Probability and Statistics 1 (STA100)

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.


Course code




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Semester tution start


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Exam semester


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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.

Learning outcome

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


Recommended prerequisites

MAT100 Mathematical Methods 1


Form of assessment Weight Duration Marks Aid
Written exam 1/1 4 Hours Letter grades

Written exam is with pen and paper

Coursework requirements

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.

Course teacher(s)

Course coordinator:

Jan Terje Kvaløy

Head of Department:

Bjørn Henrik Auestad

Method of work

To to six hours of lectures, two hours of problem solving and four to eight hours of self study per week.

Overlapping courses

Course Reduction (SP)
Statistics and social science methodology (BØK104_1) 8
Introduction to probability and statistics 1 (BMF100_1) 10
Introduction to probability and statistics (TE0199_2) 6
Introduction to Probability and Statistics (ÅMA110_1) 5
Statistical inference 1 (MOT310_1) 5
Statistics for economists (ØK0061_1) 4
Mathematics and statistics (BØK160_1) 4
Introduction to probability and statistics (TE0199_1) 5
Introduction to probability and statistics (TE0199_A) 6
Statistics (BØK145_1) 5

Open for

Biological Chemistry - Biotechnology - Bachelor's Degree Programme Civil Engineering - Bachelor in Engineering Computer Science - Bachelor in Engineering Electrical Engineering - Bachelor in Engineering Energy and Petroleum Engineering, Vocational Path - Bachelor in Engineering Environmental Engineering - Bachelor in Engineering Mechanical Engineering - Bachelor in Engineering
Admission to Single Courses at the Faculty of Science and Technology
City and Regional Planning - Master of Science Degree Programme, Five Years Environmental Engineering - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme, Five Year Structural and Mechanical Engineering - Master of Science Degree Programme. Five Years Mathematics and Physics - Five Year Integrated Master's Degree Programme Marine and Subsea Technology, Master of Science Degree Programme, Five Years Petroleum Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme, Five Years Mathematics - One-Year Programme

Course assessment

There must be an early dialogue between the course supervisor, the student union 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 subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


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