Régression logistique : introduction
|
|
|
- Colleen Simmons
- 10 years ago
- Views:
Transcription
1 Chapitre 16 Introduction à la statistique avec R Régression logistique : introduction
2 Une variable à expliquer binaire Expliquer un risque suicidaire élevé en prison par La durée de la peine L existence de mesures disciplinaire Des antécédents d abus dans l enfance
3 Les limites de la régression linéaire Haut risque de suicide = a + b duree + c discip + d abus + bruit
4 Les limites de la régression linéaire Haut risque de suicide = a + b duree + c discip + d abus + bruit La distribution du «bruit» est normale, donc «a + b duree + c discip + d abus + bruit» varie entre plus et moins l infini, or la variable «haut risque de suicide» est binaire
5 Les limites de la régression linéaire prob HR suicide oui Log prob HR suicide oui 1 ( ) a b duree c discip d abus
6 Les limites de la régression linéaire prob HR suicide oui Log prob HR suicide oui 1 ( ) a b duree c discip d abus Varie entre + et - l infini
7 Une seule variable explicative Log prob haut risque de suicide 1 prob haut risque de suicide = a + b abus
8 Une seule variable explicative Log prob haut risque de suicide 1 prob haut risque de suicide = a + b abus > mod1 <- glm(suicide.hr~abus, data=smp.l, family="binomial") > summary(mod1) Call: glm(formula = suicide.hr ~ abus, family = "binomial", data = smp.l) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) < 2e-16 *** abus e-05 *** --- Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for binomial family taken to be 1) Null deviance: on 752 degrees of freedom Residual deviance: on 751 degrees of freedom (46 observations deleted due to missingness) AIC: Number of Fisher Scoring iterations: 4
9 Une seule variable explicative Log prob haut risque de suicide 1 prob haut risque de suicide = a + b abus > mod1 <- glm(suicide.hr~abus, data=smp.l, family="binomial") > summary(mod1) Call: glm(formula = suicide.hr ~ abus, family = "binomial", data = smp.l) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) < 2e-16 *** abus e-05 *** --- Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for binomial family taken to be 1) Null deviance: on 752 degrees of freedom Residual deviance: on 751 degrees of freedom (46 observations deleted due to missingness) AIC: Number of Fisher Scoring iterations: 4
10 Une seule variable explicative Log prob haut risque de suicide 1 prob haut risque de suicide = a + b abus > mod1 <- glm(suicide.hr~abus, data=smp.l, family="binomial") > summary(mod1) Call: glm(formula = suicide.hr ~ abus, family = "binomial", data = smp.l) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) < 2e-16 *** abus e-05 *** --- Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for binomial family taken to be 1) Null deviance: on 752 degrees of freedom Residual deviance: on 751 degrees of freedom (46 observations deleted due to missingness) AIC: Number of Fisher Scoring iterations: 4
11 Une seule variable explicative Log prob haut risque de suicide 1 prob haut risque de suicide = a + b abus > mod1 <- glm(suicide.hr~abus, data=smp.l, family="binomial") > summary(mod1) Call: glm(formula = suicide.hr ~ abus, family = "binomial", data = smp.l) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) < 2e-16 *** abus e-05 *** --- Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for binomial family taken to be 1) Null deviance: on 752 degrees of freedom Residual deviance: on 751 degrees of freedom (46 observations deleted due to missingness) AIC: Number of Fisher Scoring iterations: 4
12 Une seule variable explicative Log prob haut risque de suicide 1 prob haut risque de suicide = a + b abus > mod1 <- glm(suicide.hr~abus, data=smp.l, family="binomial") > summary(mod1) Call: glm(formula = suicide.hr ~ abus, family = "binomial", data = smp.l) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) < 2e-16 *** abus e-05 *** --- Signif. codes: 0 *** ** 0.01 * (Dispersion parameter for binomial family taken to be 1) Null deviance: on 752 degrees of freedom Residual deviance: on 751 degrees of freedom (46 observations deleted due to missingness) AIC: Number of Fisher Scoring iterations: 4 > exp(0.7688) [1]
13 Une seule variable explicative > exp(0.7688) [1] > library(epi) > twoby2(1-smp.l$suicide.hr, 1-smp.l$abus) 2 by 2 table analysis: Outcome : 0 Comparing : 0 vs P(0) 95% conf. interval % conf. interval Relative Risk: Sample Odds Ratio: Conditional MLE Odds Ratio: Probability difference: Exact P-value: 1e-04 Asymptotic P-value: 1e
14 Conclusion mod1 <- glm(suicide.hr~abus, data=smp.l, family="binomial") summary(mod1) exp(0,7688) library(epi) twoby2(1-smp.l$suicide.hr, 1-smp.l$abus)
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
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
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
MSwM examples. Jose A. Sanchez-Espigares, Alberto Lopez-Moreno Dept. of Statistics and Operations Research UPC-BarcelonaTech.
