Module Details
| Module Code: |
STAT9008 |
| Title: |
Applied Statistics for Biology
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Long Title:
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Applied Statistics for Biology
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| NFQ Level: |
Advanced |
| Valid From: |
Semester 1 - 2021/22 ( September 2021 ) |
| Field of Study: |
4620 - Statistics
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| Module Description: |
This module focuses on the application of statistical methods to biological data. Students will acquire knowledge, skills and competences in the areas of statistical models, sampling theory, hypothesis testing, design of experiments and regression analysis. Upon completion, students will be able to plan, conduct, analyse, and interpret controlled experiments using appropriate statistical data analysis.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Evaluate the results of statistical analyses performed using software packages. |
| LO2 |
Construct an appropriate experimental design for a given problem, identify the correct statistical analysis to use and interpret the outcome of this analysis. |
| LO3 |
Apply regression techniques to experimental data and interpret the results. |
| LO4 |
Differentiate between when parametric and non-parametric statistical methods should be used and apply these methods to biological data. |
| LO5 |
Communicate the outputs of statistical analyses to a wider audience of peers through presentation and/or report of professional scientific standard. |
| Dependencies |
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 MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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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.
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| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
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.
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| No requirements listed |
| Indicative Content |
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The Importance of the Normal Distribution
Features of the normal distribution and relevance of it to biological data. Testing for normality.
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Hypothesis Testing
One-sample, Two independent sample, Related Samples, ANOVA
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Design of Experiments
Experimental Design for ANOVA, 2-factor factorial experiments, experiments with more than 2 factors, split plot designs, Calculating the number of replicates.
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Regression Analysis
Least squares, simple linear regression models. Assumptions, collinearity, interpreting coefficients, model fitting and model diagnostics.
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Non-parametric methods
Non-Parametric versus Parametric methods. Typical Non-parametric methods: The sign test, Kruskal Wallos, Analysis of Ranks.
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Module Content & Assessment
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| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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The University reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Theory on course topics and discussion of relevant examples from biological sciences. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Development of practical competency through laboratory-based learning. |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Review of lecture notes and recommended material and preparation of reports for selected laboratory sessions and in-class topics. |
Every Week |
3.00 |
3 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Theory on course topics and discussion of relevant examples from biological sciences. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Development of practical competency through laboratory-based learning. |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Review of lecture notes and recommended material and preparation of reports for selected laboratory sessions and in-class topics. |
Every Week |
4.00 |
4 |
| Total Hours |
8.00 |
| Total Weekly Learner Workload |
8.00 |
| Total Weekly Contact Hours |
4.00 |
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Module Resources
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| Recommended Book Resources |
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Jerrold H. Zar. (2010), Biostatistical Analysis, 5th. Prentice-Hall/Pearson, [ISBN: 0132065029].
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Thomas Glover, Kevin Mitchell. (2015), An Introduction to Biostatistics, 3rd. Waveland Press, [ISBN: 1478627794].
| | Supplementary Book Resources |
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Marc M. Triola, Mario F. Triola. (2014), Biostatistics for the biological and health sciences, Pearson Education, [ISBN: 1292039647].
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Richard K. Burdick, David J. LeBlond, Lori B. Pfahler, Jorge Quiroz, Leslie Sidor, Kimberly Vukovinsky, Lanju Zhang. (2017), Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry, Springer, [ISBN: 9783319501864].
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Susan Morgan, Tom Reichert, Tyler R. Harrison. (2016), From Numbers to Words - Reporting Statistical Results for the Social Sciences, Routledge, [ISBN: 9781138638082].
| | This module does not have any article/paper resources |
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| This module does not have any other resources |
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