Course syllabus BIAX20036 - Introduction into Statistics (FI - WS 2019/2020)
Course unit code:
|Course unit title:||Introduction into Statistics|
Planned learning activities and teaching methods:
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|Level of study:|
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|Prerequisites for registration:||none|
Assessment during the semester: Two written tests during the semester. The maximum number of points is 40. Final Assessment: The exam consists of a written test consisting of both open and closed questions. The maximum number of points is 60.
Individual grades of the credit system are awarded on the basis of a point assessment that reflects the degree of completion of the subject as follows:
To obtain an A rating, it is necessary to obtain at least 94 points to obtain a B rating of at least 86 points, a C rating of at least 76 points, a D score of at least 66 points, and an E score of at least 56 points. Credits will not be awarded to a student who receives less than 50 percent of some of the written reviews.
|Learning outcomes of the course unit:|
|The goal is to give students a comprehensive theoretical understanding of basic statistical methods and their practical application and interpretation of the results obtained. By completing the course students acquire skills in the field of the data evaluation and statistical methods supported by appropriate software.|
The content of the course consists of:
1. graphical representation of statistical data, sorting data, histogram,
2. descriptive statistics (measures of central tendency and variability),
3. descriptive statistics (skewness and kurtosis characteristics),
4. basic principles of probability, principles of sampling,
5. probability distributions,
6. methods of statistical inference (point and interval estimation),
7. selected tests - tests on the mean value and variance,
8. proportion tests, outlier detection techniques,
9. goodness of fit between a theoretical distribution and an empirical distribution,
10. correlation analysis (Pearson and Spearman's rank correlation coefficient),
11. regression analysis, linear regression,
12. nonlinear regression - intrinsically linear, finding the best regression model.
Recommended or required reading:
Language of instruction:
|Assessed students in total: 9|
|Name of lecturer(s):||prof. RNDr. Beáta Stehlíková, CSc. (person responsible for course)|
|Last modification:||25. 11. 2019|
Last modification made by Ján Lukáš on 11/25/2019.