The course gives an introduction to basic probability theory, including an introduction to common discrete and continuous probability models, and an introduction to simulation. Further the course gives an introduction to descriptive statistics and statistical analyses, in particular estimation and confidence intervals, hypothesis testing and regression analysis. An integrated part of the course is an introduction to R for programming, data analysis and simulation.
Course description for study year 2024-2025. Please note that changes may occur.
The course gives an introduction to descriptive statistics and basic probability theory for discrete and continuous probability models. Introductory theory for estimation and for statistical hypothesis testing in the most common situations is presented. Emphasis is made on both theoretical understanding and applications. An introduction to simulation is also given. Use of the software R for data-analysis and modelling is an integrated part of the course.
Topics covered: Introduction to basic probability theory, included conditional probability, expectation, variance and the most common probability distributions like binomial, hypergeometric, poisson, exponential and normal. Introduction to simulation. An introduction to point estimation, confidence intervals and hypothesis testing in one and two sample situations. An introduction to correlation, regression analysis, analysis of variance and chi-squared tests.
After having completed the course the student should:
Be able to use basic methods for analysis and presentation of data.
Be able to do basic probability calculations.
Know what a random variable, probability distribution, expectation and variance is.
Be able to calculate expectation, variance and probabilities for random variables and simple functions of random variables.
Be able to use basic probability distributions like binomial, poission, hypergeometric, exponential and normal.
Be able to use the central limit theorem.
Be able to find estimators and calculate confidence intervals for some important parameters in probability distributions.
Have a basic understanding of hypothesis testing and be able to perform hypothesis testing for one and several samples.
Know the theory for, and be able to use correlation, regression analysis and simple analysis of variance.
Know the assumptions for the various methods and be able to judge whether the assumptions are fulfilled.
Be able to use chi square tests
Be able to use some R for basic data analysis and simulation.
Required prerequisite knowledge
MAT100 Mathematical Methods 1
Form of assessment
Written exam is with pen and paper
Six compulsory assignments
Compulsory exercises have to be approved in order to take an examination.
Mandatory work demands (such as hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher three weeks ahead of examination date.
There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.