Uniwersytet w Białymstoku - Centralny System Uwierzytelniania
Strona główna

Probabilistic Methods and Statistics 420-IS1-2PST-ENG
Laboratorium (LAB) Rok akademicki 2022/23

Informacje o zajęciach (wspólne dla wszystkich grup)

Liczba godzin: 15
Limit miejsc: (brak limitu)
Zaliczenie: Zaliczenie na ocenę

Basic literature:

1. Biecek P., Przewodnik po pakiecie R, Oficyna Wyd. GiS, Wrocław 2017, wyd. 4. (first chapters available at http://biecek.pl/R/PrezentnikPoPakiecieRWydanieIVinternet.pdf) [access 2020-01-19] (in Polish).

2. Biecek P., Pogromcy danych (course materials available in the form of e-books at http://biecek.pl/R/) [access 2020-01-19] (in Polish).

3. Lander J.P., R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics), Addison Wesley 2017.

4. Wickhame H., ggplot2: Elegant Graphics for Data Analysis, online version available at https://ggplot2-book.org/) [access


5. Technical documentation of R library packages

Supplementary literature:

1. https://bookdown.org/ website with many books and scripts on an open access basis [access 2020-01-19].

2. Use R! Series published by Springer, available to students from the website of the University of Bialystok Library.

3. Stowell S., Using R for Statistics, Apress 2014.

Efekty uczenia się:

The student can use the statistical characteristics of the population and their sample equivalents - the test, evaluation of the student's activity during classes.

The student can make simple statistical inferences, also with the use of computer tools - test, evaluation of the student's activity during classes.

The student can use computer programs in the field of data analysis - test, evaluation of the student's activity during classes.

The student knows the limitations of his own knowledge and understands the need for further education - evaluation of the student's activity during classes.

Metody i kryteria oceniania:

1. The laboratory will include:

• test, for which the student can get 80 points,

• homework, for which the student can receive 20 points.

a) Each homework is scored equally and is assigned a number of points equal to the quotient of the number of points allocated to all homework and the number of all homework. The tutor may evaluate each of the works on the appropriate point scale, but the number of points obtained is proportionally converted into the number of points referred to in the previous sentence.

b) Each homework must be sent to the tutor within two weeks of the homework assignment (in the case of the end of the semester, this deadline may be shortened to 1 week). Works delivered after the deadline are not taken into account.

2. The basis for exempting a student from participation in part or all of the classes may be

• obtaining the consent of the Director of the Institute for the Individual Organization of Studies (IOS), unless the subject is included in the list of subjects for which the student is required to attend.

• participate in the Individual Study Program (IPS).

The method of conducting classes in the case of a student who has received the Director’s consent for IOS or IPS should be agreed within 14 days of receiving this consent.

3. Missing the 3 hours of the laboratory provided by the plan by the student without justifying them may be the basis for failing the classes.

4. The tutor issues a final grade in accordance with the grading scale specified at the end, provided that cheating on the test means obtaining a unsatisfactory grade from the laboratory.

Grading scale:

• unsatisfactory (fail) (ECTS grade F/FX, Polish grade 2) - up to 50.99 points,

• satisfactory (ECTS grade E, Polish grade 3) - from 51.00 to 60.99 points,

• better than sufficient (ECTS grade D, Polish grade 3+) - from 61.00 to 70.99 points,

• good (ECTS grade C, Polish grade 4) - from 71.00 to 80.99 points

• better than good (ECTS grade B, Polish grade 4+) - from 81.00 to 90.99 points,

• very good (ECTS grade A, Polish grade 5) - from 91.00 points.

Zakres tematów:

1. Introduction to R:

- RStudio as IDE for R,

- packages in R,

- basic data types and data structures in R,

- import data in R; data analysis in R,

- manipuation of data,

- working with data: tidyverse package,

- graphical presentation of statistical data - the ggplot2 package.

2. Generation of numerical distributions.

3. Descriptive statistic in R.

4. Statistical tests in R (parametric and nonparametric).

Metody dydaktyczne:

Teaching methods: computer laboratory, consultations, literature study, shomework, discussions in problem groups.

Grupy zajęciowe

zobacz na planie zajęć

Grupa Termin(y) Prowadzący Miejsca Liczba osób w grupie / limit miejsc Akcje
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