(NMSA 407) Linear regression
Lectures: doc. RNDr. Arnošt Komárek, Ph.D.
| Lab sessions: | Tu: 12:20 - 13:50 | @K11 | (lecturer: Stanislav Nagy) |
| Th: 14:00 - 15:30 | @K11 | (lecturer: Matúš Maciak) | |
| Th: 15:40 - 17:10 | @K4 | (lecturer: Matúš Maciak) |
General Information
Three 'parallel' sessions, all in English language, are taking place in the winter term 2020/2019. Each student attending one of these sessions is expected to be officially enrolled for the corresponding session in SIS. Any exceptions must be discussed and agreed with both lecturers. The sessions are synchronized in order to cover the same topics and mostly the content of the classes held in the same week will be approximately the same.
Due to the recent Covid-19 regulations the teaching procees will be based on online lectures (online video, supporting material, additional individual tasks, online discussion sessions). More details will be distributed by email (using the email addresses assigned to students in SIS). The students are, therefore, required to use their own computers/laptops and the statistical software R. If the Covid-19 regulations allow for in-person lectures the computers provided in the lecture rooms K4 and K11 (with the R software being already installed on all of them) can be used alternatively. Further details and most recent informations are also provided in Moodle UK. (login + enrollment key required) Students will obtain the Moodle login details after the registration. The enrollment key together with the group specific enrollment keys will be distributed by email.
- The R software can be obtained free of charge (GNU general public license) on 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 correspondingly (with a focuss on the statistical theory behind, not the implementation of the R commands themselves). Thus, all students are expected to be familiar with R and to be able to handle R programming by themselves.
- Lab session no.1 | Week no.1 (29.09 - 02.10)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.2 | Week no.2 (05.10 - 09.10)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.3 | Week no.3 (12.10 - 16.10)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.4 | Week no.4 (19.10 - 23.10)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.5 | Week no.5 (26.10 - 30.10)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.6 | Week no.6 (02.11 - 06.11)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.7 | Week no.7 (09.11 - 13.11)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.8 | Week no.8 (16.11 - 20.11)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.9 | Week no.8 (23.11 - 27.11)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.10 | Week no.10 (30.11 - 04.12)
Working R script: download
Supporting material available on Moodle UK
- Lab session no.11 | Week no.11 (07.12 - 11.12)
Working R script: download
Supporting material available on Moodle UK
- Final Test | Week no.12 (14.12 - 18.12)
All important information is provided on Moodle UK
- Lab session no.12 | Week no.13 (21.12 - 25.12)
Working R script: download
Supporting material available on Moodle UK
Supplementary Material
Supporting material -- audio/video recordings specific for each R script from the sylabus list -- will be avaiable at the given week on Moodle UK. Students need to register and they must be enrolled for the course.
Additional material (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
Homework Assignments
There will be two homework assignments. Each homework assignment can be worked out in a group of 1--3 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:October 22, 2020 (23:59 CEST)
- 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, 2020 (23:59 CEST)
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.
