PhD Diary, Week 9: Always check your code!
My ninth week, losing time not checking my code is delivering what it should do.
All posts
Admittedly, this is a bit messy. I am aiming to fix this once I establish sensible grouping of posts within the site. Thanks for your patience!
- Dan
PhD Diary, Week 8: Arranging notes appropriately
My eigth week, reflecting on the benefits of arranging notes with pedagogy in mind.
3.4 `T`-Optimality Introduction
Exploring `T`-optimality and how it tells whether we have a solid experiment.
3.3 `E`-Optimality Introduction
Exploring `E`-optimality and how it explains a solid experiment.
3.1 `D`-Optimality Introduction
Exploring `D`-optimality and how it dictates a solid experiment.
3.2 `A`-Optimality Introduction
Exploring `A`-optimality and how it asserts a solid experiment.
3.0 What is experimental design optimality?
We shall introduce the concept of optimality in the context of experimental design and discuss its importance.
3.5 `G`-Optimality Introduction
Exploring `G`-optimality and how it governs a solid experiment.
1.3 Determining the Precision of Estimating `β_j`
We shall introduce how we determine how precise our estimators are.
1.2 Introduction to non-linear polynomial regression
Extending our understanding of linear regression to the quadratic case.
1.0 Simple Linear Regression
An introduction to simple linear regression; an approach to model a relationship between values of an independent variable and values of a dependent variable.
1.1 Estimating `β_0` and `β_1` using least squares estimation
We shall introduce least squares estimation as a means to estimate `β_0` and `β_1`.
1.0 Introduction to Probability in Fair Experiments
An introduction to probability. This is a core prerequisite to be able to understand statistical theory.
PhD Diary, Week 7: Buying books if not booking books
My seventh week, finding sites for finding books which are not available at your institution's library.
PhD Diary, Week 6: Code it to know it
My sixth week, recognising the importance of coding when trying to learn mathematics,
1.3 Node degree
Defining the degree of a node.
1.4 Node degree distribution
The probability distribution of choosing a node with degree k out of all possible nodes.
1.0 What are networks? (nodes and edges)
An introduction to the basics of networks and graph theory.
1.2 Incidence matrix
A definition of the incidence matrix, complete with an example.
1.1 Adjacency matrix
A definition of the adjacency matrix, illustrated with examples.
PhD Diary, Week 5: Setting the scene
My fifth week, forcing myself to provide context around what I am actually doing.
PhD Diary, Week 4: First Reflections Using the 3-D Tool
My fourth week, reflecting on current progress.
PhD Diary, Week 3: Working with a Horribly Tricky Markup Language
My third week, creating the home page to my site, and describing the benefits of synthesising sources.
PhD Diary, Week 2: I love experimental design!
My second week, realising how much I enjoy reading about the architecture of a solid experiment.
PhD Diary, Week 1: First things first, make a timetable
My first week, revisiting some time management resources
Randomly Walking in Python, Stylishly
A quick guide on plotting random walks in Python and how to make your plots nice in Matplotlib for beginners.
A Few Minutes on the Pesticide Paradox
A brief look at my first MSc presentation assignment and the pesticide paradox.
