedasticity and Homoskedasticity, and Homoskedasticity-Only Standard Errors (Section 5.4) What? Consequences of homoskedasticity Implication for computing standard errors What do these two terms
PubHlth 640 Intermediate Biostatistics Unit 2 - Regression and Correlation. Simple Linear Regression Software: Stata v 10.1
PubHlth 640 Intermediate Biostatistics Unit 2 - Regression and Correlation Simple Linear Regression Software: Stata v 10.1 Emergency Calls to the New York Auto Club Source: Chatterjee, S; Handcock MS and
Dummy variables for categorical predictive attributes
Subject Coding categorical predictive attributes for logistic regression. When we want to use predictive categorical attributes in a logistic regression or a linear discriminant analysis, we must recode
Beta-Regression with SPSS Michael Smithson School of Psychology, The Australian National University
9/1/2005 Beta-Regression with SPSS 1 Beta-Regression with SPSS Michael Smithson School of Psychology, The Australian National University (email: Michael.Smithson@anu.edu.au) SPSS Nonlinear Regression syntax
Robust Linear Regression (Passing- Bablok Median-Slope)
Chapter 314 Robust Linear Regression (Passing- Bablok Median-Slope) Introduction This procedure performs robust linear regression estimation using the Passing-Bablok (1988) median-slope algorithm. Their
Chapter 10: Extensions to the GLM
Chapter 10: Extensions to the GLM 10.1 Implement a GAM for the Swedish mortality data, for males, using smooth functions for age and year. Age and year are standardized as described in Section 4.11, for
Generalized ordered logit/partial proportional odds models for ordinal dependent variables
The Stata Journal (2006) 6, Number 1, pp. 58 82 Generalized ordered logit/partial proportional odds models for ordinal dependent variables Richard Williams Department of Sociology University of Notre Dame
AcaStat User Manual. Version 8.3 for Mac and Windows. Copyright 2014, AcaStat Software. All rights Reserved.
AcaStat User Manual Version 8.3 for Mac and Windows Copyright 2014, AcaStat Software. All rights Reserved. http://www.acastat.com Table of Contents INTRODUCTION... 5 GETTING HELP... 5 INSTALLATION... 5
Introduction to Hierarchical Linear Model. Hsueh-Sheng Wu CFDR Workshop Series January 30, 2017
Introduction to Hierarchical Linear Model Hsueh-Sheng Wu CFDR Workshop Series January 30, 2017 1 Outline What is Hierarchical Linear Model? Why do nested data create analytic problems? Graphic presentation
Unit 5 Logistic Regression Practice Problems
Unit 5 Logistic Regression Practice Problems SOLUTIONS R Users Source: Afifi A., Clark VA and May S. Computer Aided Multivariate Analysis, Fourth Edition. Boca Raton: Chapman and Hall, 2004. Exercises
Missing Data and Imputation
Missing Data and Imputation Hoff Chapter 7, GH Chapter 25 April 21, 2017 Bednets and Malaria Y:presence or absence of parasites in a blood smear AGE: age of child BEDNET: bed net use (exposure) GREEN:greenness
piecewise ginireg 1 Piecewise Gini Regressions in Stata Jan Ditzen 1 Shlomo Yitzhaki 2 September 8, 2017
piecewise ginireg 1 Piecewise Gini Regressions in Stata Jan Ditzen 1 Shlomo Yitzhaki 2 1 Heriot-Watt University, Edinburgh, UK Center for Energy Economics Research and Policy (CEERP) 2 The Hebrew University
Two-Stage Least Squares
Chapter 316 Two-Stage Least Squares Introduction This procedure calculates the two-stage least squares (2SLS) estimate. This method is used fit models that include instrumental variables. 2SLS includes
Performing Cluster Bootstrapped Regressions in R
Performing Cluster Bootstrapped Regressions in R Francis L. Huang / October 6, 2016 Supplementary material for: Using Cluster Bootstrapping to Analyze Nested Data with a Few Clusters in Educational and
I Launching and Exiting Stata. Stata will ask you if you would like to check for updates. Update now or later, your choice.
I Launching and Exiting Stata 1. Launching Stata Stata can be launched in either of two ways: 1) in the stata program, click on the stata application; or 2) double click on the short cut that you have
Source:
Time Series Source: http://www.princeton.edu/~otorres/stata/ Time series data is data collected over time for a single or a group of variables. Date variable For this kind of data the first thing to do
Xamoeba_general.c. func is the user defined function being minimized/maximized;
Xamoeba_general.c /**/ /* xamoeba_general.c -- C code is from NUMERICAL RECIPES IN C -- * Additional code written by Keith Poole * September - October 2009 * Uses Nedler-Mead downhill simplex method to
Introduction to Stata Toy Program #1 Basic Descriptives
Introduction to Stata 2018-19 Toy Program #1 Basic Descriptives Summary The goal of this toy program is to get you in and out of a Stata session and, along the way, produce some descriptive statistics.
