好色tv

- Academic Registry


MA30322: Statistical modelling and data analytics 3A

[Page last updated: 22 May 2025]

Academic Year: 2025/26
Owning Department/School: Department of Mathematical Sciences
Credits: 12 [equivalent to 24 CATS credits]
Notional Study Hours: 240
Level: Honours (FHEQ level 6)
Period:
Semester 1
Assessment Summary: CWRG 25%, EXCB 75%
Assessment Detail:
  • Report Group (CWRG 25%)
  • Closed-book written examination (EXCB 75%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Before taking this module you must ( take MA22014 AND take MA22015 ) OR take MA20227 OR take MA20302
In taking this module you cannot take MA30084 OR take MA30091
Learning Outcomes: By the end of the module you will be able to:
  • formulate a problem and carry out an exploratory data analysis
  • choose an appropriate generalised linear model for a given set of data
  • fit a chosen model using R, selecting terms for inclusion in the model and assessing the adequacy of the selected model
  • make inferences on the basis of a fitted model and recognise the assumptions underlying these inferences and possible limitations to their accuracy
  • present results in a suitable style



Synopsis: You will work with a class of statistical models known as generalised linear models. You will learn the theory of statistical inference for these models and carry out data analyses in case studies involving these types of data.

Content: Normal linear models: vector and matrix representation. Large sample theory for maximum likelihood estimation and testing. Generalised linear models: exponential families, standard form, linear predictors and link functions, deviance. Continuous and discrete response distributions, contingency tables. Model building: subset selection and stepwise regression, information criteria, collinearity in regression variables. Model checking: analysis of residuals. Implementation of linear and generalised linear models in R. Formulating statistical problems. Choosing an appropriate method of analysis, model building, testing assumptions and model refinement. Presentation of results in an appropriate format. Application of the above methods to a variety of real-life statistical problems.

Course availability:

MA30322 is Optional on the following courses:

Department of Mathematical Sciences
  • USMA-AFM14 : MMath(Hons) Mathematics (Year 4)
  • USMA-AAM15 : MMath(Hons) Mathematics with Study year abroad (Year 4)
  • USMA-AKM15 : MMath(Hons) Mathematics with Year long work placement (Year 5)

Notes:

  • This unit catalogue is applicable for the 2025/26 academic year only. Students continuing their studies into 2026/27 and beyond should not assume that this unit will be available in future years in the format displayed here for 2025/26.
  • 好色tv and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.