(NMSA 407) Linear regression
Lectures: doc. RNDr. Arnošt Komárek, Ph.D.
Lab sessions: | Tu: 17:20 - 18:50 | @K4 | (lecturer: Stanislav Nagy) |
Th: 15:40 - 17:10 | @K4 | (lecturer: Matúš Maciak) | |
Th: 17:20 - 18:50 | @K11 | (lecturer: Matúš Maciak) |
General Information
Three 'parallel' sessions, all in English language, are organized for the winter term 2021/2022. Each student is expected to be officially enrolled for the corresponding session in SIS and students are required to attend the session they are enrolled to.
As far as the Covid-19 regulations allow the teaching procees will be based on regular sessions (in person participation) held in K4 and K11 lecture rooms.
All necessary teaching material will be available on this web site and additional supporting material (video recordings, online discussion/forum, etc.) will be available on Moodle UK. More details will be distributed by email (using the email addresses assigned to students in SIS).
For all in-person lectures computers will provided in both lecture rooms (K4 and K11) with the R software being already installed on all computers. Alternatively, students may also use their own computers/laptops with the statistical software R.
- The R software can be obtained free of charge (GNU general public license) from https://www.r-project.org and there are distributions available for Windows, Linux, and Macintosh.
- The standard installation contains some basic packages. Additional packages can be downloaded from the CRAN repository and they can be directly installed using the R working environment. More details will be provided during the lectures when such packages are needed.
- Many tutorials in various languages can be either found here or just by simply searching the web.
- Moreover, students are required to install an additional R package (package mffSM).
The package is not available on the CRAN repository and it can not be installed
using a standard package installation. The package can be downloaded from the
course web site
(see the SUPPLEMENTARY R PACKAGE section) and it can be installed by running the command
from R working environment. The 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).install.packages("C:/WHERE_DOWNLOADED/mffSM_1.1.zip", repos = NULL)
- The mffSM package depends on packages colorspace, lattice, and car, which are already all available in the standard way from CRAN. All these dependency packages should be normally automatically installed if the installation of the mffSM package is performed directly from the R console on an internet-connected computer using the command above.
- All computers avialable in the lecture rooms K4 and K11 are equiped with the R software and the mffSM package should be properly installed on all of them.
- The mffSM package contains most of the datasets we will be working on during the exercise classes. In addition, there are also some minor R functions which will be useful during the term.
Credit Requirements
The credit requirements for the NMSA407 exercises consist of two main parts.
- Homework assignments
Both homework assignments must be accepted. - Final test
The overall gain in the final test must be 60 points out of 100.
Syllabus & Script Files
The syllabus will be updated as the semester progresses. The R script files provided below will be discussed during the sessions and they will evaluated with the R software and explained in detail (with a focuss on the statistical theory behind, not the implementation of the R commands themselves). Students are, therefore, expected to be familiar with R and they should be able to handle the R programming by themselves.
- Lab session no.1 | (29.09 and 05.10)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.2 | (07.10 and 12.10)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.3 | (14.10 and 19.10)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.4 | (21.10 and 26.10)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.5 | (02.11 and 04.11)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.6 | (08.11 and 11.11)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.7 | (16.11 and 18.11)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.8 | (23.11 and 25.11)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.9 | (30.11 and 02.12)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.10 | (07.12 and 09.12)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.11 | (14.12 and 16.12 / online)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
- Lab session no.12 | (04.01 and 06.01)
Working R script: download
Supporting material on Moodle UK (available after all three sessions are over)
Supplementary Material
Supporting material -- audio/video recordings specific for each R script from the sylabus list -- will be additionaly avaiable on Moodle UK. Students need to register and they must be enrolled for the course.
All necessary teaching material (homework assignments, final test examples, a brief theory on the maximum likelihood estimation, etc.) can be found here.
- Maximum likelihood theory
Brief theoretical summary and illustrative examples: PDF file - Final test: sample tasks
A set of illustrative examples which could appear in the final test: PDF file
An example of the final test (version from 2017): PDF file - Final test -- detailed information: (PDF file)
(all necessary information regarding the final test organization)
Homework Assignments
There will be two homework assignments. Each homework assignment can be worked out in a group of 1--2 students and different groups can be formed for different homework assignments. Groups of three students are preferable. For more details about the homework assignments see the NMSA 407 Outline document.
- Homework assignment no.1
All necessary instructions on what to do and where/when to submit you solutions are given in the description file. The data file can be either downloaded (see below) or it can be directly loaded into the R working environment by using the command specified in the description file.
→ Assignment & Instructions: PDF Document
→ Working Dataset: RData File
→ Deadline:November 18, 2021 (23:59 CET)
- Homework assignment no.2
All necessary instructions on what to do and where/when to submit you solutions are given in the description file. The data file can be either downloaded (see below) or it can be directly loaded into the R working environment by using the command specified in the description file.
→ Assignment & Instructions: PDF Document
→ Working Dataset: RData File
→ Deadline:December 31, 2021 (23:59 CET)
Disclaimer
Vrámci platných Pravidiel pro organizaci studia na Matematicko-fyzikální fakultě Univerzity Karlovy (ze dne 14.června, 2017), sa vzhľadom k Čl. 8, dds.2 týchto pravidiel týmto vyhlasuje, že povaha předmětu vylučuje právo studenta na jeden řádny a dva opravné termíny pro získaní zápočtu. Získaní zápočtu sa riadi výhradne pravidlami uvedenými vyššie a detailne popisanými v tomto NMSA 407 outline documente.