Logistic Regression and Odds Ratios

Slides:



Advertisements
Similar presentations
Three or more categorical variables
Advertisements

Regression analysis Linear regression Logistic regression.
Logistic Regression.
Simple Logistic Regression
Chapter 11 Contingency Table Analysis. Nonparametric Systems Another method of examining the relationship between independent (X) and dependant (Y) variables.
SLIDE 1IS 240 – Spring 2010 Logistic Regression The logistic function: The logistic function is useful because it can take as an input any.
PSYC512: Research Methods PSYC512: Research Methods Lecture 19 Brian P. Dyre University of Idaho.
Chi-square Test of Independence
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.
Basic Statistical Concepts Donald E. Mercante, Ph.D. Biostatistics School of Public Health L S U - H S C.
SW388R7 Data Analysis & Computers II Slide 1 Multiple Regression – Basic Relationships Purpose of multiple regression Different types of multiple regression.
Correlation Question 1 This question asks you to use the Pearson correlation coefficient to measure the association between [educ4] and [empstat]. However,
Example of Simple and Multiple Regression
Categorical Data Prof. Andy Field.
Hierarchical Binary Logistic Regression
9/23/2015Slide 1 Published reports of research usually contain a section which describes key characteristics of the sample included in the study. The “key”
Chi-Square Test of Independence Practice Problem – 1
Logistic Regression.
Chapter 16 The Elaboration Model Key Terms. Descriptive statistics Statistical computations describing either the characteristics of a sample or the relationship.
Repeated Measures  The term repeated measures refers to data sets with multiple measurements of a response variable on the same experimental unit or subject.
7.4 – Sampling Distribution Statistic: a numerical descriptive measure of a sample Parameter: a numerical descriptive measure of a population.
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.
Logistic (regression) single and multiple. Overview  Defined: A model for predicting one variable from other variable(s).  Variables:IV(s) is continuous/categorical,
Week 5: Logistic regression analysis Overview Questions from last week What is logistic regression analysis? The mathematical model Interpreting the β.
Logistic Regression July 28, 2008 Ivan Katchanovski, Ph.D. POL 242Y-Y.
SW388R6 Data Analysis and Computers I Slide 1 Multiple Regression Key Points about Multiple Regression Sample Homework Problem Solving the Problem with.
Assessing Binary Outcomes: Logistic Regression Peter T. Donnan Professor of Epidemiology and Biostatistics Statistics for Health Research.
SW318 Social Work Statistics Slide 1 Logistic Regression and Odds Ratios Example of Odds Ratio Using Relationship between Death Penalty and Race.
11/23/2015Slide 1 Using a combination of tables and plots from SPSS plus spreadsheets from Excel, we will show the linkage between correlation and linear.
Political Science 30: Political Inquiry. Linear Regression II: Making Sense of Regression Results Interpreting SPSS regression output Coefficients for.
Slide 1 The Kleinbaum Sample Problem This problem comes from an example in the text: David G. Kleinbaum. Logistic Regression: A Self-Learning Text. New.
Logistic regression. Recall the simple linear regression model: y =  0 +  1 x +  where we are trying to predict a continuous dependent variable y from.
Practice Problem: Lambda (1)
1 Follow the three R’s: Respect for self, Respect for others and Responsibility for all your actions.
12/23/2015Slide 1 The chi-square test of independence is one of the most frequently used hypothesis tests in the social sciences because it can be used.
Logistic Regression Analysis Gerrit Rooks
Probability and odds Suppose we a frequency distribution for the variable “TB status” The probability of an individual having TB is frequencyRelative.
Death Row Inmates 2005 BLACK 41.7% HISPANIC 10.4% WHITE 45.5% OTHER 2.3%
Logistic Regression An Introduction. Uses Designed for survival analysis- binary response For predicting a chance, probability, proportion or percentage.
Nonparametric Statistics
Probability in Sampling. Key Concepts l Statistical terms in sampling l Sampling error l The sampling distribution.
LOGISTIC REGRESSION. Purpose  Logistical regression is regularly used when there are only two categories of the dependent variable and there is a mixture.
Other tests of significance. Independent variables: continuous Dependent variable: continuous Correlation: Relationship between variables Regression:
Cross Tabulation with Chi Square
Nonparametric Statistics
BINARY LOGISTIC REGRESSION
An introduction to Survival analysis and Applications to Predicting Recidivism Rebecca S. Frazier, PhD JBS International.
Logistic Regression APKC – STATS AFAC (2016).
Advanced Quantitative Techniques
Dr. Siti Nor Binti Yaacob
Chi-Square X2.
Political Science 30: Political Inquiry
Introduction to Inferential Statistics
Hypothesis Testing Review
Categorical Data Aims Loglinear models Categorical data
Multiple Regression.
Difference Between Means Test (“t” statistic)
Multiple logistic regression
Nonparametric Statistics
females males Analyses with discrete variables
Tutorial 5 Use the Complete University Admission Data ( Dataset 17) to compare the admission rates for men and women within each department (e.g. By.
Tutorial 4 For the Seat Belt Data, the Death Penalty Data, and the University Admission Data, (1). Identify the response variable and the explanatory.
Applied Statistics Using SPSS
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.
Applied Statistics Using SPSS
Logistic Regression.
Presentation transcript:

Logistic Regression and Odds Ratios Example of Odds Ratio Using Relationship between Death Penalty and Race

Probability and Odds We begin with a frequency distribution for the variable “Death Penalty for Crime” The probability of receiving a death sentence is 0.34 or 34% (50/147) The odds of receiving a death sentence = death sentence/not death sentence = 50/97 = 0.5155

Interpreting Odds The odds of 0.5155 can be stated in different ways: Defendants can expect to receive a death sentence instead of life imprisonment in about half of their trials Receiving a death sentence is half as likely as receiving a sentence of life imprisonment Or, inverting the odds, Receiving a life imprisonment sentence is twice as likely as receiving the death penalty.

Impact of an Independent Variable If an independent variable impacts or has a relationship to a dependent variable, it will change the odds of being in the key dependent variable group, e.g. death sentence. The following table shows the relationship between race and sentence:

Odds for Independent Variable Groups We can compute the odds of receiving a death penalty for each of the groups: The odds of receiving a death sentence if the defendant was Black = 28/45 = 0.6222 The odds of receiving a death sentence if the defendant was not Black = 22/52 = 0.4231

The Odds Ratio Measures the Effect The impact of being black on receiving a death penalty is measured by the odds ratio which equals: = the odds if black ÷ the odds if not black = 0.6222 ÷ 0.4231 = 1.47 Which we interpret as: Blacks are 1.47 times more likely to receive a death sentence as non blacks The risk of receiving a death sentence are 1.47 times greater for blacks than non blacks The odds of a death sentence for blacks are 47% higher than the odds of a death sentence for non blacks. (1.47 - 1.00) The predicted odds for black defendants are 1.47 times the odds for non black defendants. A one unit change in the independent variable race (nonblack to black) increases the odds of receiving a death penalty by a factor of 1.47.

SPSS Output for this Relationship The Exp(B) output using SPSS is the change in the odds ratio. The odds ratio is output in SPSS in the column labeled Exp(B).