# ISTG1003 - Statistics

### Examination arrangement

Examination arrangement: Digital exam and work

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.

• Exercises

### Further on evaluation

The course has two evaluations. A continuation exam is held for the written school exam, this may be change to oral exam if there are few students. There is no continuation exam for the project.

If one evaluation is passed, and one is failed, the evaluation that is failed can be retaken if necessary next time the course is lectured ordinary.

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 evaluation must be retaken.

Continuation 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)
Digital Infrastructure and Cyber Security (BDIGSEC)
Programming (BPROG)

### 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
TALM1005 5.0 AUTUMN 2020
TDAT2001 5.0 AUTUMN 2020
IE203312 5.0 AUTUMN 2020
IR201812 5.0 AUTUMN 2020
ISTT1003 7.5 AUTUMN 2020
IR102712 4.0 AUTUMN 2020
SMF2251 5.0 AUTUMN 2020
ISTT1002 5.0 AUTUMN 2020
ISTG1002 5.0 AUTUMN 2020
ISTT1001 5.0 AUTUMN 2020
VB6200 5.0 AUTUMN 2021
More on the course
Facts

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

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2022

Language of instruction: -

Location: Gjøvik

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

Department of Mathematical Sciences

# Examination

#### Examination arrangement: Digital exam and work

Term Status code Evaluation Weighting Examination aids Date Time Examination system
Autumn ORD Digital school exam 70/100 2022-12-16 09:00
Autumn ORD Work 30/100

Release
2022-10-24

Submission
2022-11-21

09:00

12:00

Summer UTS Digital school exam 70/100
• * 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"

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