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STAT9003 - Biostatistics

Long Title:Biostatistics
Module Code:STAT9003
Credits: 10
NFQ Level:Expert
Field of Study: Statistics
Valid From: Semester 1 - 2011/12 ( September 2011 )
Module Delivered in 1 programme(s)
Module Coordinator: David Goulding
Module Author: AINE NI SHE
Module Description: For any undergraduate or graduate science student, one of the most fundamental skills they must acquire is the ability to apply appropriate statistical techniques to their particular research area. This module will provide such students with the skills necessary to design experiments, apply appropriate statistical methods and correctly interpret experimental data.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Apply appropriate statistical methods to biological science problems.
LO2 Interpret the results of statistical analyses performed by a software package or presented in research papers.
LO3 Choose an experimental design appropriate to a given problem and perform the correct statistical analysis on the resultant data.
LO4 Apply regression techniques to the analysis of experimental data to identify relationships between variables.
LO5 Understand the difference between parametric and non-parametric methods and when the most commonly used non-parametric methods should be applied.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.

No requirements listed
No Co Requisites listed

Module Content & Assessment

Indicative Content
The Normal Distribution
Features of the normal distribution and the relevance of the normal distribution to biological data.
Statistical Inference
Sampling distributions. Estimation and significance testing: procedures involving normal, t, F and Chi-square distributions. Analysis of variance.
Regression Analysis
Least squares. The simple linear model. Analysis of residuals, coefficent of determination. Analysis of variance for regression. Confidence limits for prediction. Introduction to multiple regression: model selection procedures. Sensitivity, Specificity, Receiver Operating Characteristic (ROC) Curves.
Design of Experiments
Experimental design for analysis of variance. Factorial experiments: 2-Factor factorial experiments, factorial experiments with more than 2 factors, factorial experiments with split plots. Determining the number of replicates.
Non-Parametric Methods
Non-Parametric versus Parametric methods. Typical non-parametric methods: The Sign test, Kruskal-Wallis analysis of ranks, Kendall's rank correlation coefficient.
Assessment Breakdown%
Course Work40.00%
End of Module Formal Examination60.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions Test learning outcomes. 1,2,4 20.0 Week 6
Short Answer Questions Test learning outcomes. 3,5 20.0 Week 12
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4,5 60.0 End-of-Semester
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The institute reserves the right to alter the nature and timings of assessment


Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture 4.0 Every Week 4.00
Lab Lab 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) No Description 8.0 Every Week 8.00
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 6.00
This module has no Part Time workload.

Module Resources

Recommended Book Resources
  • Jerrold H. Zar 2010, Biostatistical analysis, 5th Ed., Prentice Hall Upper Saddle River, N.J. [ISBN: 9780132065023]
Supplementary Book Resources
  • Thomas Glover, Kevin Mitchell, 2008, An Introduction to Biostatistics, 2nd Ed., Waveland Press, Inc. [ISBN: 1577665805]
  • Marc M. Triola, Mario F. Triola 2006, Biostatistics for the biological and health sciences, Pearson Higher Education [ISBN: 9780321546494]
This module does not have any article/paper resources
This module does not have any other resources

Module Delivered in

Programme Code Programme Semester Delivery
CR_SCMPB_9 Master of Science in Computational Biology 2 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie