############################ ### toxo.r ### ############################
|
|
|
- Roger Gregory
- 5 years ago
- Views:
Transcription
1 ############################ ### toxo.r ### ############################ toxo < read.table(file="n:\\courses\\stat8620\\fall 08\\toxo.dat",header=T) #toxo < read.table(file="c:\\documents and Settings\\dhall\\My Documents\\Dan's Work Stuff\\courses\\STAT8620\\Fall 08\\toxo.dat",header=T) toxo$rain1000 < toxo$rainf/1000 toxo$ypos < round(toxo$ppos*toxo$n) toxo$yneg < toxo$n toxo$ypos toxo$samplogit < log((toxo$ypos+0.5)/(toxo$n toxo$ypos+0.5)) toxo[1:3,] rainf ppos n rain1000 ypos yneg samplogit plot(toxo$rain1000,toxo$ppos,main="prop positive versus rainfall (in 1000's)")
2 plot(toxo$rain1000,toxo$samplogit,main="samp log odds positive versus rainfall (in 1000's)") m1 < glm(cbind(ypos,yneg)~poly(rain1000,5),data=toxo, + family=binomial(link="logit")) summary(m1) Call: glm(formula = cbind(ypos, yneg) ~ poly(rain1000, 5), family = binomial(link = "logit"), data = toxo) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr( z ) (Intercept) poly(rain1000, 5) poly(rain1000, 5) poly(rain1000, 5) ***
3 poly(rain1000, 5) poly(rain1000, 5) Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for binomial family taken to be 1) Null deviance: on 33 degrees of freedom Residual deviance: on 28 degrees of freedom AIC: Number of Fisher Scoring iterations: 3 m2 < glm(cbind(ypos,yneg)~poly(rain1000,3),data=toxo, + family=binomial(link="logit")) summary(m2) Call: glm(formula = cbind(ypos, yneg) ~ poly(rain1000, 3), family = binomial(link = "logit"), data = toxo) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr( z ) (Intercept) poly(rain1000, 3) poly(rain1000, 3) poly(rain1000, 3) *** Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for binomial family taken to be 1) Null deviance: on 33 degrees of freedom Residual deviance: on 30 degrees of freedom AIC: Number of Fisher Scoring iterations: 3 anova(m2,m1,test="chisq") Analysis of Deviance Table Model 1: cbind(ypos, yneg) ~ poly(rain1000, 3) Model 2: cbind(ypos, yneg) ~ poly(rain1000, 5)
4 Resid. Df Resid. Dev Df Deviance P( Chi ) m0 < glm(cbind(ypos,yneg)~1,data=toxo, family=binomial(link="logit")) anova(m0,m2,test="chisq") Analysis of Deviance Table Model 1: cbind(ypos, yneg) ~ 1 Model 2: cbind(ypos, yneg) ~ poly(rain1000, 3) Resid. Df Resid. Dev Df Deviance P( Chi ) #deviance of model m2 is GOF statistic: deviance(m2) [1] #Pearson X^2 statistic: sum(resid(m2,type="pearson")^2) [1] m2q < glm(cbind(ypos,yneg)~poly(rain1000,3),data=toxo, + family=quasibinomial(link="logit")) summary(m2q) Call: glm(formula = cbind(ypos, yneg) ~ poly(rain1000, 3), family = quasibinomial(link = "logit"), data = toxo) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr( t ) (Intercept) poly(rain1000, 3) poly(rain1000, 3) poly(rain1000, 3) * Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for quasibinomial family taken to be ) Null deviance: on 33 degrees of freedom Residual deviance: on 30 degrees of freedom AIC: NA
