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Advanced Logistic Regression- Statistics.com

Statistics.com
0-6 Month Online
Advanced 24-Apr-2015
Online Business Analytics
Online Self Paced 1326
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After taking this online course, “Advanced Logistic Regression” participants will be able to specify, implement and interpret the output of a variety of advanced logistic regression models.

This course moves beyond the topics covered in “Logistic Regression” and covers a number of situations that call for logistic-based modeling, including a variety of ordered-categorical response (both proportional and non-proportional) models, multinomial models, panel models with fixed and random effects, GEE and quasi-least-squares models, multi-level models, survey logistic models, discriminant logistic models, skewed and penalized logistic regression, median unbiased estimation, Monte Carlo sampling, and exact logistic regression.

Course Program:

  • Week 1 – Proportional Odds Models
  • Week 2 – Multinomial Response Model
  • Week 3 – Panel and Mixed Models
  • Week 4 – Penalized and Exact Models

Important Date

April 24, 2015 to May 22, 2015

Duration: – 4 Weeks

Time Requirement: about 15 hours per week, at time of your choosing.

Fees: INR 37,740 (assuming $ = INR 60)

Part time/ Full Time:

Part Time

Some familiarity with calculus is helpful for a complete understanding of model development.

Who Should Take This Course:

Researchers in medicine, other life sciences, business, social science, environmental science, engineering and other fields who need to predict or model 1/0 or “yes-no” binary type responses as well as models having categorical and proportional responses. Those who deal with classifying data into risk groups as well as those who handle longitudinal and clustered data will find the course valuable.

  • Statistics
  • Dr Joseph Hilbe
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Advanced Logistic Regression is a very focused offering from Statistics.com aimed towards people, who spend a lot of time developing Logistic Regressions. Superb course for a specialization – given the amount of usage of Logistic regression in a lot of industries, but it comes at a very high cost for an online course.

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