Statistics
Informacje ogólne
Kod przedmiotu: | 440-ERS-1STS |
Kod Erasmus / ISCED: |
14.2
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Nazwa przedmiotu: | Statistics |
Jednostka: | Instytut Socjologii |
Grupy: |
3L stac.I st.studia socjologiczne - przedmioty obowiązkowe |
Punkty ECTS i inne: |
5.00
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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 |
Przejdź do planu
PN WT ŚR CZ PT |
Typ zajęć: |
Ćwiczenia, 30 godzin
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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. |
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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 |
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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. |
Właścicielem praw autorskich jest Uniwersytet w Białymstoku.