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MAT-10059 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

For academic year: 2023/24 Last Updated: 13 February 2024

MAT-10059 - Fundamentals of 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 2023/24

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2023/24

This module provides an introduction to common statistical techniques for exploring, summarising and modelling data. Through this module, the students will be able to build up appropriate statistical knowledge, using the R statistical software package, analyse a real-life dataset and interpret the results clearly. These analytical skills are crucial for their future careers and further study.

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
: 1,2
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: 1,2

Study hours

24 hours lectures
12 hours examples classes
12 hours lab sessions
102 hours of independent studies, including:
36 hours independent working on lecture material
12 hours preparation to examples classes
18 hours independent working on coursework
36 hours revising for exam


School Rules

None

Description of Module Assessment

1: Coursework weighted 30%
Coursework Assignment
Student will receive a real dataset to analyse using the R software package. They will have a series of 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

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and 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/student-terms-and-conditions/

Last Updated: 13 February 2024