Informačný list ECTS Sylabus
Sylabus predmetu B1-ZKM - Basic kvantitative methods (FCE - SS 2019/2020)
|University:||Slovak University of Technology in Bratislava|
|Faculty:||Faculty of Civil Engineering|
|Course unit title:|
Basic kvantitative methods
Course unit code:
Mode of completion and Number of ECTS credits:
|Exam (6 credits)|
Name of lecturer:
|doc. Ing. Tomáš Bacigál, PhD. (examiner, instructor) - slovak|
doc. RNDr. Jana Kalická, PhD. (examiner, instructor, lecturer) - slovak
prof. RNDr. Martin Kalina, PhD. (examiner, instructor, lecturer) - slovak
prof. RNDr. Radko Mesiar, DrSc. (person responsible for course) - slovak
|Learning outcomes of the course unit:|
The course provides introduction to the probability theory and summary statistics, such as random variables, their probability distributions and parameters of distributions. Further, estimators and confidence intervals, hypothesis testing, correlation and regression analysis of sample data. Afterwards students will be able to handle and analyse samples of statistical data that accurs in landscaping and landscape planning.
|Prerequisites and co-requisites:||passed Mathematics 1|
|• Population and sample. Discrete and continuous randpm variables.
• Summary statistics, estimators and confidence intervals of parameters of population (average, mode, median, standard deviation, confidence intervals...)
• Graphical visualisation of samples, histogram, box-plot ...
• Random variable, continuous and discrete distributions of random variables.
• Probability functions, empirical and theoretical cumulative distribution functions, basic types of probability functions used in landscape engineering.
• Estimators of momenta and basic methods of fitting of probability functions. Crteria of quality of fitting including graphical methods.
• Introduction into hypotesis testing. Structure of hypothesis testing. Examples of tests: t-test, chi^2-test.
• Correlation and regression analysis. Assumptions (normality, independence, linearity, homoscedascity...) a diagnozis of linear regression (r, R-square adjusted and normalized, RMSE, MAE, AIC etc.). Outliers, influential observation
• Algorithms for the choice of variables in multivariable linear regression (stepwise regression, lasso)
Recommended or required reading:
|Planned learning activities and teaching methods:|
lectures and seminars
|Assesment methods and criteria:||Pre-test 40%|
final test 60 %
Language of instruction:
|Work placement(s):||There is no compulsory work placement in the course unit.|
Last modification made by Ing. Peter Korčák on 04/16/2019.