# 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 2022-2023

Facts

STA100

1

10

Spring

1

Spring

Norwegian

Time table

## Content

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 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, linear regression, analysis of variance and chi squre 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.

None

## Recommended prerequisites

MAT100 Mathematical Methods 1

## Coursework requirements

Six compulsory assignments
Compulsory exercises have to be approved in order to take an examination.

Jan Terje Kvaløy

Jan Terje Kvaløy

## Method of work

Four to six hours of lectures, two hours of problem solving and four to eight hours of self study per week. Mandatory work demands (such as hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher three weeks ahead of examination date.

## Open for

Biological Chemistry - Biotechnology - Bachelor's Degree Programme Civil Engineering, Bachelor in Engineering Computer Science - Bachelor in Engineering Control Engineering and Circuit Design - Bachelor in Engineering Chemistry and Environmental Engineering - Bachelor in Engineering Mechanical Engineering, Bachelor in Engineering Mathematics and Physics - Bachelor's Degree Programme Geosciences Engineering - Bachelor in Engineering Petroleum Technology - 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 Industrial Automation and Signal Processing - Master's Degree Programme - 5 year Robot Technology and Signal Processing - Master's Degree Programme 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 Offshore Technology - Master's Degree Programme Petroleum Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme, Five Years Technical Societal Safety, Master of Science Degree Programme Mathematics - One-Year Programme

## Course assessment

Form and/or discussion

## Literature

The syllabus can be found in Leganto