ST23110 Linear Statistical Modelling with R
Introductory description
This module introduces the ideas and methods of statistical modelling and statistical model exploration.
Prerequisistes:
First Year Statistics Core (including ST117 Introduction to Statistical Modelling, ST118 Probability 1, and ST119 Probability 2) or equivalents.
AND
(Both ST229 Probability for Mathematical Statistics and ST230 Mathematical Statistics), or ST232/ST233 Introduction to Mathematical Statistics.
Leads to:
ST340 Programming for Data Science
ST344 Professional Practice of Data Analysis
ST346 Generalised Linear Models for Regression and Classification.
Module aims
To introduce students to the application of R software and its use as a tool for statistical modelling, specifically for working with linear models in a variety of different scenarios.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
This module introduces the study of applied statistics and the use of R to perform inference.
 Introduction to R, including variables, functions, vectors matrices, lists and control flow.
 Exploratory data analysis using R, including summary statistics and a wide array of plots.
 Linear regression: model assumptions; least squares estimators; fits, residuals and predictions; diagnostics; maximum likelihood estimators for normal linear regression and their sampling distributions; confidence intervals and hypothesis tests.
 Multiple linear regression: model assumptions, least squares estimators and their properties; estimators for normal linear models and their sampling distributions; confidence intervals and hypothesis tests; Ftests and analysis of variance; model selection and diagnostics.
Learning outcomes
By the end of the module, students should be able to:
 Know the fundamentals of using R for statistical computing.
 Describe and analyse data sets by performing exploratory data analysis using R.
 Analyse estimators for linear regression and multiple linear regression.
 Analyse and interpret data using suitable linear models, and understand the limits of such models.
 Communicate solutions to problems accurately with structured and coherent arguments.
Indicative reading list
 Linear Models In Statistics, Rencher and Schaalje, Wiley (2008).
View reading list on Talis Aspire
Subject specific skills
Demonstrate facility with advanced mathematical and probabilistic methods.
Demonstrate knowledge of key mathematical and statistical concepts, both explicitly and by applying them to the solution of mathematical problems.
Select and apply appropriate mathematical and/or statistical techniques
Create structured and coherent arguments communicating them in written form.
Select and apply appropriate computational techniques in a statistical programming language (for example, R) for exploratory data analysis.
Transferable skills
Problem solving skills: The module requires students to solve problems presenting their conclusions as logical and coherent arguments.
Written communication skills: Students complete written assessments that require precise and unambiguous communication in the manner and style expected in mathematical sciences.
Verbal communication skills: Students are encouraged to discuss and debate formative assessment and lecture material within smallgroup tutorials sessions. Students can continually discuss specific aspects of the module with the module leader. This is facilitated by statistics staff office hours.
Team working and working effectively with others: Students are encouraged to discuss and debate formative assessment and lecture material within smallgroup tutorials sessions.
Professionalism: Students work autonomously by developing and sustain effective approaches to learning, including timemanagement, organisation, flexibility, creativity, collaboratively and intellectual integrity.
Study time
Type  Required  Optional 

Lectures  20 sessions of 1 hour (20%)  2 sessions of 1 hour 
Practical classes  10 sessions of 1 hour (10%)  
Private study  40 hours (40%)  
Assessment  30 hours (30%)  
Total  100 hours 
Private study description
Weekly revision of lecture notes and materials, wider reading and practice exercises, working on problem sets and preparing for examination.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D
Weighting  Study time  

Assignment 1  20%  14 hours 
You will use the statistical programming language R to carry out calculations and fit models on provided data sets in response to a set of questions. You will present, discuss, and evaluate the results. The length of the report will not exceed 18 pages, including figures, tables, code and R output, The preparation and completion time noted below refers to the amount of time in hours that a wellprepared student who has attended lectures and carried out an appropriate amount of independent study on the material could expect to spend on this assignment. 

Assignment 2  20%  14 hours 
You will use the statistical programming language R to carry out calculations and fit models on provided data sets in response to a set of questions. You will present, discuss, and evaluate the results. The length of the report will not exceed 18 pages, including figures, tables, code and R output, The preparation and completion time noted below refers to the amount of time in hours that a wellprepared student who has attended lectures and carried out an appropriate amount of independent study on the material could expect to spend on this assignment. 

Linear Statistical Modelling with R examination  60%  2 hours 
You will be required to answer all questions on this examination paper.

Assessment group R
Weighting  Study time  

Inperson Examination  Resit  100%  
You will be required to answer all questions on this examination paper. To account for the absence of an assignment in the reassessment, the paper will include questions on using R for statistical computing, and performing exploratory data analysis using R.

Feedback on assessment
Individual feedback will be provided on problem sheets by class tutors.
Solutions and cohort level feedback will be provided for the examination
Students are actively encouraged to make use of office hours to build up their understanding, and to view all their interactions with lecturers and class tutors as feedback.
Courses
This module is Core for:
 Year 2 of USTAG302 Undergraduate Data Science
 Year 2 of USTAG304 Undergraduate Data Science (MSci)

USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
 Year 2 of G30A Master of Maths, Op.Res, Stats & Economics (Actuarial and Financial Mathematics Stream)
 Year 2 of G30B Master of Maths, Op.Res, Stats & Economics (Econometrics and Mathematical Economics Stream)
 Year 2 of G30C Master of Maths, Op.Res, Stats & Economics (Operational Research and Statistics Stream)
 Year 2 of G30D Master of Maths, Op.Res, Stats & Economics (Statistics with Mathematics Stream)
 Year 2 of G300 Mathematics, Operational Research, Statistics and Economics
 Year 2 of USTAG1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)
 Year 2 of USTAGG14 Undergraduate Mathematics and Statistics (BSc)
 Year 2 of USTAY602 Undergraduate Mathematics,Operational Research,Statistics and Economics