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Elements of statistics

General data

Course ID: 17-DSTA-IP0
Erasmus code / ISCED: 11.2 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0542) Statistics The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title: Elements of statistics
Name in Polish: Elementy statystyki
Organizational unit: AMU Nadnotecki Institute in Piła
Course groups: (in Polish) Moodle - przedmioty Innych jednostek
ECTS credit allocation (and other scores): 0 OR 2.00 (depends on study program) 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
Module type:

compulsory
Major:

information technology
Cycle of studies:

1st cycle
Module learning aims:

Familiarise with elementary statistical methods in term of descriptive statistics, parametric and nonparametric tests, regression and correlation analysis.

Acquire the skills of application statistical methods to varoius types of data.

Year of studies (where relevant):

Year 3
Course module conducted remotely (e-learning):

Up to 60% of lectures and 40% of laboratories can be realised in the form of remote classes
Pre-requisites in terms of knowledge, skills and social competences:

Elementary methods of overall mathematics
Information on where to find course materials:

Teams platform
Methods of teaching for learning outcomes achievement:

- lecture supplemented with examples

- Tasks in Excel using real data
Student workload (ECTS credits):

2
Short description:

The course will present basic statistical methods in term of descriptive statistics, statistical tests, correlation and regression analysis. In addition to theoretical knowledge of statistical methods, the emphasis will be placed on their application to various types of data using the popular Excel tool.
Full description:

Classes in the subject cover the most important issues in various fields of statistics. The first topic concerns the basic concepts of the theory of probability such as a random variable and its characteristics, probability and probability distribution. The next topic concerns the estimators of the characteristics of random variables. Next, we will discuss statistical tests. In addition to the general formula of the test, the most important parametric and non-parametric tests will be presented.

The methods of determining the confidence intervals will also be indicated. The next topic is correlation analysis, with emphasis not only on Pearson's but also Spearman's correlation analysis. The last topic is about regression analysis.
Bibliography:

1. Mieczysław Sobczyk, Statistics (in Polish), Wydawnictwo Naukowe PWN, Warszawa, 2007

2. Robert R. Johnson, Patricia J. Kuby, Elementary Statistics, Cengage Learning, 2011
Learning outcomes:

Knowledge:

- student defines basic statistical estimators

- student describe tstatistical test procedures

- student lists basic statistical test

Abilities:

- student applies statistical methods to analyzing real data.

Assessment methods and assessment criteria:

1. final project

2. Theoretical Exam (oral or written)
Practical placement:

not concern

Classes in period "Academic year 2020/2021, summer semester" (past)

Time span: 2021-03-01 - 2021-09-30
Selected timetable range:
Navigate to timetable
Type of class:
laboratory, 15 hours more information
lecture, 15 hours more information
Coordinators: Piotr Płuciennik
Group instructors: Piotr Płuciennik
Students list: (inaccessible to you)
Examination: Graded credit
Module type:

compulsory
Pre-requisites in terms of knowledge, skills and social competences (where relevant):

as above
ECTS code:

j.w
Number of hours:

30
Module learning aims:

as above
Short description:

as above
Full description:

as above
Bibliography:

as above
Notes:

none

Classes in period "Academic year 2021/2022, summer semester" (past)

Time span: 2022-02-24 - 2022-09-30
Selected timetable range:
Navigate to timetable
Type of class:
laboratory, 15 hours more information
lecture, 15 hours more information
Coordinators: Piotr Płuciennik
Group instructors: Piotr Płuciennik
Students list: (inaccessible to you)
Examination: Graded credit
Module type:

compulsory
Pre-requisites in terms of knowledge, skills and social competences (where relevant):

as above
ECTS code:

j.w
Number of hours:

30
Module learning aims:

as above
Short description:

as above
Full description:

as above
Bibliography:

as above
Notes:

none

Classes in period "Academic year 2022/2023, summer semester" (past)

Time span: 2023-02-27 - 2023-09-30
Selected timetable range:
Navigate to timetable
Type of class:
laboratory, 15 hours more information
lecture, 15 hours more information
Coordinators: Piotr Płuciennik
Group instructors: Piotr Płuciennik
Students list: (inaccessible to you)
Examination: Course - Graded credit
laboratory - Graded credit
lecture - Graded credit

Classes in period "Academic year 2023/2024, summer semester" (in progress)

Time span: 2024-02-26 - 2024-09-30
Selected timetable range:
Navigate to timetable
Type of class:
laboratory, 15 hours more information
lecture, 15 hours more information
Coordinators: Piotr Płuciennik
Group instructors: Piotr Płuciennik
Students list: (inaccessible to you)
Examination: Course - Graded credit
laboratory - Graded credit
lecture - Graded credit
Course descriptions are protected by copyright.
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