course-details-portlet

ISTT1002 - Statistics

About

Examination arrangement

Examination arrangement: Digital exam and work
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Work 30/100
Digital school exam 70/100 3 hours C

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): Measurement systems and uncertainty in measurements. Repeatability and reproducibility. Error propagation, sensitivity analysis and calibration.

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 his/her 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 knows about models and methods for handling uncertainty in measurements and is familiar with concepts such as repeatability and reproducibility in measurements.

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
  • deal with measurements from measurement systems with several input variables, calibrate a simple instrument and read calibration curves, present an uncertainty budget for the measurement system, perform calculations related to error propagation and present the measurement results

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, measurement uncertainty and sensitivity in measurements systems 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

Lectures, 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.

Required previous knowledge

Knowledge in mathematics on the level of R1 and R2 in high school, from R2 in particular understanding and competence required to calculate simple integrals.

Course materials

Gunnar Løvås: Statistikk for universiteter og høgskoler. Helga Jonsdottir: Usikkerhet og støy i målinger. Thematic videos.

Credit reductions

Course code Reduction From To
ISTA1001 5.0 AUTUMN 2020
ISTA1002 7.5 AUTUMN 2020
ISTA1003 5.0 AUTUMN 2020
ISTG1001 5.0 AUTUMN 2020
ISTG1002 7.5 AUTUMN 2020
ISTG1003 5.0 AUTUMN 2020
ISTT1001 5.0 AUTUMN 2020
TALM1005 5.0 AUTUMN 2020
TDAT2001 5.0 AUTUMN 2020
IE203312 5.0 AUTUMN 2020
IR201812 5.0 AUTUMN 2020
IR102712 4.0 AUTUMN 2020
SMF2251 5.0 AUTUMN 2020
ISTT1003 5.0 AUTUMN 2020
VB6200 5.0 AUTUMN 2021
ST0103 5.0 AUTUMN 2024
ST1101 2.5 AUTUMN 2024
ST1201 2.5 AUTUMN 2024
TMA4240 5.0 AUTUMN 2024
TMA4245 5.0 AUTUMN 2024
More on the course

Facts

Version: 1
Credits:  7.5 SP
Study level: Foundation courses, level I

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
  • Statistics
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Mathematical Sciences

Timetable

List view
Calendar view

Examination

Examination arrangement: Digital exam and work

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Digital school exam 70/100 C 2024-12-19 09:00 INSPERA
Room Building Number of candidates
SL111 lyseblå sone Sluppenvegen 14 72
SL111 grønn sone Sluppenvegen 14 50
SL111 brun sone Sluppenvegen 14 81
SL210 Sluppenvegen 14 51
SL111 blå sone Sluppenvegen 14 36
Autumn ORD Work 30/100

Submission
2024-11-18


12:00

INSPERA
Room Building Number of candidates
Summer UTS Digital school exam 70/100 C INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU