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Statistics for biologists

Informacje ogólne

Kod przedmiotu: WB.SD-09 Kod Erasmus / ISCED: (brak danych) / (0511) Biologia
Nazwa przedmiotu: Statistics for biologists
Jednostka: Wydział Biologii
Grupy: Przedmioty dla programu studiów III stopnia w dziedzinie nauki biologiczne, dyscyplinie: biologia
Przedmioty dla programu studiów III stopnia w dziedzinie nauki biologiczne, dyscyplinie: biologia
Przedmioty dla programu studiów III stopnia w dziedzinie nauki biologiczne, dyscyplinie: biologia
Punkty ECTS i inne: 4.00
Język prowadzenia: angielski

Zajęcia w cyklu "Semestr letni 2019/2020" (jeszcze nie rozpoczęty)

Okres: 2020-02-24 - 2020-06-14
Wybrany podział planu:


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Typ zajęć: Ćwiczenia, 15 godzin, 20 miejsc więcej informacji
Konwersatorium, 30 godzin, 20 miejsc więcej informacji
Koordynatorzy: Paweł Koteja
Prowadzący grup: Paweł Koteja
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Egzamin
Efekty kształcenia:

• Knowledge

The student understands the theoretical framework for statistical methods applied to the biological sciences, specifically methods based on general linear models and the least squares estimation (regression and correlation analyses and analyses of variance and covariance), distinguish between types of factors in experimental/quasi-experimental designs (manipulative vs. classification, fixed vs. random) and types of data and experimental structures (factorial vs. hierarchical; multi-factor vs. multivariate).


• Skills

The student can:

 Identify an appropriate method of statistical analysis for a given problem, experimental design and type of data, describe the problem in terms of adequate statistical model, and indicate appropriate methods of hypotheses testing;

 Effectively use a spreadsheet computer program, e.g., Excel, to prepare a well-organized database, and a package of statistical software such as Statistica, SAS, or R to perform statistical analyses for the above-mentioned models

 Evaluate data and results using critical thinking skills

 Present the results of empirical research in the form of well-organised, clearly written argumentative essays/reports/oral presentations that are supported by strong evidence and assisted by multimedia tools


• Social competence and attitude

The student:

 Effectively collaborates with other students in finding appropriate analytical methods, analysing results, and preparing written reports and oral presentations

 Accept the need for compliance with methodological requirements for the design of research plans and the interpretation of results of empirical studies


Wymagania wstępne:

Prerequisites

• Theoretical knowledge and practical skills in applying statistical methods at the basic level:

o understanding basic concepts (types of measurement scales, probability, random event, distribution of random variable, basic types of distributions, central limit theorem, expected value, measures of central tendency and dispersion, variance, standard deviation, standard error, confidence interval, parameter, estimator, statistical test, statistical null and alternative hypotheses, type I and type II errors, significance level);

o understanding and practical ability to perform basic statistical analyses (Student's-t test for comparing means and its non-parametric alternatives, chi-square test for testing goodness of fit and independence of distributions, simple analysis of regression and correlation, simple analysis of variance).

• Ability to efficiently use Excel and at least basic skills in using one of widely used packages of statistical software (e.g., SAS, SPSS, Statistica, R)


NOTE: The entrance conditions will be checked during the first class. Students who are not sure about their skills in statistics are advised to consult participation in the course with the lecturer before the course begins.


Forma i warunki zaliczenia:

• Conditions of passing practical classes and gaining admission to the final exam:

- active participation in seminars;

- accepted reports from individual/group homework;

- the final score for practical classes is pass/fail.

• Conditions of passing the final exam:

>50% points from each of the two parts of the exam;

• Scale of final grade (5 to 2 scale and corresponding A – F scale):

- < 50%: 2.0 (Fail)

- 50 - 59.9%: 3.0 (E)

- 60 - 69.9%: 3.5 (D)

- 70 - 79.9%: 4.0 (C)

- 80 - 89.9%: 4.5 (B)

- ≥90%: 5.0 (A)


Metody sprawdzania i kryteria oceny efektów kształcenia uzyskanych przez studentów:

• Evaluation of quality of presentations on seminars;

• Evaluation of reports from practical classes (pass/fail - the reports must meet the required level of quality);

• Short quizzes (checking theoretical knowledge);

• Final exam consisting of two parts:

- paper-based: checking theoretical knowledge – finding appropriate method of statistical analysis for a given problem, constructing adequate statistical model, discussion of assumptions, choice of proper tests;

- computer-based: performing complete data analysis for a given problem and a set of empirical results.


Metody dydaktyczne:

• Lectures: 3h x 5 weeks

• Seminars: 3h x 5 weeks

• Practical classes on computers: 3h x 5 weeks

• Individual and small-group homework;

• Individual or small group consultations.


Bilans punktów ECTS:

• Participation in lectures: 15h

• Participation in seminars: 15 h

• Participation in practical classes: 15 h

• Homework (self-learning, analyses of experimental designs, preparing presentations of case studies for seminars): 55 h

• Individual consultations: 2 h

• Preparing for the final exam: 20 h

• TOTAL: 122 h


Pełny opis: (tylko po angielsku)

• Introductory lectures:

 repetition of the basics of statistical methods;

 overview of types research designs and data, and relevant methods of statistical analyses;

 theoretical basis of the General Linear Model and the least-squares estimation; analysis of regression and correlation, analysis of variance and covariance; fixed, random, and mixed models of ANOVA;factorial, hierarchical, repeated measures, and combined designs; multiple comparisons (a priori and a posteriori tests).

• Seminars: The choice of particular methods discussed during the course will be adjusted to the needs of participating students. Preliminary list of proposed subjects (not all of the methods will be discussed each year, and others can be added) includes:

 Selected models within the framework of Generalized Linear Model: mixed models, logistic regression;

 Selected methods of multivariate (multidimensional) analyses: MANOVA, discriminant analysis, principal component analysis, factor analysis, clustering methods, path analysis;

 Time series analyses and analysis of rhythmicity (periodograms).

 Computer intensive methods: randomization tests, bootstrap, jackknife.

 Meta-analysis

• Practical classes: applying selected methods in analyses of example datasets (note: this is not a regular course of using a particular statistical package).

Literatura: (tylko po angielsku)

Base:

- G. Quinn and M. Keough: Experimental design and data analysis for biologists. Cambridge U. Press (2002).

- Manuals and handbooks of statistical software packages (SAS, Statistica, R).

Additional:

Handbooks of statistical methods for students who need a repetition of basics of statistical analyses, e.g.:

- R. Sokal and J. F. Rