Introduction to Logistic Regression

Overview
Many investigations often have data where there are only two possible categories of response - for example a surgical procedure may result or may not result in a complication, a crop may or may not become infested with pests, a patient's symptoms may or may not involved a sore throat.
This course will explain the use of logistic regression for studying associations between binary outcomes like those mentioned, and possible explanatory factors.

Who Should Attend?
Scientists and Technologists who already have some statistical training but whose knowledge is lacking in the area of regression methods for binary response data.  Prior attendance on A Review of Basic Statistics and Regression Analysis: A Hands-on Approach, or equivalent knowledge, is required for this course.

How You Will Benefit
You will be introduced to the increasingly widely used modelling technique of logistic regression for analysing binary response data, and learn how to fit and interpret such models.

What Do We Cover?
  • Binary data: examples, ungrouped and grouped data
  • Why is logistic regression required? Benefits
  • Logistic regression model for binary response data: link functions, odds, concept of likelihood for model fitting
  • Quantifying effects of explanatory variables: odds ratios, hypothesis testing and confidence intervals
  • Model comparisons and selection strategies
  • Assessing the goodness-of-fit of a logistic regression model: goodness-of-fit tests and residuals
  • Use of logistic regression models for prediction, ROC curves and inverse prediction (e.g. estimating an ED50)
  • Computational problems and potential remedies
  • Presentation of results from a logistic regression

Available Software
This course has practical exercises written for: Minitab, R, Stata
Note: For practical work participants must bring their own laptop with a fully licensed version of the software they wish to use.
Course Dates
Next run to be announced

Duration: 2 days
Price: £TBC

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