Think Statistics with R

The main aim of this course is to teach you to how to approach data analysis problems
with classical statistics. We focus on the intuition behind statistical methodologies
rather than on “how to run a t-test with R” (which we will also learn, by the way).

First we review the foundations (sampling theory, discrete and continuous distributions),
then we continue with hypothesis testing. The technology itself is introduced using “Student”‘s t-test
as an example, with a strong emphasis on errors (false positives, p-value distributions,
test power calculations). Finally a short “cookbook of tests” is offered.

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.
• Type I and Type II errors. P-value distributions. Power calculations.
• “Cookbook of tests”: location testing, normality, variance comparisons,
  counting statistics, contingency tables, regression tests.

Out of scope

We cannot go into the specific data analysis problems of your particular project.