You may know the
joke: "There are just 10 types of
people - those who understand binary and those who don't."
As with all online courses at statistics.com, there are no set hours when participants must be online; course work requires about 15 hours per week.
SESSION
1: Basic Terminology and Concepts
SESSION
2: Logistic Model Construction
SESSION
3: Analysis, Fit, and Interpretation of the Logistic Model
SESSION 4: Binomial Logistic Regression and Overdispersion
Logistic regression
(modeling 0/1 data) has become a ubiquitous technique, particularly since the
rise of "big data" analytics and businesses' need for classification
models (purchase or not, click or not, repay loan or not, etc.). 0/1 (yes/no) data ("same/different," "occur/don't
occur," ...) are ubiquitous and modeling them is the province of logistic
regression. Like many important ideas in
statistics, logistic regression did not become practical until the advent of
computing power and availability. Learn
all about it in Joseph Hilbe's online course, "Logistic Regression“. For more details please visit at http://www.statistics.com/logistic.
"Logistic
Regression" 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.
Dr. Joseph Hilbe, the instructor, the author of "Logistic Regression Models," and also "Generalized Linear Models and Extensions" and "Generalized Estimating Equations," was, until recently, the software reviews editor for "The American Statistician." An ASA Fellow, he is currently Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology. Participants can ask questions and exchange comments directly with Dr. Hilbe via a private discussion board throughout the period.
Dr. Joseph Hilbe, the instructor, the author of "Logistic Regression Models," and also "Generalized Linear Models and Extensions" and "Generalized Estimating Equations," was, until recently, the software reviews editor for "The American Statistician." An ASA Fellow, he is currently Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology. Participants can ask questions and exchange comments directly with Dr. Hilbe via a private discussion board throughout the period.
As with all online courses at statistics.com, there are no set hours when participants must be online; course work requires about 15 hours per week.
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.
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.
Course Program:
Course outline: The course is
structured as follows
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
You will be able to ask
questions and exchange comments with the instructors via a private discussion
board throughout the course. The courses take place online at
statistics.com in a series of 4 weekly lessons and assignments, and require about
15 hours/week. Participate at your own convenience; there are no set
times when you must be online. You have the flexibility to work a bit every
day, if that is your preference, or concentrate your work in just a couple of
days.
For Indian participants
statistics.com accepts registration for its courses at special prices in Indian
Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune
(www.c-elt.com).
If you have any query
please feel free to call me or write to me.
For More details contact
at
Call: 020 66009116
Websites:
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