Summary:

This module provides an introduction to traditional and modern construction materials. Materials are examined from the perspective of their...

Materials: Their Use and Structural Function (BCPM0062)

Applied Statistics|Computational Statistics|Interdisciplinary Studies|Interdisciplinary Thinking|Quantitative Analysis

quantitative methods|regression analysis|prediction and causality|R|Python|

Regression analysis, one of the most important quantitative methods, has interrogated countless empirical and interdisciplinary questions using small- and...

Quantitative Methods 3: Models and Inference (BASC0056)

Regression analysis, one of the most important quantitative methods, has interrogated countless empirical and interdisciplinary questions using small- and large-scale datasets. This module will explore the development of regression models and contextualise the methodological development in their social and cultural context, using applications across a wide range of disciplines such as astronomy, biology, geography, social science, epidemiology, environmental science, artificial intelligence, and so on. 

This module will start with general linear models, generalised linear models and then explore contemporary models such as generalised additive models and supervised machine learning. A series of real-world applications discussed in this module will enable students to understand the fundamentals of regression models; gain knowledge on selecting appropriate models and interpreting regression outputs. This module will discuss some key concepts and debates about prediction and causality. Examples of applications from various disciplines will be used to illustrate two different purposes of regression analysis: predictive and causal inference. This module combines lectures, seminars, group work and hands-on practicals using R and Python. Students will gain skills to implement various regression models and draw conclusions from the results.
 

In this module students engage with the selected bibliographic references that underpin bio- integrated design, while being taught basic research methodologies...