Introduction to Applied Statistics, Part II

Statistics is a must-needed set of tools in experimental sciences. In almost every study, methods from statistics are necessary and their justification is required for a good level publication. Despite the availability of a number of powerful and sophisticated software, it is not always obvious what analysis approach is needed in any particular case if one does not have an introductory level background in the matter.

 

This course builds on the previous course (Part I) but people with some knowledge of statistics can still take this course without having attended Part I.

 

This course will start with a summary of probability theory for bivariate distributions. We will explore maximum likelihood methods. Move on into simple linear regression, ANOVA and tests for contingency tables. If time will allow it, we will explore a few numerical approaches like permutation tests and bootstrapping.

 

The course won't refer to any particular software but people used to work with any given software are welcome to use it in order to solve the exercises. Most exercises and solutions provided with this course are written in python and will be available as well together with the lecture notes.

 

Everyone interested is welcome to join.

This course will be delivered live, online only.

The first lecture will take place on May 5, 2021.

Each lecture will be 1.5hrs with little breaks.