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 11

Summary

  • Poisson approximation to the Binomial

  • Confidence intervals for \(p\)

Lecture 10

Summary

  • Discrete inference

  • Binomial hypothesis tests

  • Normal approximation to the Binomial

Lecture 09

Summary

  • Two sample \(t\)-test example

  • Two sample \(t\) confidence interval example

Lecture 08

Summary

  • Two sample \(t\)-test

  • Welch-Aspin approximation

  • Equal variances assumption

Lecture 07

Summary

  • One sample \(t\)-test

Lecture 06

Summary

  • \(t\) confidence intervals

Lecture 05

Summary

  • Central limit theorem

  • Distribution of the sample mean (\(\bar X \sim N(\mu,\sigma^2/n)\))

  • Unknown variance

  • The \(t\) distribution

Lecture 04

Summary

  • Deriving the equation of the least squares regression line

  • Normal distribution recap

Lecture 03

Summary

  • The correlation coefficient \(R\)

  • 100\(R^2\)

  • The linear regression model

  • Estimating \(\hat\beta_0\) and \(\hat\beta_1\)

Lecture 02

Summary

  • Continuous data

  • Summary statistics

  • Measures of central tendency (slides)

  • Measures of spread

  • Boxplots (aka box and whisker plots)

  • Scatterplots

  • The correlation coefficient \(R\)

Lecture 01

Summary

  • How the module works

  • Classification of data

  • Discrete vs Continuous

  • Quantitative vs Qualitative

  • Barcharts