10 Credits AUTUMN



Aims/Description:   This unit aims to equip candidates with the core skills needed to design experiments and analyse observational and experimental data. They will learn about: (1) principles of experimental design and sampling; (2) basic principles of frequentist inference; (3) the link between resampling methods and frequentist ideas; (4) how to choose an appropriate statistical model for your data (t-tests, ANOVA, regression and ANCOVA); (5) model fitting, evaluation and inference using the R statistical programming language. The unit will focus on practical, rather than theoretical treatments of data analysis, using simulations in R to motivate learning wherever possible. The main topics are: Understanding statistics. You will learn how to: (1) Explain frequentist concepts such as populations vs. samples, null hypotheses and p-values; (2) Apply bootstrap and permutation tests to evaluate differences between two means; (3) Use a t-test to evaluate the significance of mean differences; (4) Construct confidence intervals for sample statistics. Principles of Experimental Design. You will learn how to: (1) Explain the three fundamental principles of experimental design (replication, randomisation and blocking); (2) Evaluate the strengths and weaknesses of different sampling protocols; (3) Design experiments using factorial and blocked structures; (4) Demonstrate and awareness of more complex designs. Analysis of variance. You will learn how to: (1) Use geometric reasoning to explain why differences in variances and F-tests are used to evaluate significance in ANOVA; (2) State the assumptions of ANOVA; (3) Specify and fit one-way and two-way ANOVA in R; (4) Interpret ANOVA tables and summaries of fitted coefficients; (5) Report the results of ANOVA in tables and graphs. Regression. You will learn how to: (1) Use geometric reasoning to explain how a 'line of best fit' is found in simple regression; (2) State the assumptions of regression; (3) Interpret summaries of fitted coefficients; (4) Report the results of simple regression in tables and graphs. Practical Model Fitting and Model Selection. You will learn how to: (1) Use R to check model assumptions and critically evaluate model fit; (2) Choose appropriate transformations to remedy problems with a fitted model; (3) Explain the key principles that underpin model selection.

Restrictions on availability: Only Available to students on the Biology with a Year Abroad BSc APSU25 and MBiolSci APSU29 programmes

Staff Contact: CHILDS DYLAN Z
Teaching Methods: Lectures, Problem solving, Independent Study
Assessment: Classroom testing

Information on the department responsible for this unit (Animal and Plant Sciences):

Departmental Home Page
Teaching timetable

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NOTE
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

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Teaching methods and assessment displayed on this page are indicative for 2021-22. Students will be informed by the academic department of any changes made necessary by the ongoing pandemic.

Western Bank, Sheffield, S10 2TN, UK