Statistics and Data Analysis module (MA52008)

Statistics is the science of collecting, analysing, and interpreting data. It is used in areas such as science, engineering, medicine, and economics.

This module focuses on giving you the skills to make inferences using sample data. That means using information from a sample of a population to estimate properties of the whole population. You will also learn how to use the software package RStudio and the R programming language to carry out statistical calculations and work with real data.

You will also develop advanced tools for analysing complex data sets. This will improve your ability to design experiments, interpret results, and conduct high-quality research. These skills are widely valued in industries such as data analysis and market research, and will strengthen your understanding of data-driven decision-making and problem-solving.

What you will learn

In this module, you will:

• learn the theory of sampling distributions and the inferences you can draw from them
• learn the mechanisms of hypothesis tests and how to calculate confidence intervals
• learn how to use 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 population mean
• recall a selection of distributions and tests that are relevant to making inferences
• test whether different populations are independent based on sample data
• model a linear relationship between two variables based on a sample
• judge 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

• Lectures
  • 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
• Tutorials
  • Two one-hour sessions weekly
  • Solve problems individually and in groups
  • Support with difficulties provided by lecturers and peers
• Workshop
  • One Data Collection and Analysis workshop, often held at the University Botanic Gardens