Lecture Topics

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Lecture Topics


1x 1 hour lecture per week

Lectures will provide the main overview of key content areas for the course, with a focus on conceptual and theoretical understanding. In lectures we will attempt to explain content making use of written explanation, worked examples and graphically. Worked examples will use both simple hand calculation, and R for larger real world examples. Students will have access to data and code from the lectures, so that they can repeat analyses for revision and to aid understanding.

We want to strongly encourage you to ask questions in lectures. If you are thinking it, it is fairly certain a number of your class mates will be as well. Help yourself and one another learn by asking the questions! If you are not comfortable asking questions in lectures (we do understand it is not always easy), please make use of the lecturer office hours and come and ask your question in person.

Lectures on this course will be recorded, however please note that the lecturers may make use of black/white boards in the lecture rooms when working through examples and answering questions. As such, please do not use lecture recordings as a replacement for attending. They are intended to use as a revision tool and for situations where occasional lectures are missed due to illness and other circumstances.

Day, time & Location

Semester 1: Monday, 14:10-15:00, Lecture Theatre 5 Appleton Tower.

Semester 2: Monday, 14:10-15:00, Lecture Theatre B, David Hume Tower LTs

Lecture Topics

Semester Week Topic
1 1 Introduction lecture: Research process, planning and design
  2 Measurement: types of data
  3 Organising data: data sets, tables plots
  4 Describing data: Central tendency
  5 Describing data: Variability
  6 No lecture
  7 Functions & Data transformation
  8 Statistical models, chance and probability
  9 Fundamentals of Probability
  10 Probability & Probability distributions
  11 Probability distributions: Binomial & Normal
2 1 Sampling distributions
  2 Point estimates and confidence intervals
  3 Fundamentals of hypothesis testing
  4 Power
  5 Bootstrapping
  6 No Lecture
  7 One sample & paired t-tests
  8 Independent sample t-tests
  9 Covariance and correlation
  10 Chi-square test of independence
  11 Applications of bootstrapping and power analysis