Graduate Skills

Webpage of the module 772213 T2 2025/26

General Information

Welcome to the module Graduate Skills 772213 for the MSc in Mathematics at the University of Hull, academic year 2025/26. In this course you will be assigned your MSc project and develop skills to help with it in T3.

You will

This course is taught by a combination on-campus sessions and learning material and activities organised within Canvas.Β 

Questions

If you have any questions please feel free to email me and please do not hesitate to attend office hours.

All the module information will be posted on this page. Teaching materials are posted on Canvas.

Lectures Calendar

We have two Lectures and one Tutorial per week:

Assessment

This module will be assessed as follows:

Type of Assessment Percentage of final grade
Oral Presentations 70%
Written Report 25%
Reflections on Presentations 5%
  1. Oral Presentations prepared and held by students:
    • I will help with the preparation of the presentations in the early stages of the module
    • The seminar atmosphere allows conceptual issues to be discussed and explored in depth in a supportive group setting
    • The order of presentations will be assigned randomly, with later presentations occurring in reverse order
    • All students will be offered an opportunity to give short practice presentations with feedback before marked presentations
    • Accommodation will be made for any student with special needs
  2. Written Report:
    • The written report will require to further investigate a mathematical, statistical, or numerical technique
    • A report has to be produced on the details of the method, investigating how and why the method works
    • The report length is 2 pages (excluding references) and has to be written using latex
    • I recommend choosing a topic related to your MSc Project, but this is not necessary
  3. Reflection on peer presentations:
    • This will be marked with a binary grading system
    • This outlines that the principle aim of the reflective process is to improve one’s own skills

Deadlines and submission are on Canvas

Lectures Diary

There are 24 lectures in this module. Links to the slides and lecture titles are below

Week of Slides Title
27 Jan Lecture 1 An introduction to Statistics
3 Feb Lecture 2 Random samples
10 Feb Lecture 3 The t-test & Introduction to R
17 Feb Lecture 4 The variance ratio & Two-samples t-test
24 Feb Lecture 5 The two-sample F-test & Goodness-of-fit test
3 Mar Lecture 6 Contingency tables & Simulation
10 Mar Lecture 7 Bootstrap & Least Squares
17 Mar Lecture 8 The maths of Regression
24 Mar Lecture 9 Practical regression
31 Mar Lecture 10 Model Selection & Regression Assumptions
7 Apr Lecture 11 Stepwise Regression & ANOVA
28 Apr Revision Week Coursework submission deadline
Extra Appendix A Probability revision
Extra Appendix B More on R

Statistical tables

R codes

Lecture Material
Lecture 3 One-Sample t-test
Lecture 4 Variance ratio test
Two-Sample t-test
Lecture 5 F-test
F-test First Principles
Goodness-of-fit
Goodness-of-fit First Principles
Lecture 6 Goodness-of-fit Contingency
Independence Test
Monte Carlo pi
Lecture 7 Bootstrap CI
Bootstrap t-test
Bootstrap F-test
2008 Crisis
Least-squares Solution 1
Least-squares Solution 2
Lecture 8 Multiple regression
R2 multiple regression
Lecture 9 Simple regression
Longley regression
Lecture 10 Longley selection
Galileo
Divorces
Heteroscedasticity
Autocorrelation
Lecture 11 Multicollinearity
Stepwise Regression: Longley
Stepwise Regression: Divorces
Anova
Ancova

Datasets

Homework

Homework papers must be submitted on Canvas by 14:00 on Thursday

Homework # Due date Topics
1 6 Feb Moment generating function. Poisson distribution. Poisson models for soccer.
2 13 Feb Bivariate transformations. Deriving the distribution of the t-statistic. Conditional expectation and variance.
3 20 Feb Vectors in R. The t-test: in R and by hand.
4 27 Feb Variance ratio test. Two-sample t-test.
5 6 Mar The two-sample F-test and t-tests.
6 13 Mar The goodness-of-fit test. The chi-squared test of independence / no association
7 20 Mar Bootstrap Confidence Intervals. Bootstrap t-test and F-test.
8 27 Mar Simple and general linear regression
9 3 Apr The t-test and F-test for regression
10 10 Apr Model Selection and testing Regression Assumptions

References

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