Important Notes - please read
1. Remote course
The course will be delivered remotely using online teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the practical periods.
2. Participants must have basic R skills prior to course
This is not an introductory course in using the statistical software package, R. Basic R coding will not be taught. To do this course successfully, you must have basic proficiency in using the R package. All examples and exercises used in this course are done using R. We want to ensure everyone is able to follow the course, and no participant is disappointed.
If you have basic R skills, that's excellent, please complete this quick task HERE. Once you have completed this and emailed your results you will be given a code to allow you to register for Introductory Statistics for Researchers.
If you do not have basic R skills, but want to do the Introductory Statistics for Researchers course, you can enrol in our Introduction to R course that runs twice ahead of this course, August 10-11 (course 1) and 17-8 (course 2), register HERE. Please email email@example.com once you have registered, and you will be given a code to allow you to register for Introductory Statistics for Researchers.
This course is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher. The theory behind the statistical procedures outlined in the course will, in general, not be discussed.
A range of statistical analyses will be discussed in the course, as described in the course outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in R. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work in the course.
- Study design - experimental and observational studies, sample size
- Descriptive statistics – mean, mode, standard deviation, inter-quartile range, correlation
- Data visualisation - boxplot, histogram, scatterplot
Introduction to statistical inference
- Uncertainty, p-values, significance/evidence
- T-test (comparing two groups)
- Checking model assumptions
Analysis of continuous responses with linear models
- Linear regression
- ANOVA / ANCOVA
Analysis of categorical responses
- Chi-square tests
- Logistic regression
Course Requirement: You will need to use a computer during the course. You will also need administrator rights to install software needed for the course onto your computer. Basic proficiency in using the R statistical package is essential.
Date: Monday 23 to Thursday 26 2021
Duration: 9.00am to 1.00pm each day
You will receive a certificate of completion for the course.