Uniwersytet w Białymstoku - Centralny System Uwierzytelniania
Strona główna

Statistics

Informacje ogólne

Kod przedmiotu: 440-ERS-1STS
Kod Erasmus / ISCED: 14.2 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. / (0314) Socjologia i kulturoznawstwo Kod ISCED - Międzynarodowa Standardowa Klasyfikacja Kształcenia (International Standard Classification of Education) została opracowana przez UNESCO.
Nazwa przedmiotu: Statistics
Jednostka: Instytut Socjologii
Grupy: 3L stac.I st.studia socjologiczne - przedmioty obowiązkowe
Punkty ECTS i inne: 5.00 Podstawowe informacje o zasadach przyporządkowania punktów ECTS:
  • roczny wymiar godzinowy nakładu pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się dla danego etapu studiów wynosi 1500-1800 h, co odpowiada 60 ECTS;
  • tygodniowy wymiar godzinowy nakładu pracy studenta wynosi 45 h;
  • 1 punkt ECTS odpowiada 25-30 godzinom pracy studenta potrzebnej do osiągnięcia zakładanych efektów uczenia się;
  • tygodniowy nakład pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się pozwala uzyskać 1,5 ECTS;
  • nakład pracy potrzebny do zaliczenia przedmiotu, któremu przypisano 3 ECTS, stanowi 10% semestralnego obciążenia studenta.

zobacz reguły punktacji
Język prowadzenia: angielski
Rodzaj przedmiotu:

obowiązkowe

Założenia (opisowo):

(tylko po angielsku) The aim of the course is to familiarize students with the basic concepts of statistics, descriptive, useful in social and economic sciences. The course is to teach students how to verify and interpret statistical data in standard situations. The purpose of the lecture and exercises is to provide students with basic concepts, tools and statistical methods through

which they will be able to: collect and compile statistical material, conduct statistical analysis of data, formulate correct conclusions.

Tryb prowadzenia przedmiotu:

w sali

Skrócony opis: (tylko po angielsku)

The aim of the course is to familiarize students with the basic concepts of statistics, descriptive, useful in social and economic sciences. The course is to teach students how to verify and interpret statistical data in standard situations. The purpose of the lecture and exercises is to provide students with basic concepts, tools and statistical methods through

which they will be able to: collect and compile statistical material, conduct statistical analysis of data, formulate correct conclusions.

Pełny opis: (tylko po angielsku)

The aim of the exercises is to familiarize students with the basic concepts of statistics, descriptive, useful in social and economic sciences. The purpose of the exercises is to provide students with basic concepts, tools and statistical methods with which they will be able to: collect and compile statistical material, conduct statistical analysis of data, formulate correct conclusions

 The main issues of the course:

1. Structure of the statistical survey - scope of the statistical survey, subject of the statistical survey, purpose of the statistical survey, sequential structure of the statistical survey

2. Basic concepts and distributions: population, data matrix, statistical variable, statistical variable distribution, joint distribution of two variables, conditional distribution, forms of statistical distribution presentation

3. Types of variables - measuring scales, transformations on measuring scales

4. Basic parameters of the value level: minimum value, maximum value, arithmetic average, modal value, median, quantiles. Properties of value level parameters

5. Basic dispersion parameters: variance, median average deviation, modal error, range. Properties of scattering parameters and the scope of their application

6. Standardization of the statistical variable, properties and applications,

7. Big theorem on decomposition of variance and its application. Parameters of conditional distributions as statistical variables. Aggregation of conditional parameters. The variance distribution theorem

8. Function parameters of two variables. Basic functions of two variables and their parameters: sum, difference and product of two variables. Covariance and its properties

9. Stochastic relationship. Independence and stochastic dependence. Maximum stochastic relationship (functional dependence).

10. Description errors and interpretation of parameters of one variable distribution. The problem of optimal statistical description.

11. Type I regressions and corresponding measures of statistical dependency. Modal regression, median regression, mean regression. Measurement of the strength of statistical dependence. Correlation ratio and dependency strength for the first type of regression

12. Linear regression type II and correlation coefficient. Determination of linear regression by the method of least squares, interpretation of linear regression coefficients. Linear correlation coefficient and its properties

Efekty uczenia się: (tylko po angielsku)

Knowledge

KP6_WG15 can characterize particular techniques of data collection and methods of analysis

Skills:

KP6_UW11 - is able to analyse collected quantitative and qualitative data, also with the use of specialized software

Verification methods:

KP6-WG15 - final test, interviews with students, tasks checking knowledge

KP6_UW11 - final test, interviews with students, tasks checking knowledge

Metody i kryteria oceniania: (tylko po angielsku)

The lecture is conducted in the form of a multimedia presentation, illustrated with examples of applications of statistical concepts and methods (in social practice and scientific activity)

During the exercises, students solve tasks that test general knowledge and shape the ability to think analytically; learn practical applications of statistical methods in social life.

Completing the lecture : written test verifying acquired skills and competences

Completion of classes: written test of theory and tasks.

Zajęcia w cyklu "Rok akademicki 2022/23" (zakończony)

Okres: 2022-10-01 - 2023-06-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Ćwiczenia, 30 godzin więcej informacji
Koordynatorzy: Łukasz Kiszkiel
Prowadzący grup: (brak danych)
Lista studentów: (nie masz dostępu)
Zaliczenie: Zaliczenie na ocenę
Skrócony opis: (tylko po angielsku)

The aim of the course is to familiarize students with the basic concepts of statistics, descriptive, useful in social and economic sciences. The course is to teach students how to verify and interpret statistical data in standard situations. The purpose of the lecture and exercises is to provide students with basic concepts, tools and statistical methods through

which they will be able to: collect and compile statistical material, conduct statistical analysis of data, formulate correct conclusions.

Pełny opis: (tylko po angielsku)

The aim of the exercises is to familiarize students with the basic concepts of statistics, descriptive, useful in social and economic sciences. The purpose of the exercises is to provide students with basic concepts, tools and statistical methods with which they will be able to: collect and compile statistical material, conduct statistical analysis of data, formulate correct conclusions

 The main issues of the course:

1. Structure of the statistical survey - scope of the statistical survey, subject of the statistical survey, purpose of the statistical survey, sequential structure of the statistical survey

2. Basic concepts and distributions: population, data matrix, statistical variable, statistical variable distribution, joint distribution of two variables, conditional distribution, forms of statistical distribution presentation

3. Types of variables - measuring scales, transformations on measuring scales

4. Basic parameters of the value level: minimum value, maximum value, arithmetic average, modal value, median, quantiles. Properties of value level parameters

5. Basic dispersion parameters: variance, median average deviation, modal error, range. Properties of scattering parameters and the scope of their application

6. Standardization of the statistical variable, properties and applications,

7. Big theorem on decomposition of variance and its application. Parameters of conditional distributions as statistical variables. Aggregation of conditional parameters. The variance distribution theorem

8. Function parameters of two variables. Basic functions of two variables and their parameters: sum, difference and product of two variables. Covariance and its properties

9. Stochastic relationship. Independence and stochastic dependence. Maximum stochastic relationship (functional dependence).

10. Description errors and interpretation of parameters of one variable distribution. The problem of optimal statistical description.

11. Type I regressions and corresponding measures of statistical dependency. Modal regression, median regression, mean regression. Measurement of the strength of statistical dependence. Correlation ratio and dependency strength for the first type of regression

12. Linear regression type II and correlation coefficient. Determination of linear regression by the method of least squares, interpretation of linear regression coefficients. Linear correlation coefficient and its properties

Literatura: (tylko po angielsku)

Stowell, Sarah (2014). Using R for Statistics. Apress. https://link.springer.com/book/10.1007%2F978-1-4842-0139-8

Bruce, P. C., & Bruce, A. (2017). Practical Statistics for Data Scientists : 50 Essential Concepts (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1517577

Fox, John. 2016 Applied Regression Analysis and Generalized Linear Models. 3rd Edition.

Angrist, Joshua D. and J¨orn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An

Empiricist’s Companion. Princeton University Press.

Suggested:

Blitzstein and Hwang. 2014. Introduction to Probability (available online through the library)

• Aronow and Miller 2017. Foundations of Agnostic Statistics (available through Blackboard).

August 29, 2017 edition.

• Grolemund and Wickham. 2017. R for Data Science (available online for everyone)

• Hern´an, Miguel A. and James M. Robins. 2012. Causal Inference. Forthcoming, Cambridge

University Press. (Note that this book is still being written and you can find draft PDFs on

the linked page above.)

• Imbens and Wooldridge (2008) “Recent Developments in the Econometrics of Program Evaluation”

• A variety of papers, book components will be assigned as well, available on the web.

Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet w Białymstoku.
ul. Świerkowa 20B, 15-328 Białystok tel: +48 85 745 70 00 (Centrala) https://uwb.edu.pl kontakt deklaracja dostępności USOSweb 7.0.3.0 (2024-03-22)