Deaths of snails vs exposure by species. Deaths of snails vs exposure by temperature.

Slides:

Advertisements

Similar presentations

A. The Basic Principle We consider the multivariate extension of multiple linear regression – modeling the relationship between m responses Y 1,…,Y m and.

Advertisements

I OWA S TATE U NIVERSITY Department of Animal Science PROC ROBUSTRET & Evaluating Regression analyses With The Help of PROC RSQUARE Animal Science 500.

Data: Crab mating patterns Data: Typists (Poisson with random effects) (Poisson Regression, ZIP model, Negative Binomial) Data: Challenger (Binomial with.

A Model to Evaluate Recreational Management Measures Objective I – Stock Assessment Analysis Create a model to distribute estimated landings (A + B1 fish)

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

EPI 809/Spring Probability Distribution of Random Error.

Regression with Autocorrelated Errors U.S. Wine Consumption and Adult Population –

Loglinear Models for Independence and Interaction in Three-way Tables Veronica Estrada Robert Lagier.

Overview of Logistics Regression and its SAS implementation

Generalized Linear Mixed Model English Premier League Soccer – 2003/2004 Season.

Datamining and statistical learning - lecture 9 Generalized linear models (GAMs)  Some examples of linear models  Proc GAM in SAS  Model selection in.

April 25 Exam April 27 (bring calculator with exp) Cox-Regression

Some Terms Y =  o +  1 X Regression of Y on X Regress Y on X X called independent variable or predictor variable or covariate or factor Which factors.

Multiple regression analysis

Chapter 11: Inferential methods in Regression and Correlation

Header= Verdana 28 pt., Red 1 STA 517 – Chapter 3: Inference for Contingency Tables 3. Inference for Contingency Tables 3.1 Confidence Intervals for Association.

1 Experimental design and analyses of experimental data Lesson 6 Logistic regression Generalized Linear Models (GENMOD)

Adjusting for extraneous factors Topics for today Stratified analysis of 2x2 tables Regression Readings Jewell Chapter 9.

Linear statistical models 2009 Models for continuous, binary and binomial responses  Simple linear models regarded as special cases of GLMs  Simple linear.

1 More on exposure/response associations Readings Jewell Chapters 3 & 7.

Chapter 11 Survival Analysis Part 3. 2 Considering Interactions Adapted from "Anderson" leukemia data as presented in Survival Analysis: A Self-Learning.

PH6415 Review Questions. 2 Question 1 A journal article reports a 95%CI for the relative risk (RR) of an event (treatment versus control as (0.55, 0.97).

Chapter 11 Survival Analysis Part 2. 2 Survival Analysis and Regression Combine lots of information Combine lots of information Look at several variables.

EPI 809/Spring Multiple Logistic Regression.

1 Modeling Ordinal Associations Section 9.4 Roanna Gee.

Logistic Regression Biostatistics 510 March 15, 2007 Vanessa Perez.

Mental Health Study Example Alachua County, Florida Purpose: Relate mental impairment to two explanatory variables, the severity of life and socioeconomic.

Data mining and statistical learning, lecture 3 Outline  Ordinary least squares regression  Ridge regression.

OLS versus MLE Example YX Here is the data:

WLS for Categorical Data

Adjusting for extraneous factors Topics for today More on logistic regression analysis for binary data and how it relates to the Wolf and Mantel- Haenszel.

This Week Continue with linear regression Begin multiple regression –Le 8.2 –C & S 9:A-E Handout: Class examples and assignment 3.

Linear statistical models 2009 Count data  Contingency tables and log-linear models  Poisson regression.

Logistic Regression II Simple 2x2 Table (courtesy Hosmer and Lemeshow) Exposure=1Exposure=0 Disease = 1 Disease = 0.

Logistic Regression III: Advanced topics Conditional Logistic Regression for Matched Data Conditional Logistic Regression for Matched Data.

G Lecture 121 Analysis of Time to Event Survival Analysis Language Example of time to high anxiety Discrete survival analysis through logistic regression.

Biostatistics Case Studies 2005 Peter D. Christenson Biostatistician Session 4: Taking Risks and Playing the Odds: OR vs.

April 11 Logistic Regression –Modeling interactions –Analysis of case-control studies –Data presentation.

Analyses of Covariance Comparing k means adjusting for 1 or more other variables (covariates) Ho: u 1 = u 2 = u 3 (Adjusting for X) Combines ANOVA and.

April 6 Logistic Regression –Estimating probability based on logistic model –Testing differences among multiple groups –Assumptions for model.

Xuhua Xia Polynomial Regression A biologist is interested in the relationship between feeding time and body weight in the males of a mammalian species.

2 December 2004PubH8420: Parametric Regression Models Slide 1 Applications - SAS Parametric Regression in SAS –PROC LIFEREG –PROC GENMOD –PROC LOGISTIC.