5 Number of Fisher Scoring iterations: 3 r0 < seq(from=min(toxo$rain1000),to=max(toxo$rain1000),length=100) expit < function(x) {1/(1+exp( x)) } pred.m2 < predict(m2,data.frame(rain1000=r0),se.fit=t,type="link") L < expit(pred.m2$fit 1.96*pred.m2$se.fit) U < expit(pred.m2$fit+1.96*pred.m2$se.fit) plot(toxo$rain1000,toxo$ppos,type="p",xlab="rainfall/1000", + ylab="prop positive for toxoplasmosis", + main="fitted probability from model m2") lines(r0,expit( pred.m2$fit )) lines(r0,l,lty=4) lines(r0,u,lty=4) legend(locator(1),lty=c(1,4),legend=c("fitted probability","approx 95% conf. limits")) pred.m2q < predict(m2q,data.frame(rain1000=r0),se.fit=t,type="link") L < expit(pred.m2q$fit 1.96*pred.m2q$se.fit) U < expit(pred.m2q$fit+1.96*pred.m2q$se.fit)
6 plot(toxo$rain1000,toxo$ppos,type="p",xlab="rainfall/1000", + ylab="prop positive for toxoplasmosis", + main="fitted probability from model m2q") lines(r0,expit( pred.m2q$fit )) lines(r0,l,lty=4) lines(r0,u,lty=4) legend(locator(1),lty=c(1,4),legend=c("fitted probability","approx 95% conf. limits"))
> budworm$samplogit < log((budworm$y+0.5)/(budworm$m budworm$y+0.5))
budworm < read.table(file="n:\\courses\\stat8620\\fall 08\\budworm.dat",header=T) #budworm < read.table(file="c:\\documents and Settings\\dhall\\My Documents\\Dan's Work Stuff\\courses\\STAT8620\\Fall
Logistic Regression. Logistic Regression Theory
Logistic Regression Dr. J. Kyle Roberts Southern Methodist University Simmons School of Education and Human Development Department of Teaching and Learning Logistic Regression The linear probability model.
Multiple Regression and Logistic Regression II. Dajiang 525 Apr
Multiple Regression and Logistic Regression II Dajiang Liu @PHS 525 Apr-19-2016 Materials from Last Time Multiple regression model: Include multiple predictors in the model = + + + + How to interpret the
Introduction to General and Generalized Linear Models
Introduction to General and Generalized Linear Models Generalized Linear Models - IIIb Henrik Madsen March 18, 2012 Henrik Madsen () Chapman & Hall March 18, 2012 1 / 32 Examples Overdispersion and Offset!
Bradley-Terry Models. Stat 557 Heike Hofmann
Bradley-Terry Models Stat 557 Heike Hofmann Outline Definition: Bradley-Terry Fitting the model Extension: Order Effects Extension: Ordinal & Nominal Response Repeated Measures Bradley-Terry Model (1952)
Ordinal Multinomial Logistic Regression. Thom M. Suhy Southern Methodist University May14th, 2013
Ordinal Multinomial Logistic Thom M. Suhy Southern Methodist University May14th, 2013 GLM Generalized Linear Model (GLM) Framework for statistical analysis (Gelman and Hill, 2007, p. 135) Linear Continuous
boxcox() returns the values of α and their loglikelihoods,
Solutions to Selected Computer Lab Problems and Exercises in Chapter 11 of Statistics and Data Analysis for Financial Engineering, 2nd ed. by David Ruppert and David S. Matteson c 2016 David Ruppert and
Negative Binomial Model for Count Data Log-linear Models for Contingency Tables - Introduction
Negative Binomial Model for Count Data Log-linear Models for Contingency Tables - Introduction Statistics 149 Spring 2006 Copyright 2006 by Mark E. Irwin Negative Binomial Family Example: Absenteeism from
Generalized Linear Models
Generalized Linear Models Scott Creel Wednesday, September 10, 2014 This exercise extends the prior material on using the lm() function to fit an OLS regression and test hypotheses about effects on a parameter.
