Logistic Regression

Logistic Regression

taught by James Hardin

 
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Aim of Course:

Logistic regression is one of the most commonly-used statistical techniques. It is used with data in which there is a binary (success-failure) outcome (response) variable, or where the outcome takes the form of a binomial proportion. Like linear regression, one estimates the relationship between predictor variables and an outcome variable. In logistic regression, however, one estimates the probability that the outcome variable assumes a certain value, rather than estimating the value itself. This online course will cover the functional form of the logistic model and how to interpret model coefficients. The concepts of "odds" and "odds ratio" are examined, as well as how to predict probabilities of events and how to assess model fit. We shall also examine the basics of Bayesian logistic regression, which is becoming more popular in research. R, Stata, and SAS code is provided for all examples used during the course. 

This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1: Basic Terminology and Concepts

  • What is a statistical model?
  • Knowledge of the basics of logistic regression modeling
  • Understanding the Bernoulli probability distribution
  • Methods of estimation
  • Models with a binary, categorical or continuous predictor
  • Understanding predictions, probabilities, and odds ratios

WEEK 2: Logistic Model Construction

  • Prediction
  • Selection and interpretation of model predictors
  • Statistics in a logistic model
  • Information criterion tests
  • Adjusting model standard errors
  • Risk factors, confounders, effect modifiers, interactions
  • Checking logistic model fit
  • Models with unbalanced data and perfect prediction
  • Exact logistic regression

WEEK 3: Modeling Table and Grouped Data

  • Modeling table data
  • Binomial PDF
  • From observation to grouped data
  • Identifying and adjusting for extra-dispersion
  • Modeling and interpretation of grouped logistic regression
  • Beta-binomial regression

WEEK 4: Bayesian Logistic Regression

  • Overview and basic concepts of Bayesian methodology
  • Examples of Bayesian logistic regression using R
  • Examples of Bayesian logistic regression using JAGS
  • Examples of Bayesian logistic regression using Stata

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, guided data modeling problems using software, and end of course data modeling project.

In addition to assigned readings, this course also has example software codes, supplemental readings available online, and an end of course data modeling project.

Note:  The Institute gratefully acknowledges the contribution of Prof. Joseph Hilbe, the original developer and instructor for the course.

Logistic Regression

Who Should Take This Course:
Medical researchers, epidemiologists, forensic statisticians, environmental scientists, actuaries, data miners, industrial statisticans, sports statisticians, and fisheries, to name a few, will all find this course useful. It is an essential course for anyone who needs to model data with binary or categorical outcomes, and who need to estimate probabilities of given outcomes based on predictor variables.
Level:
Intermediate
Prerequisite:
Some familiarity with linear modeling - such as that provided in Regression Analysis will be helpful.
Organization of the Course:
Options for Credit and Recognition:
Course Text:

The course text is A Practical Guide to Logistic Regression by Joseph Hilbe, which you can order online here.

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

Course participants may use any software that is capable of doing logistic regression. 

R: The instructor is familiar with R.  Illustrations and solutions to assignments will be provided in R in this course.  If you want to use R with this course, you should have some prior experience and facility with it (check out our introductory R courses).  You'll need the latest versions of R and RStudio.

Stata: The instructor is familiar with Stata. Homework solutions are provided in Stata, and code is provided at the end of the chapters in the text duplicating nearly all R illustrations in the book. 

SAS: The instructor and TA can offer limited assistance with SAS in this course. 

 

Instructor(s):

Dates:

July 19, 2019 to August 16, 2019 January 10, 2020 to February 07, 2020 July 17, 2020 to August 14, 2020

Logistic Regression

Instructor(s):

Dates:
July 19, 2019 to August 16, 2019 January 10, 2020 to February 07, 2020 July 17, 2020 to August 14, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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