Statistical Analysis of List Experiments
Statistical Analysis of List Experiments Kosuke Imai Princeton University Joint work with Graeme Blair October 29, 2010 Blair and Imai (Princeton) List Experiments NJIT (Mathematics) 1 / 26 Motivation
Applied Multivariate Analysis
Department of Mathematics and Statistics, University of Vaasa, Finland Spring 2017 Choosing Statistical Method 1 Choice an appropriate method 2 Cross-tabulation More advance analysis of frequency tables
Exercise: Graphing and Least Squares Fitting in Quattro Pro
Chapter 5 Exercise: Graphing and Least Squares Fitting in Quattro Pro 5.1 Purpose The purpose of this experiment is to become familiar with using Quattro Pro to produce graphs and analyze graphical data.
Statistical and Econometric Methods for Transportation Data Analysis
Statistical and Econometric Methods for Transportation Data Analysis Chapter 14 Ordered Probability Models Example 14.2 Ordered Discrete Data Ordered Probit with Random Effects A survey of 56 subjects
Multiple Imputation for Missing Data. Benjamin Cooper, MPH Public Health Data & Training Center Institute for Public Health
Multiple Imputation for Missing Data Benjamin Cooper, MPH Public Health Data & Training Center Institute for Public Health Outline Missing data mechanisms What is Multiple Imputation? Software Options
Lecture 3: The basic of programming- do file and macro
Introduction to Stata- A. Chevalier Lecture 3: The basic of programming- do file and macro Content of Lecture 3: -Entering and executing programs do file program ado file -macros 1 A] Entering and executing
Review of Stata II AERC Training Workshop Nairobi, May 2002
Review of Stata II AERC Training Workshop Nairobi, 20-24 May 2002 This note provides more information on the basics of Stata that should help you with the exercises in the remaining sessions of the workshop.
SOCY7706: Longitudinal Data Analysis Instructor: Natasha Sarkisian. Panel Data Analysis: Fixed Effects Models
SOCY776: Longitudinal Data Analysis Instructor: Natasha Sarkisian Panel Data Analysis: Fixed Effects Models Fixed effects models are similar to the first difference model we considered for two wave data
Getting Started Using Stata
Categorical Data Analysis Getting Started Using Stata Scott Long and Shawna Rohrman cda12 StataGettingStarted 2012 05 11.docx Getting Started in Stata Opening Stata When you open Stata, the screen has
Using Machine Learning to Optimize Storage Systems
Using Machine Learning to Optimize Storage Systems Dr. Kiran Gunnam 1 Outline 1. Overview 2. Building Flash Models using Logistic Regression. 3. Storage Object classification 4. Storage Allocation recommendation
Orange Juice data. Emanuele Taufer. 4/12/2018 Orange Juice data (1)
Orange Juice data Emanuele Taufer file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20labs/l10-oj-data.html#(1) 1/31 Orange Juice Data The data contain weekly sales of refrigerated
/* Parametric models: AFT modeling */ /* Data described in Chapter 3 of P. Allison, "Survival Analysis Using the SAS System." */
/* Parametric models: AFT modeling */ /* Data described in Chapter 3 of P. Allison, "Survival Analysis Using the SAS System." */ options ls =79; data recidall; input week arrest fin age race wexp mar paro
Introduction to Stata: An In-class Tutorial
Introduction to Stata: An I. The Basics - Stata is a command-driven statistical software program. In other words, you type in a command, and Stata executes it. You can use the drop-down menus to avoid
Session 8. Statistical analysis Using Gauss Applications
Session 8 Statistical analysis Using Gauss Applications page 1. Descriptive Statistics 8-2 Example: Frequencies 8-2 Example: Histogram 8-2 2. Linear Regression 8-3 Linear regression Options 8-3 Practical
HDs with 1-on-1 Matching
How to Peel Oranges into Apples: Finding Causes and Effects of Health Disparities with Difference Scores Built by 1-on-1 Matching May 24, 2017 Emil N. Coman 1 Helen Wu 2 Wizdom A. Powell 1 1 UConn Health
TYPES OF VARIABLES, STRUCTURE OF DATASETS, AND BASIC STATA LAYOUT
PRIMER FOR ACS OUTCOMES RESEARCH COURSE: TYPES OF VARIABLES, STRUCTURE OF DATASETS, AND BASIC STATA LAYOUT STEP 1: Install STATA statistical software. STEP 2: Read through this primer and complete the
CH5: CORR & SIMPLE LINEAR REFRESSION =======================================
STAT 430 SAS Examples SAS5 ===================== ssh xyz@glue.umd.edu, tap sas913 (old sas82), sas https://www.statlab.umd.edu/sasdoc/sashtml/onldoc.htm CH5: CORR & SIMPLE LINEAR REFRESSION =======================================
Moving Beyond Linearity
Moving Beyond Linearity The truth is never linear! 1/23 Moving Beyond Linearity The truth is never linear! r almost never! 1/23 Moving Beyond Linearity The truth is never linear! r almost never! But often
Stata Session 2. Tarjei Havnes. University of Oslo. Statistics Norway. ECON 4136, UiO, 2012