Logistic Regression with R: Example One
Logistic Regression with R: Example One math = read.table("http://www.utstat.toronto.edu/~brunner/appliedf12/data/mathcat.data") math[1:5,] hsgpa hsengl hscalc course passed outcome 1 78.0 80 Yes Mainstrm
Stat 401XV Exam 3 Spring 2017
Stat 40XV Exam Spring 07 I have neither given nor received unauthorized assistance on this exam. Name Signed Date Name Printed ATTENTION! Incorrect numerical answers unaccompanied by supporting reasoning
Statistics 175 Applied Statistics Generalized Linear Models Jianqing Fan
Statistics 175 Applied Statistics Generalized Linear Models Jianqing Fan Example 1 (Kyhposis data): (The data set kyphosis consists of measurements on 81 children following corrective spinal surgery. Variable
CREDIT RISK MODELING IN R. Logistic regression: introduction
CREDIT RISK MODELING IN R Logistic regression: introduction Final data structure > str(training_set) 'data.frame': 19394 obs. of 8 variables: $ loan_status : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1
Chapter 8 Exercises 1. Data Analysis & Graphics Using R Solutions to Exercises (May 1, 2010)
Chapter 8 Exercises 1 Data Analysis & Graphics Using R Solutions to Exercises (May 1, 2010) Preliminaries > library(daag) Exercise 1 The following table shows numbers of occasions when inhibition (i.e.,
Logit Analysis. Using vttown.dta. Albert Satorra, UPF
Logit Analysis Using vttown.dta Logit Regression Odds ratio The most common way of interpreting a logit is to convert it to an odds ratio using the exp() function. One can convert back using the ln()
Step 1: Load the appropriate R package. Step 2: Fit a separate mixed model for each independence claim in the basis set.
Step 1: Load the appropriate R package. You will need two libraries: nlme and lme4. Step 2: Fit a separate mixed model for each independence claim in the basis set. For instance, in Table 2 the first basis
Using R to Create Synthetic Discrete Response Regression Models
Arizona State University From the SelectedWorks of Joseph M Hilbe July 3, 2011 Using R to Create Synthetic Discrete Response Regression Models Joseph Hilbe, Arizona State University Available at: https://works.bepress.com/joseph_hilbe/3/
Case Study: Applying Generalized Linear Models
Case Study: Applying Generalized Linear Models Dr. Kempthorne May 12, 2016 Contents 1 Generalized Linear Models of Semi-Quantal Biological Assay Data 2 1.1 Coal miners Pneumoconiosis Data.................
Addiction - Multinomial Model
Addiction - Multinomial Model February 8, 2012 First the addiction data are loaded and attached. > library(catdata) > data(addiction) > attach(addiction) For the multinomial logit model the function multinom
Predicting Charitable Contributions
Predicting Charitable Contributions By Lauren Meyer Executive Summary Charitable contributions depend on many factors from financial security to personal characteristics. This report will focus on demographic
AIC = Log likelihood = BIC =
- log: /mnt/ide1/home/sschulh1/apc/apc_examplelog log type: text opened on: 21 Jul 2006, 18:08:20 *replicate table 5 and cols 7-9 of table 3 in Yang, Fu and Land (2004) *Stata can maximize GLM objective
Credit Risk Modelling
Credit Risk Modelling Tiziano Bellini Università di Bologna December 13, 2013 Tiziano Bellini (Università di Bologna) Credit Risk Modelling December 13, 2013 1 / 55 Outline Framework Credit Risk Modelling
MCMC Package Example
MCMC Package Example Charles J. Geyer April 4, 2005 This is an example of using the mcmc package in R. The problem comes from a take-home question on a (take-home) PhD qualifying exam (School of Statistics,
Let us assume that we are measuring the yield of a crop plant on 5 different plots at 4 different observation times.
Mixed-effects models An introduction by Christoph Scherber Up to now, we have been dealing with linear models of the form where ß0 and ß1 are parameters of fixed value. Example: Let us assume that we are
Projects for Bayesian Computation with R
Projects for Bayesian Computation with R Laura Vana & Kurt Hornik Winter Semeter 2018/2019 1 S&P Rating Data On the homepage of this course you can find a time series for Standard & Poors default data
Generalized Multilevel Regression Example for a Binary Outcome
Psy 510/610 Multilevel Regression, Spring 2017 1 HLM Generalized Multilevel Regression Example for a Binary Outcome Specifications for this Bernoulli HLM2 run Problem Title: no title The data source for
Dummy Variables. 1. Example: Factors Affecting Monthly Earnings
Dummy Variables A dummy variable or binary variable is a variable that takes on a value of 0 or 1 as an indicator that the observation has some kind of characteristic. Common examples: Sex (female): FEMALE=1
Non-linearities in Simple Regression
Non-linearities in Simple Regression 1. Eample: Monthly Earnings and Years of Education In this tutorial, we will focus on an eample that eplores the relationship between total monthly earnings and years
The SAS System 11:03 Monday, November 11,
The SAS System 11:3 Monday, November 11, 213 1 The CONTENTS Procedure Data Set Name BIO.AUTO_PREMIUMS Observations 5 Member Type DATA Variables 3 Engine V9 Indexes Created Monday, November 11, 213 11:4:19
ORDERED MULTINOMIAL LOGISTIC REGRESSION ANALYSIS. Pooja Shivraj Southern Methodist University
ORDERED MULTINOMIAL LOGISTIC REGRESSION ANALYSIS Pooja Shivraj Southern Methodist University KINDS OF REGRESSION ANALYSES Linear Regression Logistic Regression Dichotomous dependent variable (yes/no, died/
Categorical Outcomes. Statistical Modelling in Stata: Categorical Outcomes. R by C Table: Example. Nominal Outcomes. Mark Lunt.
Categorical Outcomes Statistical Modelling in Stata: Categorical Outcomes Mark Lunt Arthritis Research UK Epidemiology Unit University of Manchester Nominal Ordinal 28/11/2017 R by C Table: Example Categorical,
Determining Probability Estimates From Logistic Regression Results Vartanian: SW 541
Determining Probability Estimates From Logistic Regression Results Vartanian: SW 541 In determining logistic regression results, you will generally be given the odds ratio in the SPSS or SAS output. However,
book 2014/5/6 15:21 page 261 #285
book 2014/5/6 15:21 page 261 #285 Chapter 10 Simulation Simulations provide a powerful way to answer questions and explore properties of statistical estimators and procedures. In this chapter, we will
proc genmod; model malform/total = alcohol / dist=bin link=identity obstats; title 'Table 2.7'; title2 'Identity Link';
BIOS 6244 Analysis of Categorical Data Assignment 5 s 1. Consider Exercise 4.4, p. 98. (i) Write the SAS code, including the DATA step, to fit the linear probability model and the logit model to the data
6 Multiple Regression
More than one X variable. 6 Multiple Regression Why? Might be interested in more than one marginal effect Omitted Variable Bias (OVB) 6.1 and 6.2 House prices and OVB Should I build a fireplace? The following
Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Midterm
Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay Midterm GSB Honor Code: I pledge my honor that I have not violated the Honor Code during this examination.
2 H PLH L PLH visit trt group rel N 1 H PHL L PHL P PLH P PHL 5 16
Biostatistics 140.655 ongitudinal Data Analysis Tom Travison ongitudinal GM with GEE - Example ain Crossover Trial Data (Text page 13) Binomial Outcome: % atients Experiencing Relief on Different Drug
Lecture Note: Analysis of Financial Time Series Spring 2017, Ruey S. Tsay
Lecture Note: Analysis of Financial Time Series Spring 2017, Ruey S. Tsay Seasonal Time Series: TS with periodic patterns and useful in predicting quarterly earnings pricing weather-related derivatives
Final Exam - section 1. Thursday, December hours, 30 minutes
Econometrics, ECON312 San Francisco State University Michael Bar Fall 2013 Final Exam - section 1 Thursday, December 19 1 hours, 30 minutes Name: Instructions 1. This is closed book, closed notes exam.
Empirical Asset Pricing for Tactical Asset Allocation
Introduction Process Model Conclusion Department of Finance The University of Connecticut School of Business stephen.r.rush@gmail.com May 10, 2012 Background Portfolio Managers Want to justify fees with
Lapse Modeling for the Post-Level Period
Lapse Modeling for the Post-Level Period A Practical Application of Predictive Modeling JANUARY 2015 SPONSORED BY Committee on Finance Research PREPARED BY Richard Xu, FSA, Ph.D. Dihui Lai, Ph.D. Minyu
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay. Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay Final Exam Booth Honor Code: I pledge my honor that I have not violated the Honor Code during this
Logit Models for Binary Data
Chapter 3 Logit Models for Binary Data We now turn our attention to regression models for dichotomous data, including logistic regression and probit analysis These models are appropriate when the response
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
Copyright 2005 Pearson Education, Inc. Slide 6-1
Copyright 2005 Pearson Education, Inc. Slide 6-1 Chapter 6 Copyright 2005 Pearson Education, Inc. Measures of Center in a Distribution 6-A The mean is what we most commonly call the average value. It is
COMPREHENSIVE WRITTEN EXAMINATION, PAPER III FRIDAY AUGUST 18, 2006, 9:00 A.M. 1:00 P.M. STATISTICS 174 QUESTIONS
COMPREHENSIVE WRITTEN EXAMINATION, PAPER III FRIDAY AUGUST 18, 2006, 9:00 A.M. 1:00 P.M. STATISTICS 174 QUESTIONS Answer all parts. Closed book, calculators allowed. It is important to show all working,
Lecture Note: Analysis of Financial Time Series Spring 2008, Ruey S. Tsay. Seasonal Time Series: TS with periodic patterns and useful in
Lecture Note: Analysis of Financial Time Series Spring 2008, Ruey S. Tsay Seasonal Time Series: TS with periodic patterns and useful in predicting quarterly earnings pricing weather-related derivatives
Duration Models: Parametric Models
Duration Models: Parametric Models Brad 1 1 Department of Political Science University of California, Davis January 28, 2011 Parametric Models Some Motivation for Parametrics Consider the hazard rate:
> > is.factor(scabdata$trt) [1] TRUE > is.ordered(scabdata$trt) [1] FALSE > scabdata$trtord <- ordered(scabdata$trt, +
![> > is.factor(scabdata$trt) [1] TRUE > is.ordered(scabdata$trt) [1] FALSE > scabdata$trtord <- ordered(scabdata$trt, +](/thumbs/92/110469326.jpg "> > is.factor(scabdata$trt) [1] TRUE > is.ordered(scabdata$trt) [1] FALSE > scabdata$trtord <- ordered(scabdata$trt, +")
Output from scab1.r # scab1.r scabdata
Predicting the Direction of Swap Spreads
Predicting the Direction of Swap Spreads Paul Teetor March, 2007 Abstract A model is developed for predicting the direction of 10-year swap spreads from related financial time series, such as Treasury
Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Final Exam
Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay Final Exam GSB Honor Code: I pledge my honor that I have not violated the Honor Code during this
MODEL SELECTION CRITERIA IN R:
1. R 2 statistics We may use MODEL SELECTION CRITERIA IN R R 2 = SS R SS T = 1 SS Res SS T or R 2 Adj = 1 SS Res/(n p) SS T /(n 1) = 1 ( ) n 1 (1 R 2 ). n p where p is the total number of parameters. R
Module 9: Single-level and Multilevel Models for Ordinal Responses. Stata Practical 1
Module 9: Single-level and Multilevel Models for Ordinal Responses Pre-requisites Modules 5, 6 and 7 Stata Practical 1 George Leckie, Tim Morris & Fiona Steele Centre for Multilevel Modelling If you find
Intro to GLM Day 2: GLM and Maximum Likelihood
Intro to GLM Day 2: GLM and Maximum Likelihood Federico Vegetti Central European University ECPR Summer School in Methods and Techniques 1 / 32 Generalized Linear Modeling 3 steps of GLM 1. Specify the
Two-phase designs in epidemiology
Two-phase designs in epidemiology Thomas Lumley May 15, 2006 This document explains how to analyse case cohort and two-phase case control studies with the survey package, using examples from http://faculty.washington.edu/norm/software.
