Ordered Logit Models Vartanian: SW 541
|
|
|
- Branden Phillips
- 7 years ago
- Views:
Transcription
1 Ordered Logit Models Vartanian: SW 541 You are examining an ordered logit model the age at first marriage. You have a 3 level dependent variable for age at first marriage. Level 1 indicates the person was first married as a teenager, level 2 indicates the person was married in their 20s, and level 3 indicates that they were married in their 30s. You examine the effects of living in poverty as a child and the type of city they lived in as a child (with very rural areas being the excluded group). You get the results presented below. A. What do the odds ratios indicate for the independent variables? B. What do the probability estimates indicate? C. Are the effects of the independent variables proportional? C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 1
2 The LOGISTIC Procedure Model Information Data Set WORK.Z Response Variable firstm Number of Response Levels 3 Number of Observations 2809 Weight Variable WEIGHT Sum of Weights 2809 Model cumulative logit Optimization Technique Fisher's scoring Response Profile Ordered Total Total Value firstm Frequency Weight Probabilities modeled are cumulated over the lower Ordered Values. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Score Test for the Proportional Odds Assumption Chi-Square DF Pr > ChiSq Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC SC Log L C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 2
3 The SAS System 07:46 Monday, April 28, The LOGISTIC Procedure Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio <.0001 Score <.0001 Wald <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept <.0001 Intercept <.0001 INPOV BIGCITY <.0001 URBANY CITY3Y SUBY RURY Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits INPOV BIGCITY URBANY CITY3Y SUBY RURY Association of Predicted Probabilities and Observed Responses Percent Concordant 50.8 Somers' D Percent Discordant 35.5 Gamma Percent Tied 13.8 Tau-a Pairs c C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 3
4 The SAS System 07:46 Monday, April 28, Response Value= The MEANS Procedure ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ PROB Estimated Probability _LEVEL_ Response Value pr_pov pr_npov pr_vrur pr_rur pr_suby pr_city3y pr_urbany pr_big ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Response Value= ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ PROB Estimated Probability _LEVEL_ Response Value pr_pov pr_npov pr_vrur pr_rur pr_suby pr_city3y pr_urbany pr_big ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 4
5 You are examining an ordered model for number of marriages (I m searching for an ordered variable here). You examining city of residence and poverty level as independent variables. You come up with the following results. A. What do the odds ratios indicate for the independent variables? B. What do the probability estimates indicate? C. Are the effects of the independent variables proportional? The SAS System 07:46 Monday, April 28, The LOGISTIC Procedure Model Information Data Set WORK.Z Response Variable NUMMARR Number of Response Levels 5 Number of Observations 2746 Weight Variable WEIGHT Sum of Weights 2746 Model cumulative logit Optimization Technique Fisher's scoring Response Profile Ordered Total Total Value NUMMARR Frequency Weight Probabilities modeled are cumulated over the lower Ordered Values. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Score Test for the Proportional Odds Assumption Chi-Square DF Pr > ChiSq Model Fit Statistics Intercept Intercept and C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 5
6 Criterion Only Covariates AIC SC Log L C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 6
7 The SAS System 07:46 Monday, April 28, The LOGISTIC Procedure Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio <.0001 Score <.0001 Wald <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept <.0001 Intercept <.0001 Intercept <.0001 Intercept <.0001 INPOV BIGCITY URBANY CITY3Y SUBY RURY Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits INPOV BIGCITY URBANY CITY3Y SUBY RURY Association of Predicted Probabilities and Observed Responses Percent Concordant 27.1 Somers' D Percent Discordant 21.7 Gamma Percent Tied 51.2 Tau-a Pairs c C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 7
8 The SAS System 07:46 Monday, April 28, Response Value= The MEANS Procedure PROB Estimated Probability _LEVEL_ Response Value pr_pov pr_npov pr_vrur pr_rur pr_suby pr_city3y pr_urbany pr_big Response Value= PROB Estimated Probability _LEVEL_ Response Value pr_pov pr_npov pr_vrur pr_rur pr_suby pr_city3y pr_urbany pr_big Response Value= PROB Estimated Probability _LEVEL_ Response Value pr_pov pr_npov pr_vrur pr_rur pr_suby pr_city3y pr_urbany pr_big C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 8
