Logistic Regression

Logistic Regressiontaught by James Hardin

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
• 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:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
4. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses

INFORMS CAP:
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP® analysts accrue Professional Development Units to maintain their certification .
Course Text:

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

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).

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:

September 01, 2017 to September 29, 2017 March 09, 2018 to April 06, 2018 July 20, 2018 to August 17, 2018 March 08, 2019 to April 05, 2019 July 19, 2019 to August 16, 2019 March 06, 2020 to April 03, 2020 July 17, 2020 to August 14, 2020

Logistic Regression

Instructor(s):

Dates:
September 01, 2017 to September 29, 2017 March 09, 2018 to April 06, 2018 July 20, 2018 to August 17, 2018 March 08, 2019 to April 05, 2019 July 19, 2019 to August 16, 2019 March 06, 2020 to April 03, 2020 July 17, 2020 to August 14, 2020

Course Fee: \$589

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

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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