ISTG1003 - Statistics


New from the academic year 2020/2021

Examination arrangement

Examination arrangement: Written examination and work
Grade: Letters

Evaluation Weighting Duration Grade deviation Examination aids
Home examination 80/100 3 hours
Work 20/100

Course content

Basic part (5 credits)
Descriptive statistics, Probability, Probability distributions, Estimation, Hypothesis testing, Correlation and Regression analysis
Special part (2.5 credits)
Multiple linear regression, Classification, Clustering.

Learning outcome

The student has a basic understanding of probability and statistics, and has gained confidence in handling, analysing and interpreting data 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. Knowledge, skills and general competence are detailed below.
The candidate
has knowledge of use of statistics in a comprehensive way, how statistics are a necessary tool for measuring, describing and evaluating results
has basic knowledge of descriptive statistics
has basic knowledge of probability theory and central probability distributions
has basic knowledge of estimation, confidence intervals and one-sample hypothesis testing
 has basic knowledge of correlation and simple linear regression
 knows how simulation can be used as a statistical tool in engineering analysis
 knows the most important statistical concepts and symbols
has knowledge of methods within statistical learning and data science
has basic knowledge of statistical computation and visualization tools on the computer

The candidate can 
use descriptive statistics, including histograms and box plots, in his/hers professional field 
perform (simple) probability calculations, including the use of Venn diagrams, conditional probabilities and stochastic variables 
identify and calculate stochastic variables and use statistical models in relevant problems
identify and calculate central probability distributions, such as binomial, poisson, exponential and normal distribution 
calculate confidence intervals and perform one-sample hypothesis testing for normally distributed variables with known and unknown variance and apply the central limit theorem 
perform calculations related to correlation and simple linear regression 
apply statistical principles and concepts in his/hers professional field 
use calculation tools to perform necessary statistical calculations
interpret data material and results from methods within statistical learning and data science
General competence
 The candidate
sees the importance of statistical knowledge and expertise in the engineering role
is able to communicate with professionals about engineering and business problems by using statistical concepts and expressions
is confident in methodological work on a data material and can interpret sensivity related to several methods within statistical learning and data science

Learning methods and activities

Lectures, collaborative project work and exercises.

Compulsory assignments

  • Exercises

Further on evaluation

Re-sit exam in August

Specific conditions

Compulsory activities from previous semester may be approved by the department.

Admission to a programme of study is required:
Computer Science (BIDATA)

Course materials

Gunnar Løvås: Statistikk for universiteter og høgskoler. Online resources, compendium in basic statistical learning and data science.

Credit reductions

Course code Reduction From To
ISTA1001 5.0 01.09.2020
ISTA1002 5.0 01.09.2020
ISTA1003 7.5 01.09.2020
ISTG1001 5.0 01.09.2020
TALM1005 5.0 01.09.2020
TDAT2001 5.0 01.09.2020
IE203312 5.0 01.09.2020
IR201812 5.0 01.09.2020
ISTT1003 7.5 01.09.2020
IR102712 4.0 01.09.2020
SMF2251 5.0 01.09.2020
ISTT1002 5.0 01.09.2020
ISTG1002 5.0 01.09.2020
ISTT1001 5.0 01.09.2020
VB6200 5.0
More on the course



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


Term no.: 1
Teaching semester:  AUTUMN 2020

Language of instruction: -

Location: Gjøvik

Subject area(s)


Contact information
Course coordinator:

Department with academic responsibility
Department of Mathematical Sciences


Examination arrangement: Written examination and work

Term Status code Evaluation Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Work 20/100
Room Building Number of candidates
Autumn ORD Home examination (1) 80/100





Room Building Number of candidates
Summer UTS Home examination 80/100 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.
  • 1) Merk at eksamensform er endret som et smittevernstiltak i den pågående koronasituasjonen. Please note that the exam form has changed as a preventive measure in the ongoing corona situation

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

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