Generalized Linear Models

Generalized Linear Models

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.

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Overview

Generalized linear models (GLMs) are used to model responses (dependent variables) that are derived in the form of counts, proportions, dichotomies (1/0), positive continuous values, and values that follow the normal Gaussian distribution. This course explains the theory of generalized linear models, outlines the algorithms used for GLM estimation, and explains how to determine which algorithm to use for a given data analysis.

Learning Outcomes

Upon completing this course students will have a general overview of how to derive GLM functions and apply continuous response models, discrete response models, and panel models.

  • Explain the derivation of the GLM
  • Explain the statistics used to assess model fit
  • Fit continuous response models like the Gaussian, log-normal and Gamma
  • Fit binomial models like logit and probit
  • Fit count models like Poisson and negative binomial
  • Deal with overdispersion in the data

Who Should Take This Course

Analysts in any field who need to move beyond standard multiple linear regression models for modeling their data.

If you understand GLMs, you understand linear regression, logistic regression, Poisson regression, negative binomial regression, gamma regression, multinomial regression and so many other models that are either directly included in GLMs or are simple extensions. Random effects models and generalized estimating equation (GEE) models are built on top of GLMs, so understanding GLMs is a great introduction to these advanced subjects

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

General Overview of GLM

  • Derivation of GLM functions
  • GLM algorithms: OIM, EIM
  • Fit and residual statistics

Week 2

Continuous Response Models

  • Gaussian
  • Log-normal
  • Gamma
  • Log-gamma models for survival analysis
  • Inverse Gaussian

Week 3

Discrete Response Models

  • Binomial models: logit, probit, cloglog, loglog, others
  • Count models: Poisson, negative binomial, geometric

Week 4

Problems with Overdispersion

  • Overview of ordered and unordered logit and probit regression
  • Overview of panel models

Class Dates

2019

No classes scheduled at this time.

2020

Apr 10, 2020 to May 8, 2020

2021

No classes scheduled at this time.

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Prerequisites

The first week of the course presents theory to support the applications covered in weeks 2-4. If you are interested only in the applications, you can skim over the material in week 1. If you wish to follow along with week 1’s development of theory, the following additional prerequisites apply:

    • Calculus
    • Matrix Algebra

The courses listed below are prerequisites for enrollment in this course:

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.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jan 10, 2020, Jul 17, 2020

Matrix Algebra

This course will teach you the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 27, 2020, Aug 14, 2020

Regression Analysis

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 17, 2020, May 8, 2020, Oct 2, 2020

What Our Students Say​

I took this class out of general curiosity and the fact that it used Python, which is increasingly attractive to me in building my own software tools.  To my delighted surprise I found the course to be one of the very best (out of more than 20) I have taken at Statistics.com.  It proved a harmonious blend of theory (buttressed by relevant reading and videos by the instructor), focused homework exercises, and training in Python coding.  As a result I learned much and enjoyed the process greatly.  I was particularly impressed with the instructor's choice of homework assignments, which fascinated me, and with the importance he placed in students writing clear, even elegant, Python code.  This is my fifth course using Python at Statistics.com and while I found the other courses quite useful to varying degrees, I never before had an instructor who spent time advising me on the fine details of my actual coding style and suggesting useful improvements. 

Milan Hejtmanek
Seoul National University

It helped me immediately with my job. I was able to understand and apply the concepts to a time-series analysis. The R package I was using (vars) required that I read the documentation, which used much of the terminology and concepts in this class. I would have been quite lost without that introduction to the material. This class really stretched my brain! I loved it!

Lesley Painchaud
Scientist at US Navy

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®)

Please see our knowledge center for more information.

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FAQs + Knowledge Base

Related Courses

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.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jan 10, 2020, Jul 17, 2020

Mixed and Hierarchical Linear Models

This course will teach you the basic theory of linear and non-linear mixed effects models, hierarchical linear models, algorithms used for estimation, primarily for models involving normally distributed errors, and examples of data analysis.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Apr 24, 2020

Regression Analysis

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 17, 2020, May 8, 2020, Oct 2, 2020

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 supplemental readings available online, example software codes, and an end of course data modeling project.

Course Text

Generalized Linear Models and Extensions, third edition by James Hardin and Joseph Hilbe. When you order your copy, be sure to put ‘GLM course at Statistics.com’ in the Company/University field of the order form.

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

 

Software

In some lessons, you will benefit from being able to implement models in a software program that is able to do GLM (for example, Stata, SAS, S-PLUS, R). Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.

Stata: The instructor is familiar with Stata. If you are undecided about which software to use, Stata, which is relatively easy to learn and use, is a safe choice.

R: If you want to use R with this course, you should have some prior experience and facility with it (tutorial help from the instructor or TA will be available but limited.) If you wish to use R, but no have current expertise in it, you should consider taking one of our introductory R courses before taking this one.

SAS: The TA can offer limited assistance with SAS in this course. If you want to use SAS with this course, you should have some prior experience and facility with it. If you wish to use SAS, but no have current expertise in it, you should consider taking an introductory course or courses from SAS Institute or elsewhere.

SPSS: The instructor and TA are not familiar with SPSS. If you want to use SPSS with this course, you should have some prior experience and facility with it. If you wish to use SPSS, but no have current expertise in it, you should consider taking an introductory course or courses from SPSS.

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.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

The Institute gratefully acknowledges the contributions of Prof. Joseph Hilbe to the development of this course.

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Generalized Linear Models
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