This core module aims to provide students with a knowledge of the fundamentals of statistical theory and some experience of analysing data using statistical software. Students following the Health Data Science theme are required to take this core module instead of Principles of Biostatistics. Other students with sufficient pre-requisite knowledge may also choose to take this module.
Module Learning Outcomes:
By the end of the module, students should be able to:
- compare and contrast frequentist and Bayesian statistical theory
- choose appropriate models and methods for analysing a given dataset
- explain the assumptions made by these models and methods
- interpret the results obtained from application of these methods
- describe how the parameters of these models are estimated
- fit these models and apply these methods using R, and interpret the output
- perform simple likelihood and Bayesian calculations
- write simple R programs to determine repeated-sampling properties of estimators by simulation (and understand such programs).
Understanding of the concepts of integration and differentiation, logarithms and exponents, and matrix inversion; ability to perform simple manipulation of vectors and matrices (e.g. addition and multiplication); good knowledge of basic concepts of probability theory (e.g. probability density functions, marginal and conditional distributions, random variables, expectation and variance).
Lectures, computer practicals. Some preliminary reading may be required.
The module will be assessed in two parts. Part one (25% of module grade) is a timed, open-book test involving calculations, interpretation of the results, and interpretation of concepts taught in the module. Part two (75% of module grade) will involve a data set and a research question. Students will use R to analyse this data set, and then write a clear and detailed report describing the methods they used, the results they obtained, their interpretation of these results, and their conclusions.
Module Length: 8 days over 4 weeks