Statistics and Data Analysis module (MA22004)

Statistics is the science of collecting, analysing, and interpreting data. It is used to draw conclusions from data. This module extends some of the concepts introduced in the Level 1 statistics module. There are applications of statistics in science, engineering, medicine, and economics.

In particular this module focuses on providing you with the skills required to make inferences using sample data. That is, to be able to use data from a sample of a population to deduce information about the average properties of the whole population. You will also learn how to use the software package RStudio and underlying computer language R to do calculations with data.

Studying statistics is beneficial for several reasons. It equips you with advanced tools to analyse complex data sets, enhancing your analytical skills. This is essential for conducting high-quality research. It helps in designing experiments and interpreting results accurately.

Skills in statistics are highly valued in many industries. It can lead to career opportunities in data analysis, market research, and more. It fosters a deeper understanding of data-driven decision-making, enhancing your problem-solving abilities.

What you will learn

In this module, you will:

• learn the theory of sampling distributions and the appropriate inferences you can draw from these
• learn the mechanisms of hypothesis tests and calculating confidence intervals
• learn appropriate use of the R software package to carry out calculations relevant to sampling

By the end of this module, you will be able to:

• clarify how the properties of a population relate to sample data
• use sample data to make point estimates of statistics such as the mean (average) of a population
• recall a selection of distributions and tests that are relevant to make inferences
• test whether different populations are independent based on sample data
• model a linear relationship between two variables based on a sample
• judge of the reliability of linear model predictions
• use the statistical software R to generate descriptive statistics and visualisations

Assignments / assessment

• coursework (40%)
• final exam (60%)

Teaching methods / timetable

• two one-hour lectures weekly
  • key points of the week's content will be discussed
  • lecture notes covering the full module content will be available before classes
  • in-class time will be prioritised for interactive discussion
• two one-hour tutorials weekly
  • solve problems individually and in groups
  • support with difficulties will be provided by your lecturers and peers

There will also be one Data Collection and Analysis workshop. In recent years this has been held at the University Botanic Gardens.