Course overview
This course provides an introduction to statistical theory, application and communication. Topics covered will include: probability, random variables and distributions; inference, including hypothesis testing for means and proportions; and linear regression including diagnostics, model checking, and multiple linear regression with continuous predictors, factors, and interaction terms. In this course students will develop their: mathematical understanding of these topics; their ability to implement their ideas in the statistical software R; and their skill in communicating their results to both statistical and non-specialist audiences. This course encourages the development of skills for all mathematicians across a broad range of program outcomes including communication, professional practice, data literacy, ethics and integrity.
- Probability
- Inference
- Regression
Course learning outcomes
- Apply laws of probability and properties of random variables, including expected value and variance of linear combinations of random variables to a variety of problems
- Choose and perform appropriate hypothesis tests in a variety of scenarios including one sample and two sample tests for means and proportions
- Evaluate the assumptions of linear regression models fit to data and interpret the outputs from such a model fit including coefficients, measures of model-fit, and predictions
- Describe data and data analysis to both statistical and generalist audiences
- Analyse data using the statistical software R including fitting linear regression models to data, making predictions using such models, computing probabilities, applying hypothesis tests and producing graphics to visualise and investigate data