# Statistics with R

The aim of this course is to teach you how to perform basic statistical analysis using R.
First we review the foundations (sampling theory, discrete and continuous distributions),
then we focus on classical hypothesis testing.
This course will improve your generic statistics knowledge.
**We cannot go into the specific data analysis problems of your particular project.**

**Instructor:** András Aszódi.

## Topics

This course teaches the same statistical concepts as the Basic statistics with Python training but uses the R programming language.

- Sampling theory: obtaining information about a population via sampling. Sample characteristics (location, dispersion, skewness), estimation of the mean, standard error of the mean.
- Discrete and continuous probability distributions. Central limit theorem.
- Hypothesis testing. Basic principles, one- and two-sided testing, types of errors, power calculations.
- "Cookbook of tests": location testing, normality, variance comparisons, counting statistics, contingency tables, regression tests.

## Out of scope

This course will not teach you bioinformatics.
In particular, *no high-throughput sequencing data will be used* because they are impractically large,
and not everyone on campus is working with sequencing.

If you are interested in the statistical background of gene expression analysis with high-throughput sequencing, then please take our RNA-Seq data analysis course.

## Prerequisites

Basic familiarity with R is required. In particular the following skills are necessary:

- Using the R interpreter, either the command-line program or in R Studio
- How to invoke R functions, pass optional/named parameters
- Some familiarity with simple plotting commands

If you have attended our R as a programming language training then you are well equipped to take this course.

## Practical information

**Number of participants:** minimum 5, maximum 10.

**Length:** The course takes two half-days,
from 09:00 to 13:00 with 2 breaks.