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Prof Fiona Steele COL 7.12

This course is available on the MPhil/PhD in Statistics. This course is available with permission as an outside option to students on other programmes where regulations permit.

A knowledge of probability and statistical theory to the level of ST102 and ST206, and linear regression to the level of ST211. Previous programming experience is not required but students who have no previous experience in R must complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7745).

This course provides an overview of modern applied statistics. It will cover an introduction to quantitative research design and causal inference, exploratory data analysis and data visualisation, generalised linear models, and generalised latent variable models (including mixed effects or multilevel models, longitudinal data analysis, and structural equation models). The course will have an applied emphasis with students gaining hands-on experience in data analysis using R and practice in the interpretation of results.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across Autumn Term. This course includes a reading week in Week 6 of Autumn Term.

Students will be expected to produce 1 project in the AT.

Students will be given a real dataset and asked to analyse the data to answer scientific questions and then write a report. Students' reports will be marked and feedback will be given.

Maindonald, J., & Braun, J. (2006). Data analysis and graphics using R: an example-based approach. Cambridge University Press

Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.

Skrondal, A. and Rabe-Hesketh (2004)  Generalized latent variable modeling : multilevel, longitudinal, and structural equation models. Chapman & H