Introductory description

This module is core for students on an MSc in Statistics. It is not available for undergraduate students.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• Exploratory data analysis (numerical and graphical measures)
• A hands-on introduction to R, exercises to learn basics of R.
• Simpson's paradox, Regression to the mean, Correlation vs causation
• Simple linear regression; Correlation coefficient, SD line, Regression Line
• Multiple linear regression; Diagnostic plots, Hypothesis testing, ANOVA
• Structured Data (coming from simple experimental designs)
• Generalised Linear Models; Poisson and Binomial data
• Contingency tables and non-parametric tests

Learning outcomes

By the end of the module, students should be able to:

• Computational skills: Basic use of R, search for commands in help files and understand them, dealing with data (collecting, typing in, downloading, storing, sharing etc.)
• Descriptive statistics and Explorative Data Analysis (EDA): Data structures, appropriateness of data (relevance to the scientific question(S), completeness, quality etc.), representation of data (choice of the form, optimal layout, misleading representation etc.), strategies to explore certain aspects of the data
• Modelling and analysis: choice of model, discussion of model assumptions, fitting models, validation and comparison of models, prediction, sensitivity analysis (in respect to assumptions and sample data), simulation
• Context: translating scientific queries into statistical questions, classification of investigations, drawing scientific conclusions from statistical analysis
• Communication skills: listening, asking questions, explaining analysis, approach and delivering results to a non-statistician, writing a report

Indicative reading list

View reading list on Talis Aspire

Subject specific skills

-Data structures, appropriateness of data (relevance to the scientific question(s), completeness, quality etc.), representation of data (choice of the form, optimal layout, misleading representation etc.), strategies to explore certain aspects of the data
-choice of model, discussion of model assumptions, fitting models, validation and comparison of models, prediction, sensitivity analysis (in respect to assumptions and sample data), simulation
-translating scientific queries into statistical questions, classification of investigations, drawing scientific conclusions from statistical analysis

Transferable skills

-Basic use of R, search for commands in help files and understand them, dealing with data (collecting, typing in, downloading, storing, sharing etc.)
-listening, asking questions, explaining analysis approach and delivering results to a non-statistician, writing a report.