MSwM examples Jose A. Sanchez-Espigares, Alberto Lopez-Moreno Dept. of Statistics and Operations Research UPC-BarcelonaTech February 24, 2014 Abstract Two examples are described to illustrate the use of
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
Lab 13: Logistic Regression
Lab 13: Logistic Regression Spam Emails Today we will be working with a corpus of emails received by a single gmail account over the first three months of 2012. Just like any other email address this account
Logistic regression (with R)
Logistic regression (with R) Christopher Manning 4 November 2007 1 Theory We can transform the output of a linear regression to be suitable for probabilities by using a logit link function on the lhs as
Lecture 6: Poisson regression
Lecture 6: Poisson regression Claudia Czado TU München c (Claudia Czado, TU Munich) ZFS/IMS Göttingen 2004 0 Overview Introduction EDA for Poisson regression Estimation and testing in Poisson regression
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
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
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
Lecture 8: Gamma regression
Lecture 8: Gamma regression Claudia Czado TU München c (Claudia Czado, TU Munich) ZFS/IMS Göttingen 2004 0 Overview Models with constant coefficient of variation Gamma regression: estimation and testing
Simple example of collinearity in logistic regression
1 Confounding and Collinearity in Multivariate Logistic Regression We have already seen confounding and collinearity in the context of linear regression, and all definitions and issues remain essentially
Multiple Linear Regression
Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is
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
Personnalisez votre intérieur avec les revêtements imprimés ALYOS design
Plafond tendu à froid ALYOS technology ALYOS technology vous propose un ensemble de solutions techniques pour vos intérieurs. Spécialiste dans le domaine du plafond tendu, nous avons conçu et développé
We extended the additive model in two variables to the interaction model by adding a third term to the equation.
Quadratic Models We extended the additive model in two variables to the interaction model by adding a third term to the equation. Similarly, we can extend the linear model in one variable to the quadratic
Outline. Dispersion Bush lupine survival Quasi-Binomial family
Outline 1 Three-way interactions 2 Overdispersion in logistic regression Dispersion Bush lupine survival Quasi-Binomial family 3 Simulation for inference Why simulations Testing model fit: simulating the
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries
Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma [email protected] The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association
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
L3: Statistical Modeling with Hadoop
L3: Statistical Modeling with Hadoop Feng Li [email protected] School of Statistics and Mathematics Central University of Finance and Economics Revision: December 10, 2014 Today we are going to learn...
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
Section 6: Model Selection, Logistic Regression and more...
Section 6: Model Selection, Logistic Regression and more... Carlos M. Carvalho The University of Texas McCombs School of Business http://faculty.mccombs.utexas.edu/carlos.carvalho/teaching/ 1 Model Building
Introduction au BIM. ESEB 38170 Seyssinet-Pariset Economie de la construction email : [email protected]
Quel est l objectif? 1 La France n est pas le seul pays impliqué 2 Une démarche obligatoire 3 Une organisation plus efficace 4 Le contexte 5 Risque d erreur INTERVENANTS : - Architecte - Économiste - Contrôleur
ANALYSING LIKERT SCALE/TYPE DATA, ORDINAL LOGISTIC REGRESSION EXAMPLE IN R.
ANALYSING LIKERT SCALE/TYPE DATA, ORDINAL LOGISTIC REGRESSION EXAMPLE IN R. 1. Motivation. Likert items are used to measure respondents attitudes to a particular question or statement. One must recall
Psychology 205: Research Methods in Psychology
Psychology 205: Research Methods in Psychology Using R to analyze the data for study 2 Department of Psychology Northwestern University Evanston, Illinois USA November, 2012 1 / 38 Outline 1 Getting ready
Lucky vs. Unlucky Teams in Sports
Lucky vs. Unlucky Teams in Sports Introduction Assuming gambling odds give true probabilities, one can classify a team as having been lucky or unlucky so far. Do results of matches between lucky and unlucky
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
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
EDUCATION AND VOCABULARY MULTIPLE REGRESSION IN ACTION
EDUCATION AND VOCABULARY MULTIPLE REGRESSION IN ACTION EDUCATION AND VOCABULARY 5-10 hours of input weekly is enough to pick up a new language (Schiff & Myers, 1988). Dutch children spend 5.5 hours/day
Session 85 IF, Predictive Analytics for Actuaries: Free Tools for Life and Health Care Analytics--R and Python: A New Paradigm!
