Week 5: Logistic regression analysis Overview Questions from last week What is logistic regression analysis? The mathematical model Interpreting the β.

Slides:

Advertisements

Similar presentations

Continued Psy 524 Ainsworth

Advertisements

Sociology 680 Multivariate Analysis Logistic Regression.

Logistic Regression Psy 524 Ainsworth.

Regression analysis Linear regression Logistic regression.

Logistic Regression.

Learning Objectives Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.

Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Bivariate Correlation and Regression CHAPTER Thirteen.

Learning Objectives 1 Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.

Chapter 10 Regression. Defining Regression Simple linear regression features one independent variable and one dependent variable, as in correlation the.

Regression With Categorical Variables. Overview Regression with Categorical Predictors Logistic Regression.

Logistic Regression Multivariate Analysis. What is a log and an exponent? Log is the power to which a base of 10 must be raised to produce a given number.

Introduction to Logistic Regression. Simple linear regression Table 1 Age and systolic blood pressure (SBP) among 33 adult women.

Log-linear and logistic models Generalised linear model ANOVA revisited Log-linear model: Poisson distribution logistic model: Binomial distribution Deviances.

Log-linear and logistic models

Handling Categorical Data. Learning Outcomes At the end of this session and with additional reading you will be able to: – Understand when and how to.

(Correlation and) (Multiple) Regression Friday 5 th March (and Logistic Regression too!)

Notes on Logistic Regression STAT 4330/8330. Introduction Previously, you learned about odds ratios (OR’s). We now transition and begin discussion of.

An Introduction to Logistic Regression

C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Linear Regression and Linear Prediction Predicting the score on one variable.

Multiple Regression Research Methods and Statistics.

Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.

CHAPTER 5 REGRESSION Discovering Statistics Using SPSS.

Chapter 8: Bivariate Regression and Correlation

CHAPTER 8 Basic Data Analysis for Quantitative Research ESSENTIALS OF MARKETING RESEARCH Hair/Wolfinbarger/Ortinau/Bush.

1 G Lect 11W Logistic Regression Review Maximum Likelihood Estimates Probit Regression and Example Model Fit G Multiple Regression Week 11.

Learning Objective Chapter 14 Correlation and Regression Analysis CHAPTER fourteen Correlation and Regression Analysis Copyright © 2000 by John Wiley &

Week 4: Multiple regression analysis Overview Questions from last week What is regression analysis? The mathematical model Interpreting the β coefficient.

ALISON BOWLING THE GENERAL LINEAR MODEL. ALTERNATIVE EXPRESSION OF THE MODEL.

Excepted from HSRP 734: Advanced Statistical Methods June 5, 2008.

Week 6: Model selection Overview Questions from last week Model selection in multivariable analysis -bivariate significance -interaction and confounding.

Correlation is a statistical technique that describes the degree of relationship between two variables when you have bivariate data. A bivariate distribution.

Logistic Regression STA2101/442 F 2014 See last slide for copyright information.

Multilevel Linear Models Field, Chapter 19. Why use multilevel models? Meeting the assumptions of the linear model – Homogeneity of regression coefficients.

Logistic Regression Database Marketing Instructor: N. Kumar.

University of Warwick, Department of Sociology, 2014/15 SO 201: SSAASS (Surveys and Statistics) (Richard Lampard) Week 7 Logistic Regression I.

AN INTRODUCTION TO LOGISTIC REGRESSION ENI SUMARMININGSIH, SSI, MM PROGRAM STUDI STATISTIKA JURUSAN MATEMATIKA UNIVERSITAS BRAWIJAYA.

Logistic (regression) single and multiple. Overview  Defined: A model for predicting one variable from other variable(s).  Variables:IV(s) is continuous/categorical,

Linear correlation and linear regression + summary of tests

Linear vs. Logistic Regression Log has a slightly better ability to represent the data Dichotomous Prefer Don’t Prefer Linear vs. Logistic Regression.

APPLIED DATA ANALYSIS IN CRIMINAL JUSTICE CJ 525 MONMOUTH UNIVERSITY Juan P. Rodriguez.

