Probability, Statistics and Data - 2020 entry

Our ability to collect and analyse data is increasingly driving our world. Statistics is concerned with both the practice of analysing data to learn about the world, and also the theory that underpins the methods and models used for data collection and analysis. This theory is itself based on probability, the mathematics of chance and uncertainty. In this module, you will learn about the mathematics of probability, ways to count possible outcomes, and the key ideas of statistical modelling and inference, in which probability is used to quantify uncertainty. You will also gain experience of employing these ideas to analyse data using advanced statistical software such as the package R. The module develops key ideas and techniques that underpin modules such as MTh2006 Statistical Modelling and Inference. 

The aim of this module is to introduce you to basic topics in probability, statistics and data analysis. This module provides the foundation for the second-year stream in Statistical Modelling and Inference, and subsequent modules in statistics in years 3 and 4.

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

 

Module Specific Skills and Knowledge:

1 demonstrate a sound understanding of selected essential topics in probability theory, including the ability to apply those concepts in tackling an appropriate range of problems;

2 demonstrate a knowledge of the basic ideas of statistical inference, including probability distributions, point and interval estimation and hypothesis tests;

3 use the computer languate R to manipulate, visualise and analyse data.

Discipline Specific Skills and Knowledge:

4 show sufficient knowledge of fundamental mathematical and statistical concepts, manipulations and results.

Personal and Key Transferable/ Employment Skills and  Knowledge:

5 reason using abstract ideas, formulate and solve problems and communicate reasoning and solutions effectively in writing;

6 use learning resources appropriately;

7 exhibit self management and time management skills.

- the nature of data;

- data visualisation

- probability theory and applications;

- random variables and moments;

- discrete and continuous distributions;

- bivariate and multivariate distributions;

- parametric statistical models;

- prediction and simulation;

- applications of models;

- combinations of random variables;

- transformation of random variables;

- point estimation;

- interval estimation;

- hypothesis testing.

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.
 

ELE – http://vle.exeter.ac.uk

Reading list for this module: