Logistic Regression

Brief Description:

Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.

Instructor(s):
Level: Intermediate

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.

Dates:
March 16, 2012 to April 13, 2012September 07, 2012 to October 05, 2012
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

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Logistic Regression

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 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 "risk ratio" and the difference between the two statistics. Our emphasis is on model construction, interpretation, and goodness of fit. Exercises include hands-on computer problems.

Prerequisite(s):

• Calculus:  Though it is not required for practical applications of material in this course, some familiarity with calculus (see statistics.com's 3 week Calculus Review course) is helpful for a complete understanding of model development.
• Standard Multiple Regression:  A solid understanding of standard multiple linear regression, which is coverd in Regression Analysis.
• Software:  Familiarity with software that can do logistic regression (see below).

Course Program:

SESSION 1: Basic Terminology and Concepts

• Software for modeling logistic regression: Stata, R, SAS, SPSS, other
• History of the logistic model
• Concepts related to logistic regression
• 2x2, 2xn models of odds and risk ratios
• Fitting algorithms

SESSION 2: Logistic Model Construction

• Derivation of the binary logistic model
• Model-building strategies
• Link tests, partial residual plots
• Standard errors: scaling, bootstrap, jackknife, robust
• Interpreting odds ratios as risk ratios - criteria
• Stepwise methods, missing values, constrained coefficients, etc Construction and interpretation of interactions

SESSION 3: Analysis, Fit, and Interpretation of the Logistic Model

• Goodness of fit tests
• Information criterion tests
• Residual analysis
• Validation models

SESSION 4: Binomial Logistic Regression and Overdispersion

• The meaning and types of overdispersion
• Simulations: detecting apparent vs real overdispersion
• Methods of handling real overdispersion

Organization of the Course:

This course takes place over the internet, at statistics.com 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.

The course typically requires 15 hours per week. 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 you will receive individual feedback on your homework answers.

Credit:
Students come to The Institute for a variety of reasons:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Program in Advanced Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).

As you begin the class, you will be asked to specify your category.

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a record of course completion will be issued by Statistics.com, upon request.

Course Text:

The course text is Logistic Regression Models by Joseph Hilbe, which you can view at the publisher's website here. CRC Press typically gives students a generous discount when the text is ordered using this form.

Be sure to purchase your copy of the text prior to the course starting date.

Software:

Course participants may use any software that is capable of doing logistic regression. The instructor is most familiar with Stata, and the methods covered in this course will primarily be illustrated in Stata. Nearly all Stata commands, however, have corresponding R code at the end of each chapter. 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 and the illustrations and assignments are fully integrated 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: R-language solutions to assignments will be provided in this course, and R code is provided at the end of the chapters in the text duplicating nearly all Stata examples used in the text. R code for and tutorial help from the instructor or TA will be available but limited. If you want to use R with this course, you should have some prior experience and facility with it. 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 instructor and 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 can offer limited assistance with SPSS, but there is no TA support. While SPSS is easier to use than R or SAS for the purposes of this course, we nonetheless recommend that 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.

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Logistic Regression

Instructor(s):
Dates:
March 16, 2012 to April 13, 2012September 07, 2012 to October 05, 2012
Course Fee: $499
Academic Discounted Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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