9 The SAS System 07:46 Monday, April 28, Response Value= The MEANS Procedure PROB Estimated Probability _LEVEL_ Response Value pr_pov pr_npov pr_vrur pr_rur pr_suby pr_city3y pr_urbany pr_big C:\WP60_1\Lect2.phd\FINALrev\ordlogitfinal.doc 9
Using An Ordered Logistic Regression Model with SAS Vartanian: SW 541
Using An Ordered Logistic Regression Model with SAS Vartanian: SW 541 libname in1 >c:\=; Data first; Set in1.extract; A=1; PROC LOGIST OUTEST=DD MAXITER=100 ORDER=DATA; OUTPUT OUT=CC XBETA=XB P=PROB; MODEL
Basic Statistical and Modeling Procedures Using SAS
Basic Statistical and Modeling Procedures Using SAS One-Sample Tests The statistical procedures illustrated in this handout use two datasets. The first, Pulse, has information collected in a classroom
VI. Introduction to Logistic Regression
VI. Introduction to Logistic Regression We turn our attention now to the topic of modeling a categorical outcome as a function of (possibly) several factors. The framework of generalized linear models
11. Analysis of Case-control Studies Logistic Regression
Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
2013 CRS Research Report MOTORCYCLE SAFETY AND DRIVING UNDER INFLUENCE OF ALCOHOL
2013 CRS Research Report MOTORCYCLE SAFETY AND DRIVING UNDER INFLUENCE OF ALCOHOL Final Report by Andrew P. Tarko, Ph.D. Jose Thomaz CENTER FOR ROAD SAFETY SCHOOL OF CIVIL ENGINEERING PURDUE UNIVERSITY
Statistics, Data Analysis & Econometrics
Using the LOGISTIC Procedure to Model Responses to Financial Services Direct Marketing David Marsh, Senior Credit Risk Modeler, Canadian Tire Financial Services, Welland, Ontario ABSTRACT It is more important
Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@
Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@ Yanchun Xu, Andrius Kubilius Joint Commission on Accreditation of Healthcare Organizations,
Segmentation For Insurance Payments Michael Sherlock, Transcontinental Direct, Warminster, PA
Segmentation For Insurance Payments Michael Sherlock, Transcontinental Direct, Warminster, PA ABSTRACT An online insurance agency has built a base of names that responded to different offers from various
I L L I N O I S UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
Beckman HLM Reading Group: Questions, Answers and Examples Carolyn J. Anderson Department of Educational Psychology I L L I N O I S UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Linear Algebra Slide 1 of
Ordinal Regression. Chapter
Ordinal Regression Chapter 4 Many variables of interest are ordinal. That is, you can rank the values, but the real distance between categories is unknown. Diseases are graded on scales from least severe
Lecture 19: Conditional Logistic Regression
Lecture 19: Conditional Logistic Regression Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South Carolina
Generalized Linear Models
Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the
Cool Tools for PROC LOGISTIC
Cool Tools for PROC LOGISTIC Paul D. Allison Statistical Horizons LLC and the University of Pennsylvania March 2013 www.statisticalhorizons.com 1 New Features in LOGISTIC ODDSRATIO statement EFFECTPLOT
Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)
Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and
Multinomial and Ordinal Logistic Regression
Multinomial and Ordinal Logistic Regression ME104: Linear Regression Analysis Kenneth Benoit August 22, 2012 Regression with categorical dependent variables When the dependent variable is categorical,
Lecture 14: GLM Estimation and Logistic Regression
Lecture 14: GLM Estimation and Logistic Regression Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South
Assessing Course Effectiveness Learning Analytics. Jere Turner,PhD Director of Institutional Research Manchester Community College
Assessing Course Effectiveness Learning Analytics Jere Turner,PhD Director of Institutional Research Manchester Community College Background MCC is one of seven CC in NH Data warehouse built in 2004 DI
STATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
This can dilute the significance of a departure from the null hypothesis. We can focus the test on departures of a particular form.
One-Degree-of-Freedom Tests Test for group occasion interactions has (number of groups 1) number of occasions 1) degrees of freedom. This can dilute the significance of a departure from the null hypothesis.
