Week 6
Introduction
This week will introduce linear regression. The most useful and widely used model in all of statistics. Regression underpins more ‘advanced’ methods like machine learning, but for most situations, it is likely to be the only model you will require.
Regression is also one of the most misused and misunderstood models in the spatial data scientist’s toolbox. Therefore, understanding the basics is absolutely fundamental. If you understand the basics, then you stand a better chance of understanding what more sophisticated methods bring to the table.
Getting a regression model right is no more challenging than following a baking recipe - almost anyone can follow the instructions. However, like baking an amazing cake, skill, experience and understanding gained through hours of experimentation, failures, the odd amazing success and lots of perseverance are what make the difference between a great model and a soggy bottom!
Learning Objectives
By the end of this week, you will be able to:
- XXX
- XXX
- XXX
Lecture
To access the lecture notes: Lecture
Quiz
To access the quiz on Moodle, please check Moodle page.
Practical
To save a copy of notebook to your own GitHub Repo: follow the GitHub link, click on Raw and then Save File As... to save it to your own computer. Make sure to change the extension from .ipynb.txt (which will probably be the default) to .ipynbbefore adding the file to your GitHub repository.
To access the practical: