Basic Mathematical Statistics
Course description
As an engineer, you may come into contact with random phenomena, e.g. uncertainty of measurement data. In this course, you get an introduction to how mathematical models can be used to draw conclusions about collected data. In a small project, you get to analyze data in the energy field.
Mathematical statistics is sometimes called "the mathematics of chance" and knowledge in the field is important for an engineer. In the theory of probability you get an overview of the most common mathematical descriptions of random processes, while in statistical inference you encounter some common statistical methods for analyzing collected data, e.g. t-test and regression analysis.
In practical work with data, it is important to be able to choose an appropriate method for a given problem, and to be able to evaluate the results from a method and draw reasonable conclusions. In the course, you work in computer exercises with questions about data and towards the end apply your knowledge in a small project of your own, based on problems within energy systems.
Entry Requirements
Course facts
MS0073, Basic Mathematical Statistics, 5.0 Hp
Print syllabus
Syllabus
Level
First cycle (G1F)
Subject
Mathematical Statistics
Grading Scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Course language
Swedish
Entry Requirements
Objectives
The aim of the course is to give an introduction to probability theory and statistical concepts and to provide applied examples. After the course the student should be able to • apply basic probability concepts and statistical principles • formulate a given problem in statistical terms • describe the most common statistical methods including the conditions under which they can be applied • choose a suitable statistical method for a given problem • carry out a statistical analysis from the chosen method • interpret and evaluate results correctly and draw reasonable conclusions • use statistical software for descriptive statistics and simple modeling • carry out a small statistical group project
Content
The course is built on lectures, lessons, computer exercises and a small statistical project where data material is examined and regression analysis is carried out. Exercises are related to the field of energy, among others. The main moments are as follows: Descriptive statistics: Measures of mean and variation, different diagrams. Probability theory: Sample space, probability laws, random variables, probability distributions. Inference: Point estimation, confidence intervals, hypothesis testing, non-parametric methods. Regression and correlation. Practical statistical computing.
Examination Formats and Requirements for Passing the Course
Passed written and oral exercise. Passed written examination.
Responsible Department/Equivalent
Department of Energy and Technology
Supplementary information
Included in program
- Master Programme in Energy Systems Engineering
Module set
| Title | Credits | Code |
|---|---|---|
| Theory | 4.0 | 0001 |
| Project | 1.0 | 0002 |
The Course Replaces
MS0065