1. Overview of basic statistics and probability
1.1. Getting started with R
1.2. Basic concepts and data exploration
1.3. Probability distributions
1.4. Sampling, estimation and hypothesis testing
2. Introduction to multivariate data analysis
2.1. Multivariate data
2.2. Data reduction: principal components analysis and biplot
2.3. Supervised classification: discriminant analysis
2.4. Resampling and cross-validation
2.5. Correspondence analysis of count data
2.6. Low-dimensional visualisation: multidimensional scaling
3. Statistical modelling
3.1. Linear and generalised linear regression
3.2. Logistic regression for binary response
3.3. Poisson regression for counts
3.4. Additive models based on smooth splines
3.5. Model assessment and simplification
3.6. Regression analysis with many variables
Course work will be assessed out of 10 total marks. Student attitude and participation will also be considered.
Criteris específics de la nota «No Presentat»:
Not delivering any tasks.
Avaluació única:
To be discussed with the teacher at the start of the course.
Requisits mínims per aprovar:
A pass requires obtaining at least 5 marks, averaged over all tasks delivered.