PROC LOGISTIC: Traps for the unwary Peter L. Flom, Independent statistical consultant, New York, NY
PROC LOGISTIC: Traps for the unwary Peter L. Flom, Independent statistical consultant, New York, NY ABSTRACT Keywords: Logistic. INTRODUCTION This paper covers some gotchas in SAS R PROC LOGISTIC. A gotcha
A LOGISTIC REGRESSION MODEL TO PREDICT FRESHMEN ENROLLMENTS Vijayalakshmi Sampath, Andrew Flagel, Carolina Figueroa
A LOGISTIC REGRESSION MODEL TO PREDICT FRESHMEN ENROLLMENTS Vijayalakshmi Sampath, Andrew Flagel, Carolina Figueroa ABSTRACT Predictive modeling is the technique of using historical information on a certain
SAS Software to Fit the Generalized Linear Model
SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling
MORE ON LOGISTIC REGRESSION
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 816 MORE ON LOGISTIC REGRESSION I. AGENDA: A. Logistic regression 1. Multiple independent variables 2. Example: The Bell Curve 3. Evaluation
SUGI 29 Statistics and Data Analysis
Paper 194-29 Head of the CLASS: Impress your colleagues with a superior understanding of the CLASS statement in PROC LOGISTIC Michelle L. Pritchard and David J. Pasta Ovation Research Group, San Francisco,
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
ln(p/(1-p)) = α +β*age35plus, where p is the probability or odds of drinking
Dummy Coding for Dummies Kathryn Martin, Maternal, Child and Adolescent Health Program, California Department of Public Health ABSTRACT There are a number of ways to incorporate categorical variables into
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing. C. Olivia Rud, VP, Fleet Bank
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Rud, VP, Fleet Bank ABSTRACT Data Mining is a new term for the common practice of searching through
Multinomial and ordinal logistic regression using PROC LOGISTIC Peter L. Flom National Development and Research Institutes, Inc
ABSTRACT Multinomial and ordinal logistic regression using PROC LOGISTIC Peter L. Flom National Development and Research Institutes, Inc Logistic regression may be useful when we are trying to model a
A Handbook of Statistical Analyses Using R. Brian S. Everitt and Torsten Hothorn
A Handbook of Statistical Analyses Using R Brian S. Everitt and Torsten Hothorn CHAPTER 6 Logistic Regression and Generalised Linear Models: Blood Screening, Women s Role in Society, and Colonic Polyps
Modeling Lifetime Value in the Insurance Industry
Modeling Lifetime Value in the Insurance Industry C. Olivia Parr Rud, Executive Vice President, Data Square, LLC ABSTRACT Acquisition modeling for direct mail insurance has the unique challenge of targeting
Logistic Regression (a type of Generalized Linear Model)
Logistic Regression (a type of Generalized Linear Model) 1/36 Today Review of GLMs Logistic Regression 2/36 How do we find patterns in data? We begin with a model of how the world works We use our knowledge
Chi-square test Fisher s Exact test
Lesson 1 Chi-square test Fisher s Exact test McNemar s Test Lesson 1 Overview Lesson 11 covered two inference methods for categorical data from groups Confidence Intervals for the difference of two proportions
Odds ratio, Odds ratio test for independence, chi-squared statistic.
Odds ratio, Odds ratio test for independence, chi-squared statistic. Announcements: Assignment 5 is live on webpage. Due Wed Aug 1 at 4:30pm. (9 days, 1 hour, 58.5 minutes ) Final exam is Aug 9. Review
Poisson Models for Count Data
Chapter 4 Poisson Models for Count Data In this chapter we study log-linear models for count data under the assumption of a Poisson error structure. These models have many applications, not only to the
SAS Syntax and Output for Data Manipulation:
Psyc 944 Example 5 page 1 Practice with Fixed and Random Effects of Time in Modeling Within-Person Change The models for this example come from Hoffman (in preparation) chapter 5. We will be examining
Indices of Model Fit STRUCTURAL EQUATION MODELING 2013
Indices of Model Fit STRUCTURAL EQUATION MODELING 2013 Indices of Model Fit A recommended minimal set of fit indices that should be reported and interpreted when reporting the results of SEM analyses:
Categorical Data Analysis
Richard L. Scheaffer University of Florida The reference material and many examples for this section are based on Chapter 8, Analyzing Association Between Categorical Variables, from Statistical Methods
Charles Secolsky County College of Morris. Sathasivam 'Kris' Krishnan The Richard Stockton College of New Jersey
Using logistic regression for validating or invalidating initial statewide cut-off scores on basic skills placement tests at the community college level Abstract Charles Secolsky County College of Morris
Beginning Tutorials. PROC FREQ: It s More Than Counts Richard Severino, The Queen s Medical Center, Honolulu, HI OVERVIEW.