Stata Session 2 Tarjei Havnes 1 ESOP and Department of Economics University of Oslo 2 Research department Statistics Norway ECON 4136, UiO, 2012 Tarjei Havnes (University of Oslo) Stata Session 2 ECON
Fitting adjusted limited dependent variable mixture models to EQ-5D
The Stata Journal (2015) 15, Number 3, pp. 737 750 Fitting adjusted limited dependent variable mixture models to EQ-5D Mónica Hernández Alava School of Health and Related Research Health Economics and
Telephone Survey Response: Effects of Cell Phones in Landline Households
Telephone Survey Response: Effects of Cell Phones in Landline Households Dennis Lambries* ¹, Michael Link², Robert Oldendick 1 ¹University of South Carolina, ²Centers for Disease Control and Prevention
Centering and Interactions: The Training Data
Centering and Interactions: The Training Data A random sample of 150 technical support workers were first given a test of their technical skill and knowledge, and then randomly assigned to one of three
May 24, Emil Coman 1 Yinghui Duan 2 Daren Anderson 3
Assessing Health Disparities in Intensive Longitudinal Data: Gender Differences in Granger Causality Between Primary Care Provider and Emergency Room Usage, Assessed with Medicaid Insurance Claims May
Package RegressionFactory
Type Package Package RegressionFactory September 8, 2016 Title Expander Functions for Generating Full Gradient and Hessian from Single-Slot and Multi-Slot Base Distributions Version 0.7.2 Date 2016-09-07
xsmle - A Command to Estimate Spatial Panel Data Models in Stata
xsmle - A Command to Estimate Spatial Panel Data Models in Stata Federico Belotti?, Gordon Hughes,AndreaPianoMortari?? CEIS, University of Rome Tor Vergata School of Economics, University of Edinburg.
Introduction to Programming in Stata
Introduction to in Stata Laron K. University of Missouri Goals Goals Replicability! Goals Replicability! Simplicity/efficiency Goals Replicability! Simplicity/efficiency Take a peek under the hood! Data
An introduction to SPSS
An introduction to SPSS To open the SPSS software using U of Iowa Virtual Desktop... Go to https://virtualdesktop.uiowa.edu and choose SPSS 24. Contents NOTE: Save data files in a drive that is accessible
Conditional and Unconditional Regression with No Measurement Error
Conditional and with No Measurement Error /* reg2ways.sas */ %include 'readsenic.sas'; title2 ''; proc reg; title3 'Conditional Regression'; model infrisk = stay census; proc calis cov; /* Analyze the
STM103 Spring 2008 INTRODUCTION TO STATA 8.0
STM103 Spring 2008 INTRODUCTION TO STATA 8.0 1. PREPARING BEFORE ENTERING THE LAB... 2 Getting the shared dataset... 2 Assignment 1 preparation... 2 2. STARTING A STATA SESSION... 3 Opening, Saving, and
A QUICK INTRODUCTION TO STATA
A QUICK INTRODUCTION TO STATA This module provides a quick introduction to STATA. After completing this module you will be able to input data, save data, transform data, create basic tables, create basic
Lecture 1: Statistical Reasoning 2. Lecture 1. Simple Regression, An Overview, and Simple Linear Regression
Lecture Simple Regression, An Overview, and Simple Linear Regression Learning Objectives In this set of lectures we will develop a framework for simple linear, logistic, and Cox Proportional Hazards Regression
Factorial ANOVA. Skipping... Page 1 of 18
Factorial ANOVA The potato data: Batches of potatoes randomly assigned to to be stored at either cool or warm temperature, infected with one of three bacterial types. Then wait a set period. The dependent
Ben Baumer Instructor
MULTIPLE AND LOGISTIC REGRESSION What is logistic regression? Ben Baumer Instructor A categorical response variable ggplot(data = hearttr, aes(x = age, y = survived)) + geom_jitter(width = 0, height =
Coding Categorical Variables in Regression: Indicator or Dummy Variables. Professor George S. Easton
Coding Categorical Variables in Regression: Indicator or Dummy Variables Professor George S. Easton DataScienceSource.com This video is embedded on the following web page at DataScienceSource.com: DataScienceSource.com/DummyVariables
Missing Data Part 1: Overview, Traditional Methods Page 1
Missing Data Part 1: Overview, Traditional Methods Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 17, 2015 This discussion borrows heavily from: Applied
Zero-Inflated Poisson Regression
Chapter 329 Zero-Inflated Poisson Regression Introduction The zero-inflated Poisson (ZIP) regression is used for count data that exhibit overdispersion and excess zeros. The data distribution combines
An Econometric Study: The Cost of Mobile Broadband
An Econometric Study: The Cost of Mobile Broadband Zhiwei Peng, Yongdon Shin, Adrian Raducanu IATOM13 ENAC January 16, 2014 Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband
LIST OF TABLES. Page Title No.
LIST OF TABLES Table 1.1 Growth in Total Subscriber Base of Telecom Industry 5 1.2 Growth in Tele-density 11 2.1 Top 10 Countries with the number of Mobile Phone Subscribers 2011 23 2.2 World Wide Market