Study Guide

Objective

After completing the course the student can:
- calculate different mean and standard deviation figures for a given statistical data
- determine the regression line and correlation, and understand their significance
- identify and draw various statistical diagrams
- recognize the basic concepts of continuous and discrete probability distributions
- normalize a normally distributed variable and calculate the associated probabilities
- calculate confidence intervals and understand the significance of error in statistical mathematics
- determine p-value using the z-test and t-test of average
- construct contingency tables and apply the chi-square test
- utilize information technology in the processing and analysis of statistical data

Content

- mean and standard deviation figures
- diagrams and their differences
- regression, correlation
- basic definitions and formulas of probability
- discrete probability distribution, binomial distribution, Poisson distribution
- continuous probability distribution, normal distribution, normalization
- statistical testing, sampling, confidence interval
- z-test and t-test of average
- contingency tables and chi-square test

Materials

Teacher's materials in conjunction with the free online textbook: Introductory Statistics 2e by OpenStax.

For additional reading, the following book is recommended: Probability and Statistics (3rd edition), Murray R. Spiegel, John J. Schiller, R. Alu Srinivasan, SCHAUM’S outlines

Teaching methods

Lectures, teacher-directed classroom activities, group work and independent work

Exam schedules

1st part-exam: week 43
2nd part-exam: week 50
The possibility to retake part-exams is organized in Dec/Jan (time to be announced later)

International connections

During the course we will learn statistical and probability skills and knowledge fundamental for an ICT-engineer's profession. Various forms of technology are widely used in all topics and students are encouraged to learn more ways to use technology in solving problems within the course's topics. A lot of learning happens in class with the guidance of the teacher but independent study and homework is important as well.

Sustainable development aspects are considered during the course. Lecture material and homework feature examples about creating an ecologically sustainable and resilient society, and the impact of statistics in promoting sustainable development is discussed.

Completion alternatives

The course may be completed with a special exam that proves the student's competence with the course topics. This must be agreed separately with the teacher. If the student is interested, they should contact the teacher.

Student workload

Contact lessons and exams: 56 h (approx. 4 h / week)
Independent study, homework and preparation for part-exams: 70 h (approx. 5 h / week)

Content scheduling

The course will begin on week 36 and end on week 50.

Topics include:
- mean and standard deviation figures
- diagrams and their differences
- regression, correlation
- basic definitions and formulas of probability
- discrete probability distribution, binomial distribution, Poisson distribution
- continuous probability distribution, normal distribution, normalization
- statistical testing, sampling, confidence interval
- z-test and t-test of average
- contingency tables and chi-square test

Further information

All practical information is provided in ITSLEARNING.

It is possible to do an optional statistical project during the course which will give you an extra 2 credits, i.e. a total of 7 credits. This project will be about a topic of your own choosing (with the help of the teacher) and you are expected to use a wide range of statistical and/or probabilistic methods in it. You are expected to gather, process, present and analyse data and produce a report about it, to be submitted by the end of the course by an agreed upon deadline. It is expected that you devote approximately 30-50 hours of work into this. The report won't affect your course grade but, if it contains the required elements and is of an appropriate depth, you will get the 2 extra credits. The teacher must be informed prior to beginning this project.

Evaluation scale

H-5

Assessment methods and criteria

The assessment consists of:
- Two part-exams (2x40 p = 80 p).
- Homework (60 p from weekly homework and 10 p from Excel homework)
- Extra task (10 p)
Total: 160 p

The course has mandatory attendance of at least 50%. This is used to verify that the student is doing the minimum amount of work required to complete enough assignments and pass the course.

To pass, the total points obtained must be at least 50, and at least a combined of 20 p from the exams. This corresponds to basic knowledge of the fundamental learning goals set during the course.

The extra task can be completed by the deadline set by the teacher. This task is about learning how to use some technology not covered during classes.

Grade boundaries:
0-49: FAIL
50-69: 1
70-89: 2
90-109: 3
110-129: 4
130-160: 5

Assessment criteria, fail (0)

Fewer than 50 points in total
OR
fewer than 20 points from the part-exams
OR
attendance of less than 50%

Assessment criteria, satisfactory (1-2)

Total points between 50 and 89
AND
combined exam points at least 20
AND
attendance at least 50%

Assessment criteria, good (3-4)

Total points between 90 and 129
AND
combined exam points at least 20
AND
attendance at least 50%

Assessment criteria, excellent (5)

Total points at least 130
AND
attendance at least 50%

Qualifications

Courses Engineering Precalculus, Calculus and Topics in Applied Mathematics
OR
equivalent skills