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

Chapter Thirteen Copyright © 2006 John Wiley & Sons, Inc. Bivariate Correlation and Regression.

Multiple regression.

Multiple Logistic Regression STAT E-150 Statistical Methods.

Inferential Statistics. Explore relationships between variables Test hypotheses –Research hypothesis: a statement of the relationship between variables.

Logistic Regression. Linear regression – numerical response Logistic regression – binary categorical response eg. has the disease, or unaffected by the.

Regression Analysis. 1. To comprehend the nature of correlation analysis. 2. To understand bivariate regression analysis. 3. To become aware of the coefficient.

LOGISTIC REGRESSION Binary dependent variable (pass-fail) Odds ratio: p/(1-p) eg. 1/9 means 1 time in 10 pass, 9 times fail Log-odds ratio: y = ln[p/(1-p)]

Logistic Regression Analysis Gerrit Rooks

Qualitative and Limited Dependent Variable Models ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes.

Dates Presentations Wed / Fri Ex. 4, logistic regression, Monday Dec 7 th Final Tues. Dec 8 th, 3:30.

Logistic Regression Saed Sayad 1www.ismartsoft.com.

Roger B. Hammer Assistant Professor Department of Sociology Oregon State University Conducting Social Research Logistic Regression Categorical Data Analysis.

Nonparametric Statistics

Week 7: General linear models Overview Questions from last week What are general linear models? Discussion of the 3 articles.

LOGISTIC REGRESSION. Purpose  Logistical regression is regularly used when there are only two categories of the dependent variable and there is a mixture.

Logistic Regression: Regression with a Binary Dependent Variable.

Nonparametric Statistics

Logistic Regression When and why do we use logistic regression?

Logistic Regression APKC – STATS AFAC (2016).

REGRESSION G&W p

Multiple Regression.

Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.

S519: Evaluation of Information Systems

Nonparametric Statistics

BIVARIATE ANALYSIS: Measures of Association Between Two Variables

BIVARIATE ANALYSIS: Measures of Association Between Two Variables

What’s the plan? First, we are going to look at the correlation between two variables: studying for calculus and the final percentage grade a student gets.

Chapter 6 Logistic Regression: Regression with a Binary Dependent Variable Copyright © 2010 Pearson Education, Inc., publishing as Prentice-Hall.

Presentation transcript:

Week 5: Logistic regression analysis Overview Questions from last week What is logistic regression analysis? The mathematical model Interpreting the β coefficient and odds ratios Maximum likelihood model fitting An example using SPSS (with our Ottawa hospital data) Discussion of the 2 articles Data analysis discussion

What is logistic regression analysis? Used to learn more about the relationship between several exposure variables and a dichotomous outcome variable Logistic regression is an extension of the 2X2 table with several exposure variables instead of just one No assumptions about the distribution of the variables

The mathematical model In linear regression Y’ (known as Y prime) is the predicted value on the outcome variable A is the Y axis intercept β 1 is the coefficient assigned through regression X 1 is the unit of the exposure variable But for logistic regression the model is: ln ( Y’ ) =A + β 1 X 1 + β 2 X 2 + β 3 X 3 1-Y’

Interpreting the coefficient The regression equation in logistic regression tells us the (natural log of the) probability of being in one group divided by the probability of being in the other group The exponentiated coefficient gives us the odds ratio The significance of each coefficient is tested by dividing the coefficient by its standard error

Maximum likelihood model fitting Most logistic regression models use the maximum likelihood model to fit regression models The log-likelihood is calculated based on predicted and actual outcomes A goodness-of-fit chi-square is calculated (usually compares a constant-only model to the one you created) Tries to find the best fit for the variables included

A logistic regression example Background Remember our Ottawa CHIRPP data We want to compare the odds of going to a children’s hospital with going elsewhere The outcome variable is pediatric hospital compared to other hospital The exposure variables are: Age group, disposition, others?

Statistical analysis Univariate statistics (histograms for continuous variables, frequency distributions for categorical variables) Bivariate statistics (t-tests and chi square statistics) Logistic regression analysis Let’s try it in SPSS

For next week Read articles Read text Chapter 9 Start modelling your own data using the appropriate multivariable technique