Module Directory

This module will introduce the principles for the application of linear modelling methodologies for the analysis of experimental and observational data.

The first strand of the module will study the assumptions of the general linear model. Collinearity, influential data, assessing the fitted model and model selection techniques will be discussed. The second strand will introduce statistical methods for the efficient analysis of experiments when the data are normally distributed, for example one-way ANOVA. The methodology will be extended to logistic regression. The third strand of the module will study various multivariate methods for the analysis of large and high-dimensional data sets.

The aims of this module are:

• To provide the fundamental understanding of the underlying statistical methodologies.


• To provide capabilities of applying these methodologies to real experimental and observational data.


• To provide knowledge of conducting a robust statistical analysing of the data.


• To provide capabilities of interpreting the results effectively.

By the end of this module, students will be expected to:

1. Have a comprehensive understanding of study designs that produce observational and experimental data and interpretations on the results from observational or experimental data.


2. Understand comprehensively the various generalized linear regression models, including multiple linear regression, logistic regression, ANOVA and contingency table analysis.


3. Have a critical understanding of the well-established principles for the assessment of model fitting, including model selection, classification accuracy and cross validation.


4. Have an ability to deal with common obstacles for ordinary model fitting, including high-dimensional data, multicollinearity and missing values.


5. Have an ability to use R for the application of the generalised linear regression and machine learning techniques for the experimental and observational data analysis.

Syllabus

• Observational versus experimental data

• Confounding


• Randomized clinical trials


• Real-world data and causality

• Multiple linear regression

• Parameter estimation and interpretation


• Model assessment and model selection


• Dimension reduction and variable selection

• Logistic regression

• Model assumptions


• Parameter estimation and interpretation


• Missing values and imputation

• One-, two-way ANOVA, and multiple comparison


• Contingency table analysis

• Chi-square test


• Loglinear model

• Classification

• Training and test datasets, leave-one-out and cross-validation


• Precision, recall, accuracy, ROC curves, confusion matrix


• Logistic regression for classification


• Linear discriminant analysis

• Cluster analysis

• similarity measures


• hierarchical clustering


• k-means clustering

Teaching in the School will be delivered using a range of face to face lectures, classes and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.