Statistical Mathematics
| Structure Type: | Study unit |
|---|---|
| Code: | TLVP1502 |
| Type: | Compulsory / Basic Studies |
| Curriculum: | ST 2016V |
| Level: | Bachelor of Engineering |
| Year of Study: | 3 (2018-2019) |
| Credits: | 2 cr |
| Responsible Teacher: | Ranta, Mikko |
| Language of Instruction: | Finnish |
Courses During the Academic Year 2018-2019
| Impl. | Group(s) | Study Time | Teacher(s) | Language | Enrolment |
|---|---|---|---|---|---|
| 10 | T-LT-4VT | 2018-08-31 – 2018-12-21 | Kerstin Siegfrids | Finnish | 2018-08-20 – 2018-09-17 |
| 11 | I-ST-3V | 2019-01-07 – 2019-05-18 | Jussi Ojanen | Finnish | 2018-12-10 – 2019-01-14 |
Still need to take the course? See the courses during the academic year 2019-2020.
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.
Contents
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).