Statistical Modelling - 2019 entry

Statistics is concerned with the collection and summarisation of data, and the methods introduced in this module are employed in many fields, including finance, medicine, engineering, epidemiology and risk management, to name but a few. This module will introduce you to fundamental concepts in experimental design and classical techniques for statistical inference. You will learn how to use a range of statistical tools for analysing data, including visualisation and summarisation, point and interval estimation, hypothesis testing, likelihood theory and linear regression. You will learn how to apply these techniques using the open-source statistical software R.

Pre-requisite module: “Probability and Statistics” (ECM1909), or equivalent

This module aims to lay the foundations for a thorough understanding of modern statistical theory and practice. It aims to help students to learn how to analyse and present data effectively, to select and use appropriate probability models, and to use these models to learn about real-life systems using observed data.

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Understand fundamental concepts of classical statistical theory;

2 Demonstrate knowledge of the processes involved in conducting a statistical analysis;

3 Apply basic inferential procedures, such as point- and interval estimation, and hypothesis testing;

4 Understand fundamental concepts in experimental design, including sources of errors and bias and statistical power;

5 Learn how to build and implement a range of statistical regression models, to understand the processes involved in model building and validation, and to understand the limitations of these techniques and how the outputs can be interpreted;

6 Apply these ideas in practice to real data using the open source software package R;

Discipline Specific Skills and Knowledge:

7 Demonstrate a clear understanding of fundamental statistical concepts, including notions of uncertainty and evidence, and the processes involved in designing, checking and refining statistical models, including the limitations of the approaches and how these impact inference;

8 Gain computational skills in R, and gain a better understanding of the practical implementation of these approaches;

Personal and Key Transferable / Employment Skills and Knowledge:

9 Demonstrate key data analysis skills, including the practical implementation in R;

10 Formulate and solve problems and communicate reasoning and solutions effectively in writing;

11 Make appropriate use of learning resources;

12 Demonstrate self-management and time management skills.

- Visualisation and summarisation tools (e.g. scatterplots, histograms, box-and-whisker plots, summary statistics);

- Point- and interval-estimation (including e.g. bias, consistency, confidence intervals);

- Hypothesis testing (e.g. null vs. alternative hypotheses, p-values, interpretation);

- Experimental design;

- Introduction to likelihood theory and maximum likelihood;

- Linear regression (including continuous and categorical explanatory variables, confidence intervals, model fitting prediction);

- Multiple regression;

- Model checking and validation;

- Model choice (e.g. likelihood ratio testing, AIC).

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

Basic Reading:

ELE - http://vle.exeter.ac.uk

Reading list for this module: