STAT10060 Statistical Modelling
This course is an introduction to some of the basic statistical methods and tools used for analyzing data and make decisions. Core concepts will be presented using practical data examples, focusing on applications and critical evaluation. The module will introduce these methods also using the statistical software R.
Main topics covered:
- Introduction and basics of inference.
- Comparing two populations: Confidence intervals and hypothesis testing for the difference between population means and population proportions - Independent and paired samples - Sample size calculations.
- Analysis of categorical data: One and two-way frequency tables - Hypothesis testing - Test for association.
- Simple linear regression: Correlation - Regression line - Checking model adequacy - Using the model for inference and prediction.
- Analysis of variance: One-way ANOVA - Multiple comparisons - Randomized block experiments - Two-way ANOVA.
Show More
REVIEWSMODULE INFO
Not worth the stress
Too much work for an elective, coding exam worth 20% also only pick it if you’re good at maths and stats otherwise stay away unless it’s a core
Looks good on a transcript, slightly heavy workload, terrible teacher. But if you apply yourself you’ll pass
It's a good elective to do as it looks good on transcript and doesn't take much work as long as you go to all the labs.
I did find I learned a lot. An interest in stats is required though to really enjoy the module.
I would recommend this module to anyone who has an interest in physics! While not a core, for us in physics statistics is becoming a huge part of the physical research of today and this module is a good foundation to build any relevant skills upon
if you wanna do ACM or maths/TP just do it, it's worthwhile.
REVIEWSMODULE INFO
Subject: Statistics & Actuarial Science
Level: 1
Module Coordinator: Dr Silvia D'Angelo
Trimester: Spring
Credits: 5
Module Info
Subject: Statistics & Actuarial Science
Level: 1
Module Coordinator: Dr Silvia D'Angelo
Trimester: Spring
Credits: 5