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Titanic Passengers Case - Logistic Regression

The Titanic Passengers analytics case study.  The complete collection of analytics cases is available from Collection: Analytics Case Study Library.


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Key ideas:

Logistic regression, log odds and logit, odds, odds ratios, prediction profiler. 


The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.

One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others.

The Task:

We use this rich and storied example to explore some questions of interest about survival rates for the Titanic. For example, were there some key characteristics of the survivors? Were some passenger groups more likely to survive than others? Can we accurately predict survival?

We will fit a logistic regression model using the available data to explore these questions.



From Building Better Models with JMP® Pro, Chapter 5, SAS Press (2015). Grayson, Gardner and Stephens. Used with permission.