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Úvod

Výuka

Studium

Výzkum

Odkazy

English

Stránky garanta
programu PMSE

NMSA407 – Central exercise classes webpage

Timetable

Lectures:	Thursday 9:00 in K1, Thursday 17:20 in K1 (A. Komárek)
Exercise class (MM1):	Monday 19:00 in K11 (M. Maciak)
Exercise class (MM2):	Tuesday 7:20 in K11 (M. Maciak)
Exercise class (MO):	Thursday 15:40 in K4 (M. Omelka)

R software

Those who are not familiar with the R software are adviced that they already before the first exercise class go through R-related materials available on the webpage of the NMSA230: Software for Mathematics and Stochastics course (in Czech language). In English plenty of introductory materials can be obtained here or by simple web-searching.

mffSM package

Most datasets we will be working on during the exercise classes plus few smaller R functions are available in a form of extension R package mffSM which can be installed ``from a local repository'' upon its download from the appropriate link below. Windows binary file is intended for the MS Windows users (as the title suggests), the source code is intended for those users who are used to compile their software from the source (mostly Linux, Mac etc. users). The mffSM package depends on packages colorspace, lattice, car, which are available in a standard way from CRAN. All those dependency packages should normally be automatically installed if the installation of the mffSM package is performed directly from the R console on an Internet-connected computer using the command (its appropriately modified analogy):

install.packages("C:/WHERE_DOWNLOADED/mffSM_1.0.zip", repos = NULL)

The mffSM package might be updated during the semester. If this happens, the version number (like 1.0) will be increased and new files will be available.

Windows binary:	mffSM_1.0.zip	(published 20140923)
Source code:	mffSM_1.0.tar.gz	(published 20140923)

Announcements

• Requirements to get the course credit are explained in detail in this document (pdf).
• Summary table of points obtained for delivered homeworks and a test.
• The secondary test will be on January, 7 at 8:00 in K1.
• To prepare for the final test we recommend to be able to answer the questions asked during the exercises classes and in the homework assignments. Some more sample tasks that can appear in the final test can be downloaded here.

R scripts for exercise classes

Class #1 (06/10)	Assignment (R script)	(published 20150928)
Class #2 (12/10, 13/10, 15/10)	Assignment (R script)	(published 20151009)
	Data (csv)
Class #3 (19/10, 20/10, 22/10)	Assignment (R script)	(published 20151016)
Class #4 (26/10, 27/10, 29/10)	Assignment (R script)	(published 20151023)
Class #5 (2/11, 3/11, 5/11)	Assignment (R script)	(published 20151030)
Class #6 (16/11, 10/11, 12/11)	Assignment (R script)	(published 20151106)
Class #7 (23/11, 24/11, 19/11)	Assignment (R script)	(published 20151114)
	Data (csv)	4 slides about multiplicative models
Class #8 (30/11, 1/12, 26/11)	Assignment (R script)	(published 20151124)
Class #9 (7/12, 8/12, 3/12)	Assignment (R script)	(published 20151129)
Class #10 (14/12, 15/12, 10/12)	Assignment (R script)	(published 20151207)
Class #11 (21/12, 22/12, 17/12)	Assignment (R script)	(published 20151214)
	Boston2 (csv)
Class #12 (4/1, 5/1, 7/1)	Assignment (R script)	(published 20151221)
	Data (RData)	Precalculated results (RData)
Class #13 (11/1, 12/1, 14/1)	Assignment (R script)	(preliminary version published 20160109)
	data (csv)	soilData (dataset)

In the code it is assumed that datasets are stored in the directory called 'Data' which is subdirectory of the working directory.

Homework assignments

• Homework 1 (Deadlines Mo: 19/10, Tu: 20/10, Th: 22/10) - Assignment (pdf), River quality (dataset), (published 20151005).
• Homework 2 (Deadlines Mo: 30/11, Tu: 01/12, Th: 26/11) - Assignment (pdf), horsesData (dataset), (published 20151106).
• Homework 3 (Deadlines Mo: 28/12, Tu: 28/12, Th: 28/12) - Assignment (pdf), soilData (dataset), (published 20151204).

Supporting material

• Homework 1 assignment in the previous academic year and general comments to quality of delivered reports (in Czech, by Karel Zvára). Dataset potatoes.