|
Lecturer(s)
|
-
Rost Michael, doc. Ing. Ph.D.
|
|
Course content
|
Lectures: 1 - cutting edge statistical software, advantage disadvantage, history of programming environment R 2 - installation and configuration on MS Windows, review of libraries 3 - some mathematical and statistical functions 4 - graphical command and parameters, simulation and usefulness of metafunctions in R 5 - objects in R (data frame, vector, matrix, arrays, ts, list?) 6 - data input, manipulation with data, principle of vectorization, logical function in R 7 - some statistical method and how to carry them in R 8 - modern way of visualization of the data set, one dimensional data visualization 9 - visualization of the multivariate data set 10 - graphics in statistical software, lattice library 11 - introduction to programming and self written function in R 12 - object oriented programming in R 13 - new trends, statistical software, operation systems, live distributions and statistical software Seminars: 1 - cutting edge statistical software, advantage disadvantage, history of programming environment R 2 - instaliation and configuration on MS Windows, review of libraries 3 - some mathematical and statistical functions 4 - graphical command and parameters, simulation and usefulness of metafunctions in R 5 - object in R (data frame, vector, matrix, arrays, ts, list?) 6 - data input, manipulation with data, principle of vectorization, logical function in R 7 - some statistical method and how to carry them in R 8 - modern way of visualization of the data set, one dimensional data visualization 9 - visualization of the multivariate data set 10 - graphics in statistical software, lattice library 11 - introduction to programming and self written function in R 12 - object oriented programming in R 13 - new trends, statistical software, operation systems, live distributions and statistical software
|
|
Learning activities and teaching methods
|
|
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming)
|
|
Learning outcomes
|
The main aim is to introduce students to programming environment for data analysis and visualization and teach the student how to use it during the data analysis.
Students are able to communicate with programming environment R and know how to use it during the data analysis and visualization.
|
|
Prerequisites
|
Prerequisities: KMI/TPS2 or KMI/TPS2A Theory of Probability and Statistics 2
|
|
Assessment methods and criteria
|
Combined exam, Test
Credit Requirements: Seminars are mainly oriented on unsupported individual work with teacher. Seminars are tightly linked to content of lectures. The main part of work in seminars could be described as "problem solving". Themes of seminar works corresponds with explained topics on lectures. Fore more details see the summary of subject. Students are working with programming environment R during the seminars. Students obtain the credit on the base of submission of the one seminar work, attendance and passing the test. In some cases could be assign individual work. Examination Requirements: To pass written part of exam you have to solve absolute majority of problems.
|
|
Recommended literature
|
-
Dalgaard P. Introductory Statistics with R. Springer, 2002. ISBN 0-387-95475-9.
-
Everitt B. S. An R and S-Plus Companion to Multivariate Analysis. Springer, 2005. ISBN 1-85233-882-2.
-
Faraway, J. Linear Models with R. Boca Raton : Chapman & Hall/CRC, FL, 2004. ISBN 1-584-88425-8.
-
Maindonald, J., Braun, J. Data Analysis and Graphics Using R. Cambridge : Cambridge University Press, 2003. ISBN 0-521-81336-0.
-
Venables, W., N., Ripley, B.D. Modern Applied Statistics with S. New York : 4th ed, 2002. ISBN 0-387-95457-0.
|