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Statistics II

General data

Course ID:	1400-113STA2
Erasmus code / ISCED:	11.204 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (unknown)
Course title:	Statistics II
Name in Polish:	Statystyka II
Organizational unit:	Faculty of Biology
Course groups:	Requisite courses for second-year students of Biology Requisite subjects for second-year students of Biotechnology
ECTS credit allocation (and other scores):	2.00 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:	Polish
Main fields of studies for MISMaP:	biology biotechnology environmental protection
Type of course:	obligatory courses
Mode:	Classroom
Short description:	Two-way analysis of variance - fixed (I), random (II) and mixed model (III). Nested analysis of variance (random and mixed model), estimation of variance components. Basic experimental designs, assessment and interpretation of the results, main effects and interactions. Linear regression with one or several predictor variables, for experimental and observational data. Partial correlation. Analysis of covariance. Selected analyses using statistical procedures of the SAS System.
Full description:	(1) Two-way analysis of variance (ANOVA). Fixed effects model, random effects model, and mixed model - differences concerning scope of inference and the form of test statistic. Balanced and unbalanced designs, sums of squares of different types. (2) Hierarchical (nested) analysis of variance. Estimation of variance components in the case of one-way classification (model II) and for nested classification. (3) Common experimental designs and corresponding ways to decompose total variation: complete randomization design, complete randomized blocks, Latin square, split-plot design. (4) Linear regression with one predictor variable. Partial correlation, its connection with regression and simple correlations. Regression equation as a result of causal relationship (in experimental studies) and as a prediction tool. Multiple regression. (5) Analysis of covariance (ANCOVA) as a combination of analysis of variance and regression. Covariates and adjusted means, some applications. More general view on the analysis of variance, regression, and analysis of covariance - general linear model. (6) Some SAS software procedures for the analysis of variance, correlation and regression.
Bibliography:	The course does not follow any particular textbook. The books listed below cover substantially wider scope, but selected chapters may be useful. Fisher L.D., van Belle G. 1993. Biostatistics. Wiley. Sokal R.R., Rohlf F.J. 1995. Biometry. Freeman. Zar J.H. 1999. Biostatistical analysis. Prentice Hall.
Learning outcomes:	(in Polish) Student: 1) Wykazuje znajomość zaawansowanych metod statystycznych pozwalających na opisywanie zjawisk przyrodniczych. (biol K_W01; bt K_W03; os K_W01) 2) Stosuje adekwatne metody statystyczne do opisu wyników prowadzonych doświadczeń. (biol K_U08; bt K_U05; os K_U02) 3) Wykazuje akceptująca postawę wobec metod statystycznych oraz odczuwa potrzebę stałego aktualizowania wiedzy z zakresu statystyki. (biol K_K04; bt K_K02; os K_K02) 4) Zna i rozumie zasady metodologii nauk przyrodniczych oraz wykazuje umiejętność poprawnego wnioskowania w oparciu o statystyczną analizę danych. (biol K_W02; bt K_U06; os K_U09) 5) Wykazuje zrozumienie zjawisk przyrodniczych, krytycznie analizuje i interpretuje informacje pojawiające się w środkach masowego przekazu w świetle obowiązujących teorii naukowych. (biol K_K08; bt K_K01; os K_U01) 6) Zna zaawansowane metody statystycznej analizy danych. (biol K_W16)
Assessment methods and assessment criteria:	(in Polish) W trakcie semestru studenci otrzymują zadania domowe związane z kolejnymi zagadnieniami, o tej samej ogólnej treści, lecz zindywidualizowane pod względem szczegółów. Odbywają się dwa kolokwia - w połowie semestru i po jego zakopńczeniu. Zaliczenie na ocenę, na podstawie sumy wszystkich zdobytych punktów, 60% daje gwarancję zaliczenia.W punktacji uwzględniony jest podział zadań na podstawowe i dodatkowe.

Classes in period "Winter semester 2024/25" (past)

Time span:	2024-10-01 - 2025-01-26	Selected timetable range: weekly course term Go to timetable MO LAB LAB LAB TU LAB LAB W LAB TH LAB LAB FR
Type of class:	Lab, 30 hours more information
Coordinators:	Tomasz Wyszomirski
Group instructors:	Iwona Dembicz, Ewa Jabłońska, Agnieszka Kloch, Agnieszka Rudak, Tomasz Wyszomirski, Urszula Zawadzka-Pawlewska
Students list:	(inaccessible to you)
Credit:	Course - Grading Lab - Grading

Classes in period "Winter semester 2025/26" (future)

Time span:	2025-10-01 - 2026-01-25	Selected timetable range: weekly course term Go to timetable MO LAB LAB LAB TU LAB LAB W LAB TH LAB FR LAB
Type of class:	Lab, 30 hours more information
Coordinators:	Agnieszka Rudak, Tomasz Wyszomirski
Group instructors:	Iwona Dembicz, Agnieszka Kloch, Agnieszka Rudak, Tomasz Wyszomirski, Urszula Zawadzka-Pawlewska
Students list:	(inaccessible to you)
Credit:	Course - Grading Lab - Grading

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