Session 85 IF, Predictive Analytics for Actuaries: Free Tools for Life and Health Care Analytics--R and Python: A New Paradigm! Moderator: David L. Snell, ASA, MAAA Presenters: Brian D. Holland, FSA, MAAA
Unrealized Gains in Stocks from the Viewpoint of Investment Risk Management
Unrealized Gains in Stocks from the Viewpoint of Investment Risk Management Naoki Matsuyama Investment Administration Department, The Neiji Mutual Life Insurance Company, 1-1 Marunouchi, 2-chome, Chiyoda-ku,
Goodness of Fit Tests for Categorical Data: Comparing Stata, R and SAS
Goodness of Fit Tests for Categorical Data: Comparing Stata, R and SAS Rino Bellocco 1,2, Sc.D. Sara Algeri 1, MS 1 University of Milano-Bicocca, Milan, Italy & 2 Karolinska Institutet, Stockholm, Sweden
Statistical Models in R
Statistical Models in R Some Examples Steven Buechler Department of Mathematics 276B Hurley Hall; 1-6233 Fall, 2007 Outline Statistical Models Linear Models in R Regression Regression analysis is the appropriate
1 Logistic Regression
Generalized Linear Models 1 May 29, 2012 Generalized Linear Models (GLM) In the previous chapter on regression, we focused primarily on the classic setting where the response y is continuous and typically
ANOVA. February 12, 2015
ANOVA February 12, 2015 1 ANOVA models Last time, we discussed the use of categorical variables in multivariate regression. Often, these are encoded as indicator columns in the design matrix. In [1]: %%R
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
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:
Stock Price Forecasting Using Information from Yahoo Finance and Google Trend
Stock Price Forecasting Using Information from Yahoo Finance and Google Trend Selene Yue Xu (UC Berkeley) Abstract: Stock price forecasting is a popular and important topic in financial and academic studies.
Local classification and local likelihoods
Local classification and local likelihoods November 18 k-nearest neighbors The idea of local regression can be extended to classification as well The simplest way of doing so is called nearest neighbor
Chapter 7: Simple linear regression Learning Objectives
Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -
Advanced Software Engineering Agile Software Engineering. Version 1.0
Advanced Software Engineering Agile Software Engineering 1 Version 1.0 Basic direction A agile method has to be A method is called agile if it follows Incremental the principles of the agile manifest.
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 Need For Speed. leads to PostgreSQL. Dimitri Fontaine [email protected]. 28 Mars 2013
The Need For Speed leads to PostgreSQL Dimitri Fontaine [email protected] 28 Mars 2013 Dimitri Fontaine [email protected] The Need For Speed 28 Mars 2013 1 / 23 Dimitri Fontaine 2ndQuadrant France
Electronic Thesis and Dissertations UCLA
Electronic Thesis and Dissertations UCLA Peer Reviewed Title: A Multilevel Longitudinal Analysis of Teaching Effectiveness Across Five Years Author: Wang, Kairong Acceptance Date: 2013 Series: UCLA Electronic
N-Way Analysis of Variance
N-Way Analysis of Variance 1 Introduction A good example when to use a n-way ANOVA is for a factorial design. A factorial design is an efficient way to conduct an experiment. Each observation has data
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,
Exchange Rate Regime Analysis for the Chinese Yuan
Exchange Rate Regime Analysis for the Chinese Yuan Achim Zeileis Ajay Shah Ila Patnaik Abstract We investigate the Chinese exchange rate regime after China gave up on a fixed exchange rate to the US dollar
Introduction ToIP/Asterisk Quelques applications Trixbox/FOP Autres distributions Conclusion. Asterisk et la ToIP. Projet tuteuré
Asterisk et la ToIP Projet tuteuré Luis Alonso Domínguez López, Romain Gegout, Quentin Hourlier, Benoit Henryon IUT Charlemagne, Licence ASRALL 2008-2009 31 mars 2009 Asterisk et la ToIP 31 mars 2009 1
Fondation Rennes 1. Atelier de l innovation. Fondation Rennes 1. Fondation Rennes 1 MANAGEMENT AGILE. Fondation Rennes 1 ET INNOVATION
Atelier de l innovation MANAGEMENT AGILE ET INNOVATION Chaire Economie de l innovation - Mourad Zeroukhi 2012-2014 Centre de Recherche en иconomie et Management UniversitИ de Rennes 1 Chaire Economie de
International Statistical Institute, 56th Session, 2007: Phil Everson
Teaching Regression using American Football Scores Everson, Phil Swarthmore College Department of Mathematics and Statistics 5 College Avenue Swarthmore, PA198, USA E-mail: [email protected] 1. Introduction
Statistical Models in R
Statistical Models in R Some Examples Steven Buechler Department of Mathematics 276B Hurley Hall; 1-6233 Fall, 2007 Outline Statistical Models Structure of models in R Model Assessment (Part IA) Anova
Website Report: http://www.servicesaprixfixes.com/ To-Do Tasks: 22 SEO SCORE: 73 / 100. Add the exact keywords to this URL.
