Logistic Regression

Logistic Regression

This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.

This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.

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Overview

Logistic regression is a commonly used statistical technique to understand data with binary outcomes (success-failure), or where outcomes take the form of a binomial proportion. The objective of logistic regression is to estimate the probability that an outcome will assume a certain value. This course covers the functional form of the logistics model and how to interpret model coefficients. R, Stata, and SAS code is provided for all examples used during the course.

Learning Outcomes

Students who complete this course will understand the functional form of the logistic model and how to interpret model coefficients. You will learn basic terminology and concepts, how to construct and adjust a logistic model, how to model table and grouped data, and basic concepts of Bayesian logistic regression. You will also examine the concept of “odds” and “odds ratio”, as well as how to predict probabilities of events and how to assess model fit.

  • Specify when a logistic regression model is used, and its form
  • How to fit a logistic model
  • Using information criteria to assess model performance
  • Dealing with risk factors, confounders, effect modifiers, interactions
  • How to model table data
  • Dealing with excessive disperson

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.

Instructors

dr-james-hardin

Dr. James Hardin

Dr. James Hardin is an Associate Dean of Faculty Affairs and CurriculumProfessor at the University of South Carolina. Co-author (with Joseph Hilbe) of Generalized Estimating Equations, Dr. Hardin is on the editorial board of The Stata Journal and is the developer of the Stata GEE command, and with Dr. Hilbe is the developer of the GLM command.
See Instructor Bio

Course Syllabus

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

Class Dates

2020

Jan 10, 2020 to Feb 7, 2020

Jul 17, 2020 to Aug 14, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

Some familiarity with linear modeling – such as that provided in Regression Analysis will be helpful.

What Our Students Say​

Great instructor, TA and peer students. I've really enjoyed this course. Thank you!

Hao Song
The National Board of Certification and Recertification for Nurse Anesthetists

I found it challenging but also a rewarding experience. After completing this course I am now more confident about undertaking statistical analysis.

Michael Jonita
Hunter Research Foundation

Frequently Asked Questions

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We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

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Additional Course Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

Course Text

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

Please order a copy of your course textbook prior to course start 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.  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. 

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is 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.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

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

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Logistic Regression
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