Applied Logistic Regression

开始时间: 待定 持续时间: Unknown

所在平台: Coursera

课程类别: 医学

大学或机构: The Ohio State University(美国俄亥俄州立大学)



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This Applied Logistic Regression course provides theoretical and practical training for epidemiologists, biostatisticians and professionals of related disciplines in statistical modeling with particular emphasis on logistic regression.

The increasingly popular logistic regression model has become the standard method for regression analysis of binary response data in the health sciences.

By the end of this course, students should

  • Master methods of statistical modeling when the response variable is binary.
  • Be confident users of the Stata package for computing binary logistic regression models.

This is a hands-on, applied course where students will become proficient at using computer software to analyze data drawn primarily from the fields of medicine, epidemiology and public health.

There will be many practical examples and homework exercises in this class to help you learn. If you fully apply yourself in this course and complete all of the homework, you will have the opportunity to master various methods of statistical modeling and you will become a more confident user of the Stata* package for computing linear, polynomial and multiple regression.

*Access to Stata will be provided at no cost for the duration of this course.

Note: Enrollment for this course will close on Wednesday, May 13, 2015.

Centre Virchow-Villermé logo

This course was developed in partnership with the Centre Virchow-Villermé for Public Health Paris-Berlin, a bi-national centre of the Université Sorbonne Paris Cité and Charité – Universitätsmedizin Berlin. Special support was contributed from the Université Paris Descartes that also belongs to the community of Université Sorbonne Paris Cité.


Week One
  • Logistic Regression Analysis
  • Fitting the Logistic Model
Week Two
  • The Likelihood Ratio Test
  • Finding a Confidence Interval for β and π
  • The Multiple Logistic Regression Model
  • Fitting the Multiple Logistic Regression Model
Week Three
  • Confidence Intervals for β, the Logit, and π
  • Interpretation of Coefficients
  • Dichotomous Independent Variables
Week Four
  • Polychotomous Independent Variables
  • Continuous Independent Variables
Week Five
  • Statistical Adjustment
  • Interaction and Confounding
Week Six
  • Estimating Odd Ratios in the Presence of Interaction
  • The 2X2 Table - Stratified Analysis vs Logistic Regression
  • Assessing the Fit of the Logistic Regression Model
Week Seven
  • Summary Measures of Goodness-of-Fit
  • Area Under the ROC Curve
Week Eight
  • R-squared-type Measures
  • Numerical Problems
  • Estimating the Mortality of ICU Patients





This course provides theoretical and practical training on the increasingly popular logistic regression model, which has become the standard analytical method for use with a binary response variable.


机器学习 逻辑回归 统计学