Page 1 of 8 Website Report: http://www.servicesaprixfixes.com/ Keyword: Marketplace, B2B, services aux entreprises, small business Date: Decemr 12, 2014 SEO SCORE: 73 / 100 To-Do Tasks: 22 Add the exact
Using Stata for Categorical Data Analysis
Using Stata for Categorical Data Analysis NOTE: These problems make extensive use of Nick Cox s tab_chi, which is actually a collection of routines, and Adrian Mander s ipf command. From within Stata,
Troncatures dans les modèles linéaires simples et à effets mixtes sous R
Troncatures dans les modèles linéaires simples et à effets mixtes sous R Lyon, 27 et 28 juin 2013 D.Thiam, J.C Thalabard, G.Nuel Université Paris Descartes MAP5 UMR CNRS 8145 IRD UMR 216 2èmes Rencontres
A survey analysis example
A survey analysis example Thomas Lumley November 20, 2013 This document provides a simple example analysis of a survey data set, a subsample from the California Academic Performance Index, an annual set
Chapter 6: Multivariate Cointegration Analysis
Chapter 6: Multivariate Cointegration Analysis 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie VI. Multivariate Cointegration
An Introduction to Categorical Data Analysis Using R
An Introduction to Categorical Data Analysis Using R Brett Presnell March 28, 2000 Abstract This document attempts to reproduce the examples and some of the exercises in An Introduction to Categorical
Implementation of SAP-GRC with the Pictet Group
Implementation of SAP-GRC with the Pictet Group Olivier VERDAN, Risk Manager, Group Risk, Pictet & Cie 11 th December 2013 Zürich Table of contents 1 Overview of the Pictet Group 2 Operational Risk Management
Stockage distribué sous Linux
Félix Simon Ludovic Gauthier IUT Nancy-Charlemagne - LP ASRALL Mars 2009 1 / 18 Introduction Répartition sur plusieurs machines Accessibilité depuis plusieurs clients Vu comme un seul et énorme espace
Binary Diagnostic Tests Two Independent Samples
Chapter 537 Binary Diagnostic Tests Two Independent Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary
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
EFFECTS OF FORCE LEVEL AND TIME LAPSES AFTER TASK PERFORMANCE ON THE ACCURACY OF FORCE MATCHING IN A POWER-GRIP TRACKING TASK
EFFECTS OF FORCE LEVEL AND TIME LAPSES AFTER TASK PERFORMANCE ON THE ACCURACY OF FORCE MATCHING IN A POWER-GRIP TRACKING TASK LAING, ANDREW C.T. Ergonomics Initiative in Injury Prevention. Faculty of Applied
Calcul parallèle avec R
Calcul parallèle avec R ANF R Vincent Miele CNRS 07/10/2015 Pour chaque exercice, il est nécessaire d ouvrir une fenètre de visualisation des processes (Terminal + top sous Linux et Mac OS X, Gestionnaire
Correlation and Simple Linear Regression
Correlation and Simple Linear Regression We are often interested in studying the relationship among variables to determine whether they are associated with one another. When we think that changes in a
Lecture 18: Logistic Regression Continued
Lecture 18: Logistic Regression Continued Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South Carolina
1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
E(y i ) = x T i β. yield of the refined product as a percentage of crude specific gravity vapour pressure ASTM 10% point ASTM end point in degrees F
Random and Mixed Effects Models (Ch. 10) Random effects models are very useful when the observations are sampled in a highly structured way. The basic idea is that the error associated with any linear,
RAPPORT FINANCIER ANNUEL PORTANT SUR LES COMPTES 2014
RAPPORT FINANCIER ANNUEL PORTANT SUR LES COMPTES 2014 En application de la loi du Luxembourg du 11 janvier 2008 relative aux obligations de transparence sur les émetteurs de valeurs mobilières. CREDIT
Comparing Nested Models
Comparing Nested Models ST 430/514 Two models are nested if one model contains all the terms of the other, and at least one additional term. The larger model is the complete (or full) model, and the smaller
