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Lecturer(s)
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Pavlů Ivana, Mgr. Ph.D.
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Vencálek Ondřej, doc. Mgr. Ph.D.
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Fačevicová Kamila, Mgr. Ph.D.
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Jašková Paulína, Mgr.
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Škorňa Stanislav, Mgr.
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Course content
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1. Motivation and basic concepts of statistics. 2. Work with data - data structure (rows and columns of data matrix), basic work with data - selection of part of data according to position and according to condition. 3. Description of the distribution of one attribute - qualitative attributes - frequency, relative frequency, bar chart and pie chart. 4. Description of the distribution of one attribute - quantitative attributes - basic numerical characteristics of the location (average, median, quantiles). 5. Description of the distribution of one attribute - quantitative attributes - basic numerical characteristics of variability (variance, standard deviation, coefficient of variation, interquartile range). 6. Description of thedistribution of one attribute - quantitative attribute - skewness, kurtosis; invariance / equivariance of individual numerical characteristics with respect to linear transformations and the importance of these properties for practice. 7. Description of the distribution of one attribute - quantitative attributes- histogram (Sturges' rule), boxplot. 8. Description of the distribution of two attributes - two qualitative attributes - contingency table, visualization options. 9. Description of the distribution of two attributes - qualitative and quantitative attributes - comparison of numerical characteristics, possibilities of visualization. 10. Description of the distribution of two attributes - two quantitative attributes - correlation, scatterplot. 11. Possibilities of description and visualization of multidimensional data - variance matrix, correlation matrix.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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The aim of the course is to acquaint students with the basic procedures of descriptive statistics and general principles of working with data.
After the course, the student is able to perform a simple data analysis. He or she is able to choose appropriate numerical and graphical means for the description of various statistical features (according to their nature) and the relationships between these features. He or she is also able to interpret these outputs correctly.
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Prerequisites
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- knowledge of basic mathematics - don't be afraid to try working with statistical software
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Assessment methods and criteria
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Seminar Work
Seminar work and presentation of results.
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Recommended literature
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J. Hanousek, P. Charamza. (1992). Moderní metody zpracování dat: matematická statistika pro každého. Praha: GRADA.
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M. Budíková, T., Lerch, Š. Mikoláš. (2005). Základní statistické metody. MU Brno.
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N. Silver. (2014). Signál a šum ? většina předpovědí selže, některé ne.
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O. Vencálek. (2015). Základy analýzy dat v softwaru Mathematica. UP Olomouc.
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W. N. Venables, B. D. Ripley. (2002). Modern Applied Statistics with S. Springer.
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