Study 2: data analysis. Example analysis using R
Study 2: data analysis Example analysis using R Steps for data analysis Install software on your computer or locate computer with software (e.g., R, systat, SPSS) Prepare data for analysis Subjects (rows)
MCMC Package Example (Version 0.5-1)
MCMC Package Example (Version 0.5-1) Charles J. Geyer September 16, 2005 1 The Problem This is an example of using the mcmc package in R. The problem comes from a take-home question on a (take-home) PhD
Hypothesis Tests: One Sample Mean Cal State Northridge Ψ320 Andrew Ainsworth PhD
Hypothesis Tests: One Sample Mean Cal State Northridge Ψ320 Andrew Ainsworth PhD MAJOR POINTS Sampling distribution of the mean revisited Testing hypotheses: sigma known An example Testing hypotheses:
Regression and Simulation
Regression and Simulation This is an introductory R session, so it may go slowly if you have never used R before. Do not be discouraged. A great way to learn a new language like this is to plunge right
Monetary Economics Risk and Return, Part 2. Gerald P. Dwyer Fall 2015
Monetary Economics Risk and Return, Part 2 Gerald P. Dwyer Fall 2015 Reading Malkiel, Part 2, Part 3 Malkiel, Part 3 Outline Returns and risk Overall market risk reduced over longer periods Individual
Exchange Rate Regime Analysis for the Indian Rupee
Exchange Rate Regime Analysis for the Indian Rupee Achim Zeileis Ajay Shah Ila Patnaik Abstract We investigate the Indian exchange rate regime starting from 1993 when trading in the Indian rupee began.
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2017, Mr. Ruey S. Tsay. Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2017, Mr. Ruey S. Tsay Final Exam Booth Honor Code: I pledge my honor that I have not violated the Honor Code during this
Creation of Synthetic Discrete Response Regression Models
Arizona State University From the SelectedWorks of Joseph M Hilbe 2010 Creation of Synthetic Discrete Response Regression Models Joseph Hilbe, Arizona State University Available at: https://works.bepress.com/joseph_hilbe/2/
Ordinal and categorical variables
Ordinal and categorical variables Ben Bolker October 29, 2018 Licensed under the Creative Commons attribution-noncommercial license (http: //creativecommons.org/licenses/by-nc/3.0/). Please share & remix
ARIMA ANALYSIS WITH INTERVENTIONS / OUTLIERS
TASK Run intervention analysis on the price of stock M: model a function of the price as ARIMA with outliers and interventions. SOLUTION The document below is an abridged version of the solution provided
STA 4504/5503 Sample questions for exam True-False questions.
STA 4504/5503 Sample questions for exam 2 1. True-False questions. (a) For General Social Survey data on Y = political ideology (categories liberal, moderate, conservative), X 1 = gender (1 = female, 0
[BINARY DEPENDENT VARIABLE ESTIMATION WITH STATA]
![[BINARY DEPENDENT VARIABLE ESTIMATION WITH STATA]](/thumbs/85/91912134.jpg "[BINARY DEPENDENT VARIABLE ESTIMATION WITH STATA]")
Tutorial #3 This example uses data in the file 16.09.2011.dta under Tutorial folder. It contains 753 observations from a sample PSID data on the labor force status of married women in the U.S in 1975.
Chapter 4 Level of Volatility in the Indian Stock Market
Chapter 4 Level of Volatility in the Indian Stock Market Measurement of volatility is an important issue in financial econometrics. The main reason for the prominent role that volatility plays in financial
An Empirical Study on Default Factors for US Sub-prime Residential Loans
An Empirical Study on Default Factors for US Sub-prime Residential Loans Kai-Jiun Chang, Ph.D. Candidate, National Taiwan University, Taiwan ABSTRACT This research aims to identify the loan characteristics
EC327: Limited Dependent Variables and Sample Selection Binomial probit: probit
EC327: Limited Dependent Variables and Sample Selection Binomial probit: probit. summarize work age married children education Variable Obs Mean Std. Dev. Min Max work 2000.6715.4697852 0 1 age 2000 36.208
WesVar uses repeated replication variance estimation methods exclusively and as a result does not offer the Taylor Series Linearization approach.
CHAPTER 9 ANALYSIS EXAMPLES REPLICATION WesVar 4.3 GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for analysis of
Building and Checking Survival Models
Building and Checking Survival Models David M. Rocke May 23, 2017 David M. Rocke Building and Checking Survival Models May 23, 2017 1 / 53 hodg Lymphoma Data Set from KMsurv This data set consists of information
Random Effects ANOVA
Random Effects ANOVA Grant B. Morgan Baylor University This post contains code for conducting a random effects ANOVA. Make sure the following packages are installed: foreign, lme4, lsr, lattice. library(foreign)
Multiple regression - a brief introduction
Multiple regression - a brief introduction Multiple regression is an extension to regular (simple) regression. Instead of one X, we now have several. Suppose, for example, that you are trying to predict
11. Logistic modeling of proportions
11. Logistic modeling of proportions Retrieve the data File on main menu Open worksheet C:\talks\strirling\employ.ws = Note Postcode is neighbourhood in Glasgow Cell is element of the table for each postcode
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay Midterm ChicagoBooth Honor Code: I pledge my honor that I have not violated the Honor Code during this
Longitudinal Logistic Regression: Breastfeeding of Nepalese Children
Longitudinal Logistic Regression: Breastfeeding of Nepalese Children Scientific Question Determine whether the breastfeeding of Nepalese children varies with child age and/or sex of child. Data: Nepal
Model fit assessment via marginal model plots
The Stata Journal (2010) 10, Number 2, pp. 215 225 Model fit assessment via marginal model plots Charles Lindsey Texas A & M University Department of Statistics College Station, TX lindseyc@stat.tamu.edu
Econ 371 Problem Set #4 Answer Sheet. 6.2 This question asks you to use the results from column (1) in the table on page 213.
Econ 371 Problem Set #4 Answer Sheet 6.2 This question asks you to use the results from column (1) in the table on page 213. a. The first part of this question asks whether workers with college degrees
1 Stat 8053, Fall 2011: GLMMs
Stat 805, Fall 0: GLMMs The data come from a 988 fertility survey in Bangladesh. Data were collected on 94 women grouped into 60 districts. The response of interest is whether or not the woman is using
Duration Models: Modeling Strategies
Bradford S., UC-Davis, Dept. of Political Science Duration Models: Modeling Strategies Brad 1 1 Department of Political Science University of California, Davis February 28, 2007 Bradford S., UC-Davis,
Occupancy models with detection error Peter Solymos and Subhash Lele July 16, 2016 Madison, WI NACCB Congress
Occupancy models with detection error Peter Solymos and Subhash Lele July 16, 2016 Madison, WI NACCB Congress Let us continue with the simple occupancy model we used previously. Most applied ecologists
Sociology 704: Topics in Multivariate Statistics Instructor: Natasha Sarkisian. Binary Logit
Sociology 704: Topics in Multivariate Statistics Instructor: Natasha Sarkisian Binary Logit Binary models deal with binary (0/1, yes/no) dependent variables. OLS is inappropriate for this kind of dependent
u panel_lecture . sum
u panel_lecture sum Variable Obs Mean Std Dev Min Max datastre 639 9039644 6369418 900228 926665 year 639 1980 2584012 1976 1984 total_sa 639 9377839 3212313 682 441e+07 tot_fixe 639 5214385 1988422 642
Lecture 1: Empirical Properties of Returns
Lecture 1: Empirical Properties of Returns Econ 589 Eric Zivot Spring 2011 Updated: March 29, 2011 Daily CC Returns on MSFT -0.3 r(t) -0.2-0.1 0.1 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Maximum Likelihood Estimation
Maximum Likelihood Estimation EPSY 905: Fundamentals of Multivariate Modeling Online Lecture #6 EPSY 905: Maximum Likelihood In This Lecture The basics of maximum likelihood estimation Ø The engine that
> attach(grocery) > boxplot(sales~discount, ylab="sales",xlab="discount")
Example of More than 2 Categories, and Analysis of Covariance Example > attach(grocery) > boxplot(sales~discount, ylab="sales",xlab="discount") Sales 160 200 240 > tapply(sales,discount,mean) 10.00% 15.00%
Girma Tefera*, Legesse Negash and Solomon Buke. Department of Statistics, College of Natural Science, Jimma University. Ethiopia.
