Lecture recordings

Recordings

Here are the lecture recordings, with the most recent appearing at the top. If the latest lecture is yet to appear here, you’ll likely be able to find it via the MA26620 Blackboard page > Books & Tools > All Panopto Videos.

Lecture 19: 2025-03-25

  • Two-way ANOVA.

  • Interaction plots

Lecture 18: 2025-03-18

  • Two-way ANOVA.

  • Decomposition of corrected sums of squares for the two-way case.

  • The table.

Lecture 17: 2025-03-11

  • Two-way ANOVA model.

  • Estimating parameters.

Lecture 16: 2025-03-04

  • Contrasts

Lecture 15: 2025-02-25

  • Expectations of sums of squares.

  • (videos 2 and 3 are from the prac where we began with a mini-lecture on the ANOVA table)

Lecture 14: 2025-02-18

  • The One-way ANOVA model.

  • Relationships between sums of squares.

Lecture 13: 2025-02-11

  • The chi-squared test.

  • Introduction to One-Way ANOVA.

Lecture 12: 2025-02-04

  • Poisson hypothesis tests - example.

  • The Normal approximation to the Poisson distribution.

  • Poisson confidence intervals.

Lecture 11: 2025-01-28

  • No lecture recording available - technology failed and we used the whiteboard.

  • Binomial confidence intervals.

  • The Poisson distribution.

  • Poisson hypothesis tests.

Lecture 10: 2024-12-11

  • Binomial hypothesis tests.

  • The Normal and Poisson approximations to the Binomial.

Lecture 09: 2024-12-04

  • Two-sample t confidence intervals (example).

  • The F-test for equal variance.

  • Binomial revision.

  • Binomial hypothesis tests.

Lecture 08: 2024-11-27

Lecture 07: 2024-11-20

  • Two sample t-tests.

  • Calculating estimated standard errors (generally).

  • The unequal variances case.

  • The equal variances case.

Report writing exercise: 2024-11-13

  • Report writing exercise.

  • No recording is available for this session due to its discursive and group-work nature.

Lecture 06: 2024-11-06

  • t confidence intervals.

  • The t-test.

Lecture 05: 2024-10-30

  • Q-Q plots.

  • Standardising the sample mean with unknown variance: the \(T\)-statistic.

  • Confidence intervals.

Lecture 04: 2024-10-23

  • The Central Limit Theorem.

  • Deriving the distribution of \(\bar X\).

  • Unknown variance.

Lecture 03: 2024-10-16

  • More on the correlation coefficient, \(R\).

  • The linear regression model.

  • The residual sum of squares and minimising it.

  • Deriving the least squares regression estimators \(\hat\beta_0\) and \(\hat\beta_1\).

Lecture 02: 2024-10-09

  • Summary statistics

  • Boxplots a.k.a. box and whisker plots

  • Scatterplots

  • The correlation coefficient, \(R\).

Slides from first half of lecture [PPTX]

Lecture 01: 2024-10-02

  • Module introduction

  • Classification of data: qualitative vs quantitative and discrete vs continuous

  • Presenting and summarising discrete and continuous data