Applied Statistics (Spring 2024)

Abstract

The course introduces the students to probability theory and applied statistics. It will focus on understanding the theoretical foundations of statistics and on applying the theory using mathematical analysis and simulations in R.

Description

The course intends to give the student tools to identify and solve statistical problems in practice, occurring in data-analysis.

The subjects covered in the course include: probability spaces, random variables, conditional and joint probability, independence, expectation, variance, correlation and covariance, simulation of random variables, law of large numbers, central limit theorem, explorative data analysis, statistical models, bootstrapping, maximum likelihood estimation, confidence intervals, hypothesis testing.

Formal prerequisites

The course is mandatory for second semester BSc in Data Science students and requires basics in programming and mathematics. 

Intended learning outcomes

After the course, the student should be able to:

• Apply fundamental definitions and theorems from probability theory and statistics
• Perform basic computations on random variables and simulate random variables using R
• Perform basic statistical modelling and inference (estimation and hypothesis testing) using mathematical analysis and in R
• Analyse sampling distribution of estimators using both mathematical tools and simulation (bootstrapping) with R
• Present a statistical analysis in a clear way that allows the reader to understand the conclusions and the assumptions they are based on
• Do basic programming and data manipulation in R
• Identify statistical problems in a given data analysis

Ordinary exam

Exam type:
A: Written exam on premises, External (7-point scale)
Exam variation:
A22: Written exam on premises with restrictions.