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Statistical Mathematics

Structure Type: Study unit
Code: TLVP1502
Type: Compulsory / Basic Studies
Curriculum: KT 2014V
Level: Bachelor of Engineering
Year of Study: 4 (2017-2018)
Credits: 2 cr
Responsible Teacher: Ranta, Mikko
Language of Instruction: Finnish

Courses During the Academic Year 2017-2018

Impl.Group(s)Study TimeTeacher(s)LanguageEnrolment
6 2017-09-01 – 2017-12-22Kerstin SiegfridsFinnish2017-08-21 – 2017-09-18
7I-EY-3V, I-RT-3V2017-08-25 – 2017-12-22Mikko RantaFinnish2017-08-21 – 2017-09-18
8I-KT-2V, I-TT-2V2018-01-08 – 2018-05-26Jussi OjanenFinnish2017-12-11 – 2018-01-15

Still need to take the course? See the courses during the academic year 2018-2019.

Learning Outcomes

The student is able to find statistical data and interpret statistical figures. The student is able to analyze statistical data with different statistics. The student knows the basics of correlation and regression analysis, and is able to run variance analysis and T-test analysis. The student can critically analyze the results acquired from the research. The student is able to use Excel software for statistical analysis.

Student's Workload

54 hours, of which
6 hours of scheduled studies
14 hours of virtual studies, and
34 hours of autonomous studies

The assessment of student’s own learning 1 h is included in contact lessons.


Basic terms, statistical charts, parameter estimation, linear regression, dependence analysis, correlation, variance, T-test. Using Excel in statistical research.

Recommended or Required Reading and Other Learning Resources/Tools

Lecture notes

Mode of Delivery / Planned Learning Activities and Teaching Methods

Contact studies, virtual lectures and exercises, project work.

Assessment Criteria

1 the student is able to recognise and use the basic concepts of statistical analysis, and can calculate the most common characteristic values of a given material.
3 the student can carry out a statistical analysis on the internal dependences and influences of a given sample of data, e.g., correlations, regression and the T-test.
5 the student can interpret statistical analyses, draw logical conclusions, and apply the obtained knowledge and skills with challenging statistical samples.

Assessment Methods

Examination, project work and exercises. Numeric scale (0-5).