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Statistical Inference & Linear Models - MAT00053I

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  • Department: Mathematics
  • Credit value: 20 credits
  • Credit level: I
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

An investigation of classical Frequentist statistical methodology with application to common data analysis problems, following on from more theoretical/foundational material in Probability & Markov Chains.

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Additional information

This module is the second part of the Probability & Statistics stream, and as such must be taken with the first part (Probability & Markov Chains).

Pre-requisite modules:

  • Intro to Prob & Stats
  • Probability & Markov Chains

Post-requisite modules:

  • The majority of H/M level statistics modules

     

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

The students will look at the theory and practice of common classical statistical procedures that are useful in their own right and are built on in later modules. Of particular importance are confidence intervals, hypothesis testing and linear regression. The module includes coursework in which students will produce a statistical report, demonstrating both their understanding and their computational skills

Module learning outcomes

  1. By the end of the module, students will be able to:

  2. Perform, interpret and critique common Frequentist statistical calculations (namely confidence intervals and hypothesis tests).

  3. Modify or construct similar tools based on the theory that supports them.

  4. Explain procedures for fitting linear models and assessing their adequacy.

  5. Explain and motivate procedures for variable selection.

  6. Implement key methodology with real data and to communicate its significance in a statistical report.

Module content

  • Confidence intervals (parametric and bootstrap)

  • Hypothesis testing (including permutation tests)

  • Linear models

  • Data analysis with R

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Special assessment rules

None

Additional assessment information

There will be five formative assignments with marked work returned in the seminars. At least one of them will contain a longer written part, done in LaTeX.

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Module feedback

Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.

Indicative reading

Faraway, J.J., 2004. Linear models with R. Chapman and Hall/CRC.

Wood, S.N., 2015. Core statistics (Vol. 6). Cambridge University Press.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.