Introductory statistics

General data

Course ID:	3502-WDSTAT
Erasmus code / 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) Sociology and cultural studies The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Introductory statistics
Name in Polish:	Wprowadzenie do statystyki
Organizational unit:	Institute of Sociology
Course groups:	(in Polish) Ćwiczenia
ECTS credit allocation (and other scores):	(not available) 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
Type of course:	obligatory courses
Mode:	Remote learning
Short description:	The course allows participants to learn and understand the basic concepts of statistical description and inference: Population, variables, size and frequency distributions, total distributions, conditional distributions, value and dispersion level parameters; Relationships between two variables: type I and II regressions, stochastic relationship, strength of statistical relationship, correlation ratio, correlation coefficient, rank correlation coefficients; Relationships between multiple variables: multiple regression, partial correlation; Random variable, normal distribution, statistics from sample distribution; Statistical inference: point and interval estimation, hypothesis verification.
Full description:	It is a core course during which students gradually learn the basics of statistical analysis. The knowledge and skills developed in the class are necessary both for the understanding of quantitative research conducted and published by various institutions, and for independent planning of such research and analysis of their results. Subsequent classes introduce students to the language of statistics and enable them to understand the concepts used in statistical description and inference. During the course, the commonly used statistical parameters are defined and discussed, and much time is spent on the problems of importance and interpretation of the calculated parameters. Since correct interpretation rest on in-depth understanding, presentation of the discussed parameters and the relationships between them is conducted using elementary examples, which are arithmetically simplified so that students can focus on the important propensities of the concepts without wasting time on complicated calculations. Reference to factual empirical data comes later in the course, after the language of statistics is sufficiently mastered. The issue of statistical inference is related not only to statistics but also to the broader rules of correct reasoning. During the course, students will become familiar with such concepts as hypothesis, error of first and second kind, estimator, etc. The statistical analyses conducted in this part are based on the basic concepts of probability calculus. Some introduction to calculus will be offered, but students who have not had it before will have to spend some time getting familiar with it. The team of course teachers offers assistance in finding proper reading in this area. The language of statistics is formal even though it frequently uses the concepts occurring also in everyday language. Acquisition of this language demands systematic and independent work on the part of the student, although it does not initially require mathematical knowledge beyond that acquired in high school. For this reason, in addition to attending classes students will be asked to do home assignments regularly. These assignments are seen as mandatory and will be given and graded in the manner determined by the course teacher. In order to facilitate students; independent work, the teaching team has prepared a set of statistical problems, which is available online and constantly enlarged
Bibliography:	Grzegorz Lissowski, Jacek Haman, Mikołaj Jasiński: Podstawy statystyki dla socjologów
Learning outcomes:	Knows basic methods and techniques of social research and can choose appropriate methods to solve basic research problems Understands the specificity of sociological analysis Knows how to plan and carry out a simple quantitative and qualitative study Has basic applied knowledge of statistical description and inference Can record and observe social phenomena in a methodologically correct way Can plan and carry out a social study using basic quantitative and qualitative research methods and techniques Can interpret simple social phenomena using basic statistical methods
Assessment methods and assessment criteria:	Final written exam, progress tests and home assignments; obtaining course credit is conditional on not exceeding the limit of two absences in a semester. Minimum 50% points from Internet Home Assigments, among this not less than 40% of the assigments carried out in the second semester. The condition of admission to the zero exam after the first semester is also obtaining at least 50% of points from online homework from the first semester, closed before the exam date. The retake exam takes place in the same way as the exam on the first date

This course is not currently offered.

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