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MAT-10077 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2025/26 Last Updated: 04 December 2025

MAT-10077 - Statistics with Applications in R
Coordinator: Ghasem Yadegarfar
Lecture Time: See Timetable...
Level: Level 4
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2025/26

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2025/26

This module aims to provide you with a sound knowledge of core statistical concepts, skills and techniques. It will enable you to apply statistical ideas to solve problems from a wide range of disciplines; developing competency in interpreting and explaining solutions of problems in non-technical language; giving fluency in procedural skills, common problem-solving skills and strategies; using statistical tools to analyse real data sets in the statistical software environment R.

Aims
The module aims to help students to:
1) have a sound knowledge of statistical concepts, skills and techniques;
2) apply statistical ideas to solve problems from a wide range of disciplines;
3) be competent in interpreting and explaining solutions of problems in non-technical language;
4) be fluent in procedural skills, common problem-solving skills and strategies;
5) use statistical tools to analyse real data sets using statistical software.

Intended Learning Outcomes

describe and illustrate data using suitable summary statistics and plots: 1,2
apply elementary statistical concepts and terminology such as sampling distribution, confidence intervals, and hypothesis tests: 1,2
conduct data analysis and statistical inferences, including confidence intervals and hypothesis tests, in situations and interpret the results of such analyses: 2
use various statistical tools to analyse real data sets in the statistical software environment R: 1
present the results of statistical data analysis including graphical and numerical summaries to an audience with diverse statistical knowledge: 2

Study hours

24 hours lectures
24 hours examples classes
12 hours lab sessions
90 hours of independent studies, including:
36 hours independent working on lecture material
12 hours preparation to examples classes
12 hours independent working on the assignment
30 hours revising for exam


School Rules

None

Description of Module Assessment

1: Assignment weighted 30%
Assignment
Students will receive a real dataset to analyse using the R software package. They will have 5 questions to answer that will lead them through analysis of the data, from summarising the data using suitable summary statistics and plots through to performing hypothesis tests and fitting linear regression models. Students will need to interpret and clearly report their findings for each question.

2: Exam weighted 70%
Exam
A standard 2h exam, within which students will be asked to answer approximately 5 questions covering the different topics covered throughout this module, all of which are compulsory

The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 04 December 2025