Paper 69-25 PROC FREQ: It s More Than Counts Richard Severino, The Queen s Medical Center, Honolulu, HI ABSTRACT The FREQ procedure can be used for more than just obtaining a simple frequency distribution
Simple Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
Binary Logistic Regression
Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including
Weight of Evidence Module
Formula Guide The purpose of the Weight of Evidence (WoE) module is to provide flexible tools to recode the values in continuous and categorical predictor variables into discrete categories automatically,
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
The Probit Link Function in Generalized Linear Models for Data Mining Applications
Journal of Modern Applied Statistical Methods Copyright 2013 JMASM, Inc. May 2013, Vol. 12, No. 1, 164-169 1538 9472/13/$95.00 The Probit Link Function in Generalized Linear Models for Data Mining Applications
Failure to take the sampling scheme into account can lead to inaccurate point estimates and/or flawed estimates of the standard errors.
Analyzing Complex Survey Data: Some key issues to be aware of Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 24, 2015 Rather than repeat material that is
ABSTRACT INTRODUCTION STUDY DESCRIPTION
ABSTRACT Paper 1675-2014 Validating Self-Reported Survey Measures Using SAS Sarah A. Lyons MS, Kimberly A. Kaphingst ScD, Melody S. Goodman PhD Washington University School of Medicine Researchers often
Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.)
Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.) Logistic regression generalizes methods for 2-way tables Adds capability studying several predictors, but Limited to
Online Appendix to Are Risk Preferences Stable Across Contexts? Evidence from Insurance Data
Online Appendix to Are Risk Preferences Stable Across Contexts? Evidence from Insurance Data By LEVON BARSEGHYAN, JEFFREY PRINCE, AND JOSHUA C. TEITELBAUM I. Empty Test Intervals Here we discuss the conditions
LOGIT AND PROBIT ANALYSIS
LOGIT AND PROBIT ANALYSIS A.K. Vasisht I.A.S.R.I., Library Avenue, New Delhi 110 012 amitvasisht@iasri.res.in In dummy regression variable models, it is assumed implicitly that the dependent variable Y
Statistics 305: Introduction to Biostatistical Methods for Health Sciences
Statistics 305: Introduction to Biostatistical Methods for Health Sciences Modelling the Log Odds Logistic Regression (Chap 20) Instructor: Liangliang Wang Statistics and Actuarial Science, Simon Fraser
The Cox Proportional Hazards Model
The Cox Proportional Hazards Model Mario Chen, PhD Advanced Biostatistics and RCT Workshop Office of AIDS Research, NIH ICSSC, FHI Goa, India, September 2009 1 The Model h i (t)=h 0 (t)exp(z i ), Z i =
Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
Nominal and ordinal logistic regression
Nominal and ordinal logistic regression April 26 Nominal and ordinal logistic regression Our goal for today is to briefly go over ways to extend the logistic regression model to the case where the outcome
6 Variables: PD MF MA K IAH SBS
options pageno=min nodate formdlim='-'; title 'Canonical Correlation, Journal of Interpersonal Violence, 10: 354-366.'; data SunitaPatel; infile 'C:\Users\Vati\Documents\StatData\Sunita.dat'; input Group
Examining a Fitted Logistic Model
STAT 536 Lecture 16 1 Examining a Fitted Logistic Model Deviance Test for Lack of Fit The data below describes the male birth fraction male births/total births over the years 1931 to 1990. A simple logistic
THE EFFECT OF AGE AND TYPE OF ADVERTISING MEDIA EXPOSURE ON THE LIKELIHOOD OF RETURNING A CENSUS FORM IN THE 1998 CENSUS DRESS REHEARSAL
THE EFFECT OF AGE AND TYPE OF ADVERTISING MEDIA EXPOSURE ON THE LIKELIHOOD OF RETURNING A CENSUS FORM IN THE 1998 CENSUS DRESS REHEARSAL James Poyer, U.S. Bureau of the Census James Poyer, U.S. Bureau
IBM SPSS Regression 20
IBM SPSS Regression 20 Note: Before using this information and the product it supports, read the general information under Notices on p. 41. This edition applies to IBM SPSS Statistics 20 and to all subsequent
Interpretation of Somers D under four simple models
Interpretation of Somers D under four simple models Roger B. Newson 03 September, 04 Introduction Somers D is an ordinal measure of association introduced by Somers (96)[9]. It can be defined in terms
Random effects and nested models with SAS
Random effects and nested models with SAS /************* classical2.sas ********************* Three levels of factor A, four levels of B Both fixed Both random A fixed, B random B nested within A ***************************************************/
Section 12 Part 2. Chi-square test
Section 12 Part 2 Chi-square test McNemar s Test Section 12 Part 2 Overview Section 12, Part 1 covered two inference methods for categorical data from 2 groups Confidence Intervals for the difference of
Common Univariate and Bivariate Applications of the Chi-square Distribution
Common Univariate and Bivariate Applications of the Chi-square Distribution The probability density function defining the chi-square distribution is given in the chapter on Chi-square in Howell's text.
i SPSS Regression 17.0
i SPSS Regression 17.0 For more information about SPSS Inc. software products, please visit our Web site at http://www.spss.com or contact SPSS Inc. 233 South Wacker Drive, 11th Floor Chicago, IL 60606-6412
Multivariate Logistic Regression
1 Multivariate Logistic Regression As in univariate logistic regression, let π(x) represent the probability of an event that depends on p covariates or independent variables. Then, using an inv.logit formulation
End User Satisfaction With a Food Manufacturing ERP
Applied Mathematical Sciences, Vol. 8, 2014, no. 24, 1187-1192 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.4284 End-User Satisfaction in ERP System: Application of Logit Modeling Hashem
How to set the main menu of STATA to default factory settings standards
University of Pretoria Data analysis for evaluation studies Examples in STATA version 11 List of data sets b1.dta (To be created by students in class) fp1.xls (To be provided to students) fp1.txt (To be
Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
Job Insecurity Measures as Predictors of Workers Compensation filing
Job Insecurity Measures as Predictors of Workers Compensation filing HeeKyoung Chun Lenore Azaroff, Robert Karasek, Rafael Moure-Eraso, Sangwoo Tak UMass, SHE, WE Background: Job insecurity Globalization
Recommend Continued CPS Monitoring. 63 (a) 17 (b) 10 (c) 90. 35 (d) 20 (e) 25 (f) 80. Totals/Marginal 98 37 35 170
Work Sheet 2: Calculating a Chi Square Table 1: Substance Abuse Level by ation Total/Marginal 63 (a) 17 (b) 10 (c) 90 35 (d) 20 (e) 25 (f) 80 Totals/Marginal 98 37 35 170 Step 1: Label Your Table. Label
CHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
Introduction to Quantitative Methods
Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................
Applied Statistics. J. Blanchet and J. Wadsworth. Institute of Mathematics, Analysis, and Applications EPF Lausanne
Applied Statistics J. Blanchet and J. Wadsworth Institute of Mathematics, Analysis, and Applications EPF Lausanne An MSc Course for Applied Mathematicians, Fall 2012 Outline 1 Model Comparison 2 Model
Chapter 29 The GENMOD Procedure. Chapter Table of Contents
Chapter 29 The GENMOD Procedure Chapter Table of Contents OVERVIEW...1365 WhatisaGeneralizedLinearModel?...1366 ExamplesofGeneralizedLinearModels...1367 TheGENMODProcedure...1368 GETTING STARTED...1370
Logistic Regression (1/24/13)
STA63/CBB540: Statistical methods in computational biology Logistic Regression (/24/3) Lecturer: Barbara Engelhardt Scribe: Dinesh Manandhar Introduction Logistic regression is model for regression used
LOGISTIC REGRESSION ANALYSIS
LOGISTIC REGRESSION ANALYSIS C. Mitchell Dayton Department of Measurement, Statistics & Evaluation Room 1230D Benjamin Building University of Maryland September 1992 1. Introduction and Model Logistic
Data Mining and Data Warehousing. Henryk Maciejewski. Data Mining Predictive modelling: regression
Data Mining and Data Warehousing Henryk Maciejewski Data Mining Predictive modelling: regression Algorithms for Predictive Modelling Contents Regression Classification Auxiliary topics: Estimation of prediction
Multiple logistic regression analysis of cigarette use among high school students
Multiple logistic regression analysis of cigarette use among high school students ABSTRACT Joseph Adwere-Boamah Alliant International University A binary logistic regression analysis was performed to predict
Chapter VIII Customers Perception Regarding Health Insurance
Chapter VIII Customers Perception Regarding Health Insurance This chapter deals with the analysis of customers perception regarding health insurance and involves its examination at series of stages i.e.
Introduction. Survival Analysis. Censoring. Plan of Talk
Survival Analysis Mark Lunt Arthritis Research UK Centre for Excellence in Epidemiology University of Manchester 01/12/2015 Survival Analysis is concerned with the length of time before an event occurs.
