University of Warsaw - Central Authentication System

(in Polish) Advanced Regression

General data

Course ID:	2500-PL-PS-SP15-10
Erasmus code / ISCED:	14.4 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. / (0313) Psychology The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	(unknown)
Name in Polish:	Advanced Regression
Organizational unit:	Faculty of Psychology
Course groups:
ECTS credit allocation (and other scores):	4.00 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.
Language:	English
Prerequisites (description):	(in Polish) Kurs dla specjalizacji 315
Short description:	The course is intended for students that want to learn ordinary least squares (OLS)analysis in order to apply it in their own research and/or to evaluate the work of others. The course will consist of 20h of theoretical lecture and 10h of practical workshop.  The focus of LECTURE part will be on reviewing logic of assumptions, possibilities and limitations of OLS regression analysis.  The focus of WORKSHOP part will be on practical issues such as selecting the appropriate analysis, preparing data for analysis, interpreting results, and presenting results of a complex nature. The primary goal of the course is to develop an applied and intuitive understanding of the covered statistical material.
Learning outcomes:	Students  Will learn the basics assumptions and logic behind OLS regression analysis  Will develop skills with a range of practical procedures in order to diagnose and prepare data, build model and eventually run OLS regression analysis.  get acquainted with the statistical computing system SPSS (R-studio) and with its use for manipulation and analysis of statistical data. NOTE: primary examples will be done in SPSS, however I’ll introduce some examples in R. You are allowed (and encouraged) to do all your stat homework in R-studio

Classes in period "Summer semester 2023/24" (in progress)

Time span:	2024-02-19 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable MO TU W TH CW FR
Type of class:	Classes, 30 hours more information
Coordinators:	(unknown)
Group instructors:	Maciej Bieńkowski, Mikołaj Winiewski
Students list:	(inaccessible to you)
Examination:	Course - Grading Classes - Grading
Full description:	The course is intended for students that want to learn ordinary least squares (OLS)analysis in order to apply it in their own research and/or to evaluate the work of others. The course will consist of 20h of theoretical lecture and 10h of practical workshop.  The focus of LECTURE part will be on reviewing logic of assumptions, possibilities and limitations of OLS regression analysis.  The focus of WORKSHOP part will be on practical issues such as selecting the appropriate analysis, preparing data for analysis, interpreting results, and presenting results of a complex nature. The primary goal of the course is to develop an applied and intuitive understanding of the covered statistical material. Covered material will deal mostly with Multiple Regression analysis including:  Model specification and interpretation  Diagnostics (tests and plots)  Analysis of residuals and outliers  Multicollinearity  Autocorrelation  Hierarchical models  Dummy (dichotomous) independent variables  Dummy coding of nominal variables  Inducing linearity by nonlinear transformations of independent variables  Multiplicative Interaction terms  Mediation analysis Specific topics and amount of covered material will depend in part on the interests of the students and class progress.
Bibliography:	Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge

Course descriptions are protected by copyright.
Copyright by University of Warsaw.