Course - Statistics - ISTT1003
ISTT1003 - 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): Multiple linear regression, classification, cluster analysis.
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 some methods for statistical learning and data science.
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
- perform regression, classification and cluster analysis on different data sets, and describe results from methods for statistical learning and data science
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, statistical learning and data science 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.
Specific conditions
Admission to a programme of study is required:
Computer Science - Engineering (BIDATA)
Digital Infrastructure and Cyber Security (BDIGSEC)
Recommended previous knowledge
Mathematical Methods 1
Introduction to the engineering profession
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. Compendium in basic statistical learning and data science. Thematic videos.
Credit reductions
| Course code | Reduction | From | To |
|---|---|---|---|
| ISTA1001 | 5.0 | AUTUMN 2020 | |
| ISTA1002 | 5.0 | AUTUMN 2020 | |
| ISTA1003 | 7.5 | AUTUMN 2020 | |
| ISTG1001 | 5.0 | AUTUMN 2020 | |
| ISTG1002 | 5.0 | AUTUMN 2020 | |
| ISTG1003 | 7.5 | AUTUMN 2020 | |
| ISTT1001 | 5.0 | AUTUMN 2020 | |
| ISTT1002 | 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 | |
| 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 |
Version: 1
Credits:
7.5 SP
Study level: Foundation courses, level I
Term no.: 1
Teaching semester: AUTUMN 2024
Language of instruction: Norwegian
Location: Trondheim
- Statistics
Department with academic responsibility
Department of Mathematical Sciences
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 SL510 Sluppenvegen 14 12 SL311 orange sone Sluppenvegen 14 30 SL110 lilla sone Sluppenvegen 14 49 SL111 orange sone Sluppenvegen 14 60 -
Autumn
ORD
Work
30/100
Submission
2024-11-18
INSPERA
12:00 -
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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"