Fraud Risk Prediction in Merchant-Bank Relationship using Regression Modeling
R E S E A R C H includes research articles that focus on the analysis and resolution of managerial and academic issues based on analytical and empirical or case research Fraud Risk Prediction in Merchant-Bank
Survival analysis methods in Insurance Applications in car insurance contracts
Survival analysis methods in Insurance Applications in car insurance contracts Abder OULIDI 1-2 Jean-Marie MARION 1 Hérvé GANACHAUD 3 1 Institut de Mathématiques Appliquées (IMA) Angers France 2 Institut
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association
13. Poisson Regression Analysis
136 Poisson Regression Analysis 13. Poisson Regression Analysis We have so far considered situations where the outcome variable is numeric and Normally distributed, or binary. In clinical work one often
5 Modeling Survival Data with Parametric Regression
5 Modeling Survival Data with Parametric Regression Models 5. The Accelerated Failure Time Model Before talking about parametric regression models for survival data, let us introduce the accelerated failure
Linda K. Muthén Bengt Muthén. Copyright 2008 Muthén & Muthén www.statmodel.com. Table Of Contents
Mplus Short Courses Topic 2 Regression Analysis, Eploratory Factor Analysis, Confirmatory Factor Analysis, And Structural Equation Modeling For Categorical, Censored, And Count Outcomes Linda K. Muthén
Developing Business Failure Prediction Models Using SAS Software Oki Kim, Statistical Analytics
Paper SD-004 Developing Business Failure Prediction Models Using SAS Software Oki Kim, Statistical Analytics ABSTRACT The credit crisis of 2008 has changed the climate in the investment and finance industry.
SUMAN DUVVURU STAT 567 PROJECT REPORT
SUMAN DUVVURU STAT 567 PROJECT REPORT SURVIVAL ANALYSIS OF HEROIN ADDICTS Background and introduction: Current illicit drug use among teens is continuing to increase in many countries around the world.
ADVANCED FORECASTING MODELS USING SAS SOFTWARE
ADVANCED FORECASTING MODELS USING SAS SOFTWARE Girish Kumar Jha IARI, Pusa, New Delhi 110 012 gjha_eco@iari.res.in 1. Transfer Function Model Univariate ARIMA models are useful for analysis and forecasting
Two Correlated Proportions (McNemar Test)
Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with
CHAPTER 5 COMPARISON OF DIFFERENT TYPE OF ONLINE ADVERTSIEMENTS. Table: 8 Perceived Usefulness of Different Advertisement Types
CHAPTER 5 COMPARISON OF DIFFERENT TYPE OF ONLINE ADVERTSIEMENTS 5.1 Descriptive Analysis- Part 3 of Questionnaire Table 8 shows the descriptive statistics of Perceived Usefulness of Banner Ads. The results
EPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST
EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeated-measures data if participants are assessed on two occasions or conditions
Psychology 405: Psychometric Theory Homework on Factor analysis and structural equation modeling
Psychology 405: Psychometric Theory Homework on Factor analysis and structural equation modeling William Revelle Department of Psychology Northwestern University Evanston, Illinois USA June, 2014 1 / 20
Chapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
Statistical Machine Learning
Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes
Regression Modeling Strategies
Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions
Caregiving Impact on Depressive Symptoms for Family Caregivers of Terminally Ill Cancer Patients in Taiwan
Caregiving Impact on Depressive Symptoms for Family Caregivers of Terminally Ill Cancer Patients in Taiwan Siew Tzuh Tang, RN, DNSc Associate Professor, School of Nursing Chang Gung University, Taiwan
ABSTRACT INTRODUCTION
Paper SP03-2009 Illustrative Logistic Regression Examples using PROC LOGISTIC: New Features in SAS/STAT 9.2 Robert G. Downer, Grand Valley State University, Allendale, MI Patrick J. Richardson, Van Andel
Research Methods & Experimental Design
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
Section 13, Part 1 ANOVA. Analysis Of Variance
Section 13, Part 1 ANOVA Analysis Of Variance Course Overview So far in this course we ve covered: Descriptive statistics Summary statistics Tables and Graphs Probability Probability Rules Probability
Notating the Multilevel Longitudinal Model. Multilevel Modeling of Longitudinal Data. Notating (cont.) Notating (cont.)
Notating the Multilevel Modeling of Longitudinal Data Recall the typical -level model Y ij = γ 00 + (γ 0 + u j )X ij + u 0j + e ij Dr. J. Kyle Roberts Southern Methodist University Simmons School of Education