In this module you will learn the basics of statistical inference, including probability, sampling variability, confidence intervals and how to identify patterns in data and to represent them using statistical models. You will learn the essential mathematical techniques that are required for the implementation and interpretation of statistical and machine learning methods. You will learn how to fit statistical models to data, to evaluate whether models are appropriate given the context of the data and how they can be used to quantify relationships and for prediction.

Pre-requisites: None

The aim of this module is to equip students with the skills they will need to perform data science techniques and statistical analysis and to understand and interpret the outputs. Initially the focus will be on understanding essential concepts in probability and mathematics that underpin statistical analysis. Statistical distributions will be explored and used as the basis of statistical inference, with an emphasis on how data can inform decision making. Regression modelling will be introduced as a method of understanding relationships between variables and for prediction. Model diagnostics and methods for assessing model fit will be used to evaluate whether regression models are fit for purpose.

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

Module Specific Skills and Knowledge:

1. Understand principles of probability and sampling;
2. Apply statistical regression models to data, choosing the appropriate form based on the form and origins of the data 3 Perform regression and machine learning in R/RStudio

Discipline Specific Skills and Knowledge:

4. Understand random sampling and statistical distributions
5. Understand the methodology, and practical use, of regression modelling
6. Assess whether a regression model is appropriate in a given setting (model checking and diagnostics) and whether it provides an accurate representation of relationships within data

Personal and Key Transferable/ Employment Skills and Knowledge:

7. Statistical analysis skills;
8. Use R/RStudio and other software to implement statistical and data science methods
9. Use learning resources effectively
10. Communicate the results of data analysis clearly and accurately, both in writing and verbally

Topics will include:

• Data and variables;
• Initial data analysis;
• Probability;
• Sampling;
• Statistical distributions;
• Point estimation and confidence intervals
• Linear regression;
• Model selection;
• Non-parametric statistics.

Reassessment will be by coursework and/or test in the failed or deferred element only. For deferred candidates, the module mark will be uncapped. For referred candidates, the module mark will be capped at 50% 

Basic Reading:

ELE: http://vle.exeter.ac.uk/

 

Faraway, J.J., Linear Models with R, (2nd edition), Chapman & Hall Dobson, A.J., Introduction to Statistical Modelling,  Springer

Heumann, C., Schomaker, M., Shalabh, Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R, Springer

A First Course in Probability. Sheldon Ross. Pearson

Schaum's outline of probability and statistics. Spiegel, Murray R., John Schiller, Alu Srinivasan. McGraw-Hill

Introduction to Probability with Statistical Applications. Geza Schay. Birkhauser

Reading list for this module: