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).
• Discrete and continuous probability distributions. Central limit theorem.
• The distribution of the sample mean. Confidence intervals.
• Basic principles of hypothesis testing. "Student"'s t-test.
• Type I and Type II errors. P-value distributions. Power calculations.
• "Cookbook of tests": distribution tests, parametric and non-parametric tests, counting statistics, contingency tables, correlation tests.

Exercises

Online exercises are available when this course is running. Please select the option "R stats" from the dropdown in the "Request an exercise notebook" form.

Out of scope

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