- Lecturer: Katia Babbar
- Lecturer: Anran Hu
General Prerequisites:
The course assumes a good undergraduate level understanding of statistics, especially basic characteristics of key univariate distributions, statistical estimators (Least-Square, Method of Moments, Maximum Likelihood Estimators), confidence interval, hypothesis testing, normality tests, F-Tests. Students should also familiarized themselves with Python as a programming language, aiming to understand plotting techniques to apply Exploratory Data Analysis.
Course Term: Michaelmas
Course Lecture Information: 6 hours lectures
Course Overview:
This introductory course will review Linear Regression in detail, both the univariate and multivariate case. Linear regression is a fundamental statistical learning method and many other sophisticated learning approaches that students will encounter later on course, can be viewed as generalizations of Linear Regression. The emphasis will be on distilling the underlying assumptions of the model and interpretation of outcomes. Examples will be explored using Python, introducing Financial data and drawing attention to statistical concepts which are a assumed to be pre-requisite as described below.
Learning Outcomes:
Course Synopsis: