This unit builds on introductory 1st year statistics units and is targeted towards students in the agricultural, life and environmental sciences. It consists of two parts and presents, in an applied manner, the statistical methods that students need to know for further study and their future careers. In the first part the focus is on designed studies including both surveys and formal experimental designs. Students will learn how to analyse and interpret datasets collected from designs from more than than 2 treatment levels, multiple factors and different blocking designs. In the second part the focus is on finding patterns in data. In this part the students will learn to model relationships between response and predictor variables using regression, and find patterns in datasets with many variables using principal components analysis and clustering. This part provides the foundation for the analysis of big data. In the practicals the emphasis is on applying theory to analysing real datasets using the statistical software package R. A key feature of the unit is using R to develop coding skills that are become essential in science for processing and analysing datasets of ever increasing size.
Two 1-hour lectures per week, one 3-hour computer practical per week
One exam during the exam period (50%),three reports (10% each), ten online quizzes (2% each)
No textbooks are recommended but useful reference books are: - Mead R, Curnow RN, Hasted AM (2002) 'Statistical methods in agriculture and experimental biology.' (Chapman and Hall: Boca Raton). - Quinn GP, Keough MJ (2002) 'Experimental design and data analysis for biologists.' (Cambridge University Press: Cambridge, UK).
Available as a degree core unit only in the Agriculture, Animal and Veterinary Bioscience, and Food and Agribusiness streams
[6cp from (ENVX1001 or ENVX1002 or BIOM1003 or MATH1011 or MATH1015 or DATA1001)] OR [3cp from (MATH1XX1 or MATH1906 or MATH1XX3 or MATH1907) and an additional 3cp from (MATH1XX5)]