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Tilastomatematiikan peruskurssi (MATLY0005), 3 op
Basic information
| Course name: | Tilastomatematiikan peruskurssi Basic course in statistics |
| Course Winha code: | MATLY0005 |
| Kurre acronym: | TilMat |
| Credits: | 3 |
| Type and level of course: | Basic studies |
| Year of study, semester or study period: | 2.year |
| Implementation: | 1.period, 2.period |
| Semester: | 0607 |
| Language of tuition: | Suomi |
| Teacher: | Veli-Matti Taavitsainen |
| Final assessment: | Grading scale (0-5) |
Descriptions
Prerequisites
Basic course in mathematics A (MATLY0111)
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. The idea of simulation using random numbers. Confidence limits and statistical reasoning using confidence limits. Statistical tests and their applications in chemical engineering. The idea of statistical process control (SPC). Regression analysis and its applications in chemical engineering. Statistical functions in Excel.
Course contents (additional)
Multiple linear regression. Estimating reliability of regression models. Statistical software (e.g. R)
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 model random phenomena and statistical interpretation of measurement 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 chemical 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 chemical engineering. The student is able to apply the principle of least squares in regression analysis
Recommended reading
Handouts in pdf-format.
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: -
Teaching methods and student workload
Assessment weighting and grading
Two exams (minimum 40% of the maximum score), approval of the computer labs.
Related competences of the degree programme
Theoretical basis and mathematical and science skills