Introduction to Statistics
Once you’ve wrangled your data into form, it’s time to perform statistical tests or inference on these data. In this section, we’ll try and give pointers on not only just the components of how to perform those tests, but also a very very very VERY brief overview of some of the theory behind why we choose various approaches to statistical inference, and what sorts of assumptions we need to be conscious of when we set out to ask a biological question that we plan to answer by way of a statistical hypothesis test.
- IntroductionA primer to this section. READ ME FIRST.
- Probability 101What is probability? How should we think about it? When is it used?
- LikelihoodHow likely is something to happen? What does it mean when we estimate how likely a particular outcome is? Find out.
- Sampling DistributionsWhat is a sample? How should we think about randomness? What do we do with many samples?
- Hypothesis TestingWhat does it mean to test a hypothesis with a statistical test and how can we do so rigorously?
- Confidence IntervalsCalculating a range of values between which we can understand our estimate to fall
- Comparing Sample MeansOnce we’ve got our samples, and calculated our means, is the comparison so straight forward? Maybe, but maybe not!
- Analysis of VarianceWhat is variance, and how can we tell when the amount of variance between groups tells us something important?
- Linear RegressionFitting a line through some points and asking what even does that tell us anyways??
- Generalized Linear RegressionFitting a line through some points, but relaxing some assumptions
- Logistic RegressionStill fitting a line through some points and asking what it tells us, but this time, with special data!
- Linear Mixed EffectsTaking data hierarchy into account
About R Manual
Learn more about this project
The R Manual is a resource contributed to by several authors and led by Dr. Shelby Riskin, and Dr. Jacqueline Sztepanacz. This resource is not officially affiliated with the R Programming language, but aims to help students with the steep learning curve associated with learning R for the first time.
Funding
This project is made possible through the University of Toronto Learning & Education Advancement Fund (LEAF) program
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