Applied Epidemiologic Analysis - P8400 Fall 2002

1 היחידה לייעוץ סטטיסטי אוניברסיטת חיפה פרופ’ בנימין רייזר פרופ’ דוד פרג’י גב’ אפרת ישכיל.

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

6-1 Introduction To Empirical Models Based on the scatter diagram, it is probably reasonable to assume that the mean of the random variable Y is.

GEE Approach Presented by Jianghu Dong Instructor: Professor Keumhee Chough (K.C.) Carrière.

1 Topic 2 LOGIT analysis of contingency tables. 2 Contingency table a cross classification Table containing two or more variables of classification, and.

March 28, 30 Return exam Analyses of covariance 2-way ANOVA Analyses of binary outcomes.

1 STA 617 – Chp10 Models for matched pairs Summary  Describing categorical random variable – chapter 1  Poisson for count data  Binomial for binary.

Log-linear Models HRP /03/04 Log-Linear Models for Multi-way Contingency Tables 1. GLM for Poisson-distributed data with log-link (see Agresti.

1 Topic 4 : Ordered Logit Analysis. 2 Often we deal with data where the responses are ordered – e.g. : (i) Eyesight tests – bad; average; good (ii) Voting.

Sigmoidal Response (knnl558.sas). Programming Example: knnl565.sas Y = completion of a programming task (1 = yes, 0 = no) X 2 = amount of programming.

Lecture 3: Parametric Survival Modeling

Today: March 7 Data Transformations Rank Tests for Non-Normal data Solutions for Assignment 4.

Applied Epidemiologic Analysis - P8400 Fall 2002 Labs 6 & 7 Case-Control Analysis ----Logistic Regression Henian Chen, M.D., Ph.D.

1 Say good things, think good thoughts, and do good deeds.

1 Modeling change Kristin Sainani Ph.D. Stanford University Department of Health Research and Policy

Dependent Variable Discrete  2 values – binomial  3 or more discrete values – multinomial  Skewed – e.g. Poisson Continuous  Non-normal.

Applied Epidemiologic Analysis - P8400 Fall 2002 Labs 6 & 7 Case-Control Analysis ----Logistic Regression Henian Chen, M.D., Ph.D.

04/19/2006Econ 6161 Econ 616 – Spring 2006 Qualitative Response Regression Models Presented by Yan Hu.

1 Statistics 262: Intermediate Biostatistics Mixed models; Modeling change.

1 Experimental Statistics - week 12 Chapter 11: Linear Regression and Correlation Chapter 12: Multiple Regression.

Analysis of matched data Analysis of matched data.

1 Linear Regression Model. 2 Types of Regression Models.

ביצוע רגרסיה לוגיסטית. פרק ה-2

Survival Analysis {Chapter 12}

Modeling Ordinal Associations Bin Hu

Presentation transcript:

Deaths of snails vs exposure by species

Deaths of snails vs exposure by temperature

Deaths of snails vs exposure by relative humidity

SAS code for deaths of snails vs species, exposure temperature and relative humidity proc genmod data=linear.lab13_1; class species exposure temp rel_humidity; model deaths/total=species exposure temp rel_humidity/dist=binomial; run;  When each observation in the input data set contains the number of events (for example, successes) and the number of trials from a set of binomial trials, use the events/trials syntax.  This form is applicable only to summarized binomial response data.

SAS code for deaths of snails vs species, exposure temperature and relative humidity Analysis Of Parameter Estimates Standard Wald 95% Confidence Chi- Parameter DF Estimate Error Limits Square Pr > ChiSq Intercept Exposure Exposure <.0001 Exposure <.0001 Exposure Species A <.0001 Species B Temp <.0001 Temp Temp Rel_humidity <.0001 Rel_humidity <.0001 Rel_humidity Rel_humidity Scale

Interpretation of SAS output for the study of deaths among snails in relation to species, exposure, temperature and relative humidity.  The model parameters represent estimates of  The presence of interaction effects can be tested by computing

Survival probability vs age, sex and passenger class PClass_Sex_Rel. frequencyN1 1stfemale stmale ndfemale ndmale rdfemale rdmale Missing values?

Relative frequency of myocardial infarction vs coffee and cigarrette consumption

Main effects? Interaction effects?

SAS code for the study of myocardial infarction vs coffee and cigarrette consumption proc genmod data=linear.lab14_1; class coffee cigarrettes; model cases/total = coffee cigarrettes/dist=binomial; run;  When each observation in the input data set contains the number of events (for example, successes) and the number of trials from a set of binomial trials, use the events/trials syntax.  This form is applicable only to summarized binomial response data.

SAS code for the study of myocardial infarction vs coffee and cigarrette consumption Quantitative inputs (scores) Qualitative inputs.

Rail accidents Observed data may be regarded as rates (number of accidents per mile)