STAT50010 Introductory Statistics using R for computational biologists.

Academic Year 2015/2016

Basic statistical ideas will be introduced including (1)Common probability distributions. (2) Confidence Intervals and Hypothesis Testing. (3) Classical parameter estimation theory. (4) Generlized Linear Models (5)The Bootstrap. (6) Model Comparison and validation. Examples and coding will be performed in R.

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Curricular information is subject to change

Learning Outcomes:

Learn how to interpret and calculate descriptive statistics.Learn to programme in R.Gain exposure to formal statistical testing.Gain exposure to the rationale behind hypothesis testing.Gain exposure to some common statistical models. Learn how to validate and compare statistical models.

Student Effort Hours: 
Student Effort Type Hours


Computer Aided Lab


Autonomous Student Learning




Requirements, Exclusions and Recommendations
Learning Recommendations:

Basic statistics; types of random variable, simple summary statistics, etc.

Description % of Final Grade Timing
Continuous Assessment: Three assignments.


Throughout the Trimester


This module is not passable by compensation

Resit Opportunities

In-semester assessment


If you fail this module you may repeat, resit or substitute where permissible

Name Role
Dr Michael Salter-Townshend Lecturer / Co-Lecturer
Dr Guillermo Vinué Lecturer / Co-Lecturer