Course content

Basic part (5 credits): Descriptive statistics. Probability of events, combinatorics and conditional probability. Stochastic variables, expectation and variance. Covariance, correlation and independence. Common probability distributions (e.g., binomial, poisson, exponential and normal distribution). The central limit theorem. Parameter estimation and confidence intervals. One-sample hypothesis tests. Simple linear regression.

Special part (2.5 credits): Experimental design: Two-factor experimental design, block pairing, analysis of methods for repeated and non-repeated experiments. Statistical quality control: Sources of variation, random sampling, control charts for expected values, standard deviations and count data, and capability indexes.

Learning outcome

Knowledge

The candidate is familiar with the basic ideas in probability and statistics. The candidate has knowledge about simple statistical models and processes that are often used within their field of study. The candidate knows how to use statistics in a comprehensive way and understands that statistics is a necessary tool for measuring, describing and evaluating results. The student also knows how to use basic statistical inference methods to describe processes and populations based on independent trials and random samples. The candidate has knowledge of experimental design and statistical process control.​

Skills

The candidate can

• Present and describe the characteristics of a data material using descriptive statistics, tables and figures
• Calculate the probability of events and conditional probabilities, using e.g. combinatorics, stochastic variables, the most common probability distributions (e.g., binomial, poisson, exponential and normal distribution) and the central limit theorem.
• Perform simple methods for statistical inference such as parameter estimation, confidence intervals, one-sample hypothesis tests, correlation and simple linear regression
• apply statistical principles and concepts in his/hers professional field
• use Python, or a similar statistical software, to perform basic statistical analysis
• interpret data material and results from statistical analysis related to experimental and statistical process control

General competence

The candidate sees the importance of statistical knowledge and expertise in the engineering role and is able to communicate with professionals about engineering problems by using statistical concepts and expressions. The candidate has gained confidence in simple statistical analysis, two-factor experiments and statistical process control through student activities such as exercises and project work. This competence provides a platform for further engineering studies, and for various types of applications in industry, consulting and the public sector.

Learning methods and activities

Online lectures, thematic videos, gatherings, collaborative project work and exercises.

Compulsory assignments

• Exercises

Further on evaluation

The course has two evaluations with character grades; a collaborative project and an individual exam. In order to pass the course, both evaluations must be passed.

A continuation exam is held in August for the written school exam, this may be change to an oral exam if there are few students. There is no continuation exam for the project, so the project must be re-taken when the course is given ordinarily.

Students that want to improve their grade in the course, can choose to retake one of the two evaluations.

If the evaluation is changed, the whole course must be retaken.

Specific conditions

Recommended previous knowledge

Mathematical Methods 1

Introduction to the engineering profession

Required previous knowledge

Kunnskap i matematikk tilsvarende R1 og R2 i videregående skole, fra R2 spesielt forståelse og kompetanse kom kreves for å regne på enkle integraler.

Course materials

Gunnar Løvås: Statistikk for universiteter og høgskoler. Thematics videoes. Compendium in experimental design.

Credit reductions