F21SA - Statistical Modelling and Analysis

Course leader(s):

• Damian Clancy (Edinburgh, September)
• Adrian Turcanu (Dubai, September)
• Haslifah Hasim (Dubai, September)

Syllabus

1. Basic probability concepts (1.1 1. Random variables and their distributions, 1.2 2. How distributions relate to sampling scenarios)

2. Joint distributions (2.1 1. Joint distributions, 2.2 2. Sums of random variables, 2.3 3. Central limit theorems)

3. Classical inference (3.1 1. Point estimation, moment estimators and maximum likelihood, 3.2 2. Confidence intervals – calculation and interpretation, 3.3 3. Hypothesis testing and p-values)

4. Essentials of Bayesian inference (4.1 1. Priors and posteriors, 4.2 2. Credible intervals, 4.3 3. Predictive distributions)

5. Regression (5.1 1. Linear regression, 5.2 2. Correlation, 5.3 3. Multiple regression)

6. Statistical computing (6.1 1. Analyse data and perform a variety of calculations using R statistical software.)

Learning outcomes

By the end of the course, students should be able to do the following:

• Use probability theory to calculate probabilities in a variety of contexts.
• Distinguish between distributions and use the appropriate formulae and statistical tables.
• Estimate unknown parameters of a distribution through point estimators or confidence intervals using model fitting methods.
• Conduct hypothesis tests in various contexts and interpret results appropriately.
• Construct linear regression models fitting the data to predict variables or study correlations.
• Derive and interpret posterior distributions by using Bayesian inference.
• Analyse data and perform a variety of calculations using R statistical software.
• Formulate appropriate reflections in a statistical study.

Further details

Curriculum explorer: Click here

SCQF Level: 11

Credits: 15