Vol. 5(2), pp. 15-21, July, 2014 DOI: 10.5897/IJSTER2013.0227 Article Number: C81977845738 ISSN 2141-6559 Copyright 2014 Author(s) retain the copyright of this article http://www.academicjournals.org/ijster
Diploma Part 2. Quantitative Methods. Examiner s Suggested Answers
Diploma Part 2 Quantitative Methods Examiner s Suggested Answers Question 1 (a) The binomial distribution may be used in an experiment in which there are only two defined outcomes in any particular trial
Exchange Rate Regime Analysis for the Indian Rupee
Exchange Rate Regime Analysis for the Indian Rupee Achim Zeileis Ajay Shah Ila Patnaik Abstract We investigate the Indian exchange rate regime starting from 1993 when trading in the Indian rupee began
R is a collaborative project with many contributors. Type contributors() for more information.
R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type license() or licence() for distribution details. R is a collaborative project
Sociology Exam 3 Answer Key - DRAFT May 8, 2007
Sociology 63993 Exam 3 Answer Key - DRAFT May 8, 2007 I. True-False. (20 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. The odds of an event occurring
Sean Howard Econometrics Final Project Paper. An Analysis of the Determinants and Factors of Physical Education Attendance in the Fourth Quarter
Sean Howard Econometrics Final Project Paper An Analysis of the Determinants and Factors of Physical Education Attendance in the Fourth Quarter Introduction This project attempted to gain a more complete
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay. Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay Midterm ChicagoBooth Honor Code: I pledge my honor that I have not violated the Honor Code during this
Internet Appendix to The Booms and Busts of Beta Arbitrage
Internet Appendix to The Booms and Busts of Beta Arbitrage Table A1: Event Time CoBAR This table reports some basic statistics of CoBAR, the excess comovement among low beta stocks over the period 1970
Two Way ANOVA in R Solutions
Two Way ANOVA in R Solutions Solutions to exercises found here # Exercise 1 # #Read in the moth experiment data setwd("h:/datasets") moth.experiment = read.csv("moth trap experiment.csv", header = TRUE)
Market Variables and Financial Distress. Giovanni Fernandez Stetson University
Market Variables and Financial Distress Giovanni Fernandez Stetson University In this paper, I investigate the predictive ability of market variables in correctly predicting and distinguishing going concern
Maximum Likelihood Estimation Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 13, 2018
Maximum Likelihood Estimation Richard Williams, University of otre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 3, 208 [This handout draws very heavily from Regression Models for Categorical
Getting Started in Logit and Ordered Logit Regression (ver. 3.1 beta)
Getting Started in Logit and Ordered Logit Regression (ver. 3. beta Oscar Torres-Reyna Data Consultant otorres@princeton.edu http://dss.princeton.edu/training/ Logit model Use logit models whenever your
STK Lecture 7 finalizing clam size modelling and starting on pricing
STK 4540 Lecture 7 finalizing clam size modelling and starting on pricing Overview Important issues Models treated Curriculum Duration (in lectures) What is driving the result of a nonlife insurance company?