production
Skip to Content

Log In

  1. University Home
  2. Catalogue of Courses
  3. Undergraduate
  4. 2025-2026
  5. Mathematics
  6. MA2512: Statistics

MA2512: STATISTICS (2025-2026)

Last modified: 13 Nov 2025 15:16


Course Overview

This is a second year statistics course. It covers the fundamental principles of probability and statistical inference and then applies these to the study an important class of statistical models called linear regression models. The course will cover both the mathematical theory and the applications, such as the fitting of linear statistical models with the use of the R software.

Course Details

Study Type Undergraduate Level 2
Term Second Term Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Simona Paoli

What courses & programmes must have been taken before this course?

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

  • The course starts with the fundamental notions of probability needed in statistical inference, including: random variables (both continuous and discrete) and their probability distributions; jointly distributed random variables, independent random variables; expectation, covariance, correlation.
  • These notions are then applied to statistical inference, which is about deducing information about a population based on a sample from that population. The likelihood function is introduced as well as the method of maximum likelihood estimation to obtain estimates for the parameters of the population distribution. Both point estimates and interval estimates are treated, the latter in the form of confidence intervals and hypotheses tests.
  • These notions of statistical inference are then applied to study linear regression models, starting with the simple linear regression model, which features a single explanatory variable. After introducing the equation of the model, the maximum likelihood estimates of the model parameters are deduced, and its geometric interpretation in terms of least squares estimates discussed. The statistical inference for the model parameters is treated and then used to calculate confidence intervals for expectation and prediction intervals for predicted values.
  • The linear regression model with more than one explanatory variables (called general linear model) is then studied, using tools from both linear algebra and statistical inference. The special case of polynomial regression is also studied. Point and interval estimates for the model parameters are discussed. Analysis of variance techniques (including the extra sum of squares) is treated to determine which explanatory variables are statistically significant.
  • The R software is used throughout to fit linear regression models to datasets. 

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2025 for 1st Term courses and 19 December 2025 for 2nd Term courses.

Summative Assessments

Exam

Assessment Type Summative Weighting 70
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Written feedback on the overall performance of the class.
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand the main probabilistic notions underpinning statistical inference.
ConceptualUnderstandUnderstand the theory of linear regression models.
ProceduralAnalyseFit linear regression models to datasets.
ProceduralApplyApply the principles and tools of statistical inference using the frequentist approach to estimation.

Homework

Assessment Type Summative Weighting 15
Assessment Weeks 31 Feedback Weeks 33

Look up Week Numbers

Feedback

Solutions provided on MyAberdeen and written feedback on marked scripts.
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand the main probabilistic notions underpinning statistical inference.
ProceduralApplyApply the principles and tools of statistical inference using the frequentist approach to estimation.

Homework

Assessment Type Summative Weighting 15
Assessment Weeks 38 Feedback Weeks 40

Look up Week Numbers

Feedback

Solutions provided on MyAberdeen and written feedback on marked scripts.
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand the theory of linear regression models.
ProceduralAnalyseFit linear regression models to datasets.

Formative Assessment

There are no assessments for this course.

Resit Assessments

Exam

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Best of written exam (100%) or written exam (70%) with carried forward in-course assessment (30%)
Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand the theory of linear regression models.
ProceduralAnalyseFit linear regression models to datasets.
ProceduralApplyApply the principles and tools of statistical inference using the frequentist approach to estimation.
ConceptualUnderstandUnderstand the main probabilistic notions underpinning statistical inference.
 

Compatibility Mode

We have detected that you are have compatibility mode enabled or are using an old version of Internet Explorer. You either need to switch off compatibility mode for this site or upgrade your browser.