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Strona główna

Statistics

General data

Course ID: WM-MA-S
Erasmus code / ISCED: (unknown) / (0541) Mathematics The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title: Statistics
Name in Polish: Statystyka
Organizational unit: Faculty of Mathematics and Natural Sciences. School of Exact Sciences.
Course groups:
ECTS credit allocation (and other scores): 7.00 Basic information on ECTS credits allocation principles:
  • the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS;
  • the student’s weekly hourly workload is 45 h;
  • 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes;
  • weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS;
  • work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.

view allocation of credits
Language: Polish
(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się:

mathematics

Subject level:

elementary

Learning outcome code/codes:

MA1_W01, MA1_W03,MA1_W04, MA1_W05,MA1_W08;

MA1_U09, MA1_U11,MA1_U30, MA1_U31,MA1_U32, MA1_U33,MA1_U34, MA1_U35,MA1_U36;

MA1_K02;

Preliminary Requirements:

Knowledge of: mathematical analysis I, algebra with geometry I, probability theory I.

Full description:

The aim of the course is to provide knowledge of the basics of mathematical statistics and statistical inference. In lecture, students master the knowledge of the assumptions and construction of statistical models and their role in statistical inference.

In exercises and laboratory, students master methods of practical application of statistical models in other fields.

Efekty kształcenia i opis ECTS:

In the field of lecture, the student:

W1. has knowledge of the place of statistics in mathematics and its applications (MA1_W01);

W2. has knowledge of the construction and analysis of simple mathematical-statistical models based on basic mathematical theories and their applications in other sciences (MA1_W03);

W3. knows basic theorems of descriptive and mathematical statistics (MA1_W04);

W4. knows examples illustrating basic statistical concepts (MA1_W05);

W5. knows the basics of computational techniques in the area of descriptive and mathematical statistics (MA1_W08)

In the area of exercises and laboratory, the student:

U1. is able to assign appropriate functions with their properties to statistical issues (MA1_U09);

U2. is able to interpret and apply quantitative and graphical methods of presenting data and their interdependencies (MA1_U11);

U3. is able to construct and analyse a mathematical model of a random experiment and uses the concepts of probabilistic and statistical space (MA1_U30);

U4. knows practical applications of basic (discrete and continuous) probability distributions (MA1_U31);

U5. is able to apply the integral p-value and Bayesian formulas (MA1_U32);

U6. is able to estimate the parameters of parametric distributions, also using CLT (MA1_U33);

U7. is able to use statistical population characteristics and their sample equivalents (MA1_U34);

U8. is able to make simple statistical inferences, also using computer tools (MA1_U35);

U9. is able to talk about statistical issues in understandable language (MA1_U36)

and

K1. is prepared to formulate questions to deepen a broader understanding of the problem being solved (MA1_K02).

Assessment methods and assessment criteria:

For all effects, the following assessment criteria are adopted for all forms of verification:

grade 5: fully achieved (no obvious shortcomings)

grade 4.5: achieved almost fully and criteria for awarding a higher grade are not met

grade 4: largely achieved and the criteria for a higher grade are not met

grade3.5: largely achieved -with a clear majority of positives -and the criteria for granting a higher grade are not met

grade 3: achieved for most of the cases covered by the verification and criteria for a higher grade are not met

grade 2: not achieved for most of the cases covered by the verification

Classes in period "Winter semester 2021/22" (past)

Time span: 2021-10-01 - 2022-01-31
Selected timetable range:
Navigate to timetable
Type of class:
Classes, 15 hours more information
Laboratory, 30 hours more information
Lectures, 30 hours more information
Coordinators: Piotr Śliwka
Group instructors: Piotr Śliwka
Students list: (inaccessible to you)
Examination: Course - examination
Classes - graded credit
Laboratory - graded credit
Lectures - examination
(in Polish) E-Learning:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

(in Polish) Opis nakładu pracy studenta w ECTS:

Lecture - estimated student workload:

- class participation: 30h

- preparation for classes: 5h

- consultations with the lecturer: 3h

- exam preparation: 15h

- individual reading: 5h

- exam: 2h


i.e. 60h equivalent to 2 ECTS.



Laboratory - estimated student workload:

- class participation: 30h

- preparing for classes: 15h

- consultations with the lecturer: 5h

- individual reading: 4h

- preparation for credit: 5h

- credit for classes: 1h


i.e. 60h equivalent 2 ECTS.


Classes - estimated student workload:

- class participation: 15h

- preparation for classes: 15h

- consultations with the lecturer: 4h

- individual reading: 5h

- preparation for credit: 5h

- homework: 5h

- credit for classes: 1h


i.e. 50h equivalent to 2 ECTS.

Classes in period "Winter semester 2022/23" (past)

Time span: 2022-10-01 - 2023-01-31
Selected timetable range:
Navigate to timetable
Type of class:
Classes, 15 hours more information
Laboratory, 30 hours more information
Lectures, 30 hours more information
Coordinators: Piotr Śliwka
Group instructors: Piotr Śliwka
Students list: (inaccessible to you)
Examination: Course - examination
Classes - graded credit
Laboratory - graded credit
Lectures - examination
(in Polish) E-Learning:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

(in Polish) Opis nakładu pracy studenta w ECTS:

Lecture - estimated student workload:

- class participation: 30h

- preparation for classes: 5h

- consultations with the lecturer: 3h

- exam preparation: 15h

- individual reading: 5h

- exam: 2h


i.e. 60h equivalent to 2 ECTS.



Laboratory - estimated student workload:

- class participation: 30h

- preparing for classes: 15h

- consultations with the lecturer: 5h

- individual reading: 4h

- preparation for credit: 5h

- credit for classes: 1h


i.e. 60h equivalent 2 ECTS.


Classes - estimated student workload:

- class participation: 15h

- preparation for classes: 15h

- consultations with the lecturer: 4h

- individual reading: 5h

- preparation for credit: 5h

- homework: 5h

- credit for classes: 1h


i.e. 50h equivalent to 2 ECTS.

Type of subject:

obligatory

Course descriptions are protected by copyright.
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