GSAC CONSIGNE DE NAVIGABILITE définie par la DIRECTION GENERALE DE L AVIATION CIVILE Les examens ou modifications décrits ci-dessous sont impératifs. La non application des exigences contenues dans cette
Liste d'adresses URL
Liste de sites Internet concernés dans l' étude Le 25/02/2014 Information à propos de contrefacon.fr Le site Internet https://www.contrefacon.fr/ permet de vérifier dans une base de donnée de plus d' 1
Nonlinear Regression Functions. SW Ch 8 1/54/
Nonlinear Regression Functions SW Ch 8 1/54/ The TestScore STR relation looks linear (maybe) SW Ch 8 2/54/ But the TestScore Income relation looks nonlinear... SW Ch 8 3/54/ Nonlinear Regression General
Some Essential Statistics The Lure of Statistics
Some Essential Statistics The Lure of Statistics Data Mining Techniques, by M.J.A. Berry and G.S Linoff, 2004 Statistics vs. Data Mining..lie, damn lie, and statistics mining data to support preconceived
2 RENSEIGNEMENTS CONCERNANT L ASSURÉ SI CELUI-CI N EST PAS LE REQUÉRANT INFORMATION CONCERNING THE INSURED PERSON IF OTHER THAN THE APPLICANT
SÉCURITÉ SOCIALE SOCIAL SECURITY ACCORD DU 9 FÉVRIER 1979 ENTRE LA FRANCE ET LE CANADA AGREEMENT OF FEBRUARY 9, 1979 BETWEEN FRANCE AND CANADA Formulaire FORM SE 401-06 INSTRUCTION D UNE DEMANDE DE PENSION
NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
Module 14: Missing Data Stata Practical
Module 14: Missing Data Stata Practical Jonathan Bartlett & James Carpenter London School of Hygiene & Tropical Medicine www.missingdata.org.uk Supported by ESRC grant RES 189-25-0103 and MRC grant G0900724
Spectrométrie d absorption moléculaire UV-visible
Spectrométrie d absorption moléculaire 1 faibles quantités échantillon liquide byfiles.storage.live.com/y1php6mrdyehuw8p4jw0... non destructrice bp0.blogger.com/.../m_7spgk78eu/s400/boum_4.png s'applique
EARLY VS. LATE ENROLLERS: DOES ENROLLMENT PROCRASTINATION AFFECT ACADEMIC SUCCESS? 2007-08
EARLY VS. LATE ENROLLERS: DOES ENROLLMENT PROCRASTINATION AFFECT ACADEMIC SUCCESS? 2007-08 PURPOSE Matthew Wetstein, Alyssa Nguyen & Brianna Hays The purpose of the present study was to identify specific
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
Insurance Fraud Estimation: More Evidence from the Quebec Automobile Insurance Industry. by Louis Caron and Georges Dionne
Insurance Fraud Estimation: More Evidence from the Quebec Automobile Insurance Industry by Louis Caron and Georges Dionne Abstract This article follows a previous study on insurance fraud in the Quebec
Lecture 25. December 19, 2007. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
Note concernant votre accord de souscription au service «Trusted Certificate Service» (TCS)
Note concernant votre accord de souscription au service «Trusted Certificate Service» (TCS) Veuillez vérifier les éléments suivants avant de nous soumettre votre accord : 1. Vous avez bien lu et paraphé
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
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
GLM with a Gamma-distributed Dependent Variable
GLM with a Gamma-distributed Dependent Variable Paul E. Johnson October 6, 204 Introduction I started out to write about why the Gamma distribution in a GLM is useful. In the end, I ve found it difficult
User Manual Culturethèque Thailand
User Manual Culturethèque Thailand Contents: 1. What can one do at Culturethèque?... 3 2. How to access Culturethèque?... 4 3. How to search on Culturethèque?... 5 4. How to access documents on Culturethèque?...
First-half 2012 Results. August 29 th, 2012. Jean-Paul AGON. Chairman and CEO
First-half 2012 Results August 29 th, 2012 Jean-Paul AGON Chairman and CEO First-half 2012 Results +11.4% +10.8% Operating profit 1,702.3 1,896.5 Net profit after non-controlling interests 1,625.2 1,466.6