F21SA Statistical Modelling and Analysis

Dr Marcelo Pereyra

Course co-ordinator(s): Dr Marcelo Pereyra (Edinburgh).

Aims:

The aim of this course is to learn and apply a range of Statistical Modelling and Analysis techniques applicable for data analysis

Detailed Information

Pre-requisites: none.

Location: Dubai, Edinburgh.

Semester: 1.

Syllabus:

A practical understanding of:

  • Basic probability concepts: Random variables and their distributions; how distributions relate to sampling scenarios.
  • Joint distributions, Sums of random variables, Central limit theorems
  • Classical inference: Point estimation, moment estimators and maximum likelihood; Confidence intervals – calculation and interpretation; Hypothesis testing and p-values
  • Essentials of Bayesian inference: Priors and posteriors; Credible intervals; Predictive distributions
  • Modelling approaches: Regression and ANOVA;
  • Multivariate exploratory techniques: Principal Components Analysis + Factor Analysis; Introduction to non-parametric methods
  • Practical elements in R or Python

Learning Outcomes: Subject Mastery

  • Detailed and critical understanding of the concepts, issues, principles and theories of statistical modelling and analysis
  • Critical theoretical and detailed practical knowledge of statistical modelling and analysis techniques
  • Practical professional experience of analysing, designing, implementing and validating experiments using common statistical techniques

Learning Outcomes: Personal Abilities

  • Ability to deal with complex issues and make informed professional judgements about statistical models and analysis
  • Exercise substantial autonomy and initiative in performing data analysis.
  • Showing initiative and good professional team working skills in shared data analysis. (PDP)
  • Demonstrate critical reflection on statistical modelling and analysis issues. (PDP)

Assessment Methods:

Assessment: Examination: (weighting 70%) Coursework: (weighting 30%)

Re-assessment: Examination: (weighting 100%)

SCQF Level: 11.

Credits: 15.