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Tilastomatematiikan peruskurssi (MATLV0005), 3 op
Basic information
| Course name: | Tilastomatematiikan peruskurssi Basic course in statistics |
| Course Winha code: | MATLV0005 |
| Kurre acronym: | TilMat |
| Credits: | 3 |
| Type and level of course: | Basic studies |
| Year of study, semester or study period: | 2.year |
| Implementation: | Spring semester, 3.period, 4.period |
| Semester: | 0607 |
| Language of tuition: | Suomi |
| Teacher: | H-L Merenti-Välimäki |
| Final assessment: | Grading scale (0-5) |
Descriptions
Prerequisites
Basic course in mathematics A and B (MATLV0111, MATLV0112)
Course contents (core content level)
Probability and basic rules of calculating probabilities. Random variables and their most common distributions. Estimation of measurement uncertainty. Graphical representation of statistical data. Confidence limits and statistical reasoning using confidence limits. Statistical tests and their applications in media engineering. Regression analysis and its applications in media engineering. Statistical functions in Excel and Matlab.
Course contents (additional)
Multiple linear regression. Estimating reliability of regression models. Statistical software (e.g. Statgraphics)
Core content level learning outcomes (knowledge and understanding)
The student learns the basic concepts related to probability and basic principles of statistical thinking. The student learns how to model random phenomena and visualize the measured data. The student knows the basic principles of statistical tests, statistical process control and how to apply simple regression analysis in engineering problems. The student learns to interpret statistical data using basic statistics and graphical representations.
Core content level learning outcomes (skills)
The student is able to apply basic rules of probability. The student knows the most important discrete and continuous distributions and how to apply them in problems of media engineering. The student understands the meaning of a confidence interval and how to apply it in statistical reasoning. The student knows the most common statistical tests of comparisons and how to apply them in problems of media engineering. The student is able to apply the principle of least squares in regression analysis
Recommended reading
Launonen-Sorvali-Toivonen: Teknisten ammattien matematiikka 3E
Antti Niemi: Todenäköisyyslaskennan ja tilastomatematiikan perusteet
Mäkelä: Matlab-perusteet
Printouts
Teaching and learning strategies
Class room teaching: 42 h
Computer labs: 14 h
Exams: 2h + 2h
Student individual workload (workload analysis not carried out): 20 h
Total: 80 h
Follow-up of the student workload analysis performed: -
Tuition in Finnish.