Statics & Data Science
Budget: £10 – £20 GBP
3000 words.
This Individual Coursework involves investigating the matrix operations behind correlation, covariance,
regression and principal component analysis. This involves the bivariate normal distribution, matrix
transformations and matrix decompositions. You are encouraged to explore the topic, use your
initiative, and show some originality, within the time available. Ensure that you clearly reference any
sources you have used.
Please submit one report (e.g. as a single Microsoft Word document) covering all of the tasks below,
clearly organised by subtask. Make sure you include your Python code and selected output directly in
the report. You must not submit a zip file. The word limit is a maximum rather than a target.
Concentrate on producing a clear and concise answer to each subtask.
Introduction
A square matrix ? is called orthogonal if ??? = ?. The columns of an orthogonal matrix ? (when
interpreted as vectors) are unit vectors (magnitude of 1) and every pair of columns is orthogonal
When an orthogonal matrix is used as a matrix transformation, it preserves both
magnitudes and angles. Common examples of orthogonal matrices are rotations and reflections
This Individual Coursework involves investigating the matrix operations behind correlation, covariance,
regression and principal component analysis. This involves the bivariate normal distribution, matrix
transformations and matrix decompositions. You are encouraged to explore the topic, use your
initiative, and show some originality, within the time available. Ensure that you clearly reference any
sources you have used.
Please submit one report (e.g. as a single Microsoft Word document) covering all of the tasks below,
clearly organised by subtask. Make sure you include your Python code and selected output directly in
the report. You must not submit a zip file. The word limit is a maximum rather than a target.
Concentrate on producing a clear and concise answer to each subtask.
Introduction
A square matrix ? is called orthogonal if ??? = ?. The columns of an orthogonal matrix ? (when
interpreted as vectors) are unit vectors (magnitude of 1) and every pair of columns is orthogonal
When an orthogonal matrix is used as a matrix transformation, it preserves both
magnitudes and angles. Common examples of orthogonal matrices are rotations and reflections