The course is the second part of the module Probabilities and Statistics of the international Computer-Science Master (M1 IIT) program. The course mainly targets students and researchers who are interested in experimental research methods and often need to deal with small samples and messy data. Previous knowledge of statistics or probability theory is not required, but some basic understanding of probabilities could help.
The course will introduce fundamental concepts of descriptive and inferential statistics. The goal of the course is NOT to provide a set of statistical recipes or step-by-step instructions. Particular focus will be given on understanding key principles, thinking about underlying assumptions, and recognizing the limitations of statistical methods.
The students will also learn how to use the R statistical software to analyze real datasets and how to apply computational methods to estimate parameters or evaluate statistical procedures.
Classes are given by Theophanis Tsandilas.
I have posted a set of home exercises to help you prepare for the final exam.
The assignment is out!
Nov 30. Discrete and continuous probability distributions: binomial, normal, log-normal, chi-square and t-distribution.
The sampling distribution of a statistic. The Central Limit Theorem. A brief introduction to confidence intervals.
[Lecture 2: Slides] [Lecture 2: R code]
Jan 18. Preparation for the final exam.
[Lecture 7: Home exercises]
Part of the course content has been inspired by Thom Baguley's book:
Buying the book for the purposes of the course is not required, but I recommend it to students who are interested in deepening their understanding of statistics and would like to have a reference for their data analyses. There are many other textbooks on statistics, but unfortunately, I cannot express a personal opinion on their content or teaching approach.
During the class, I will add links to various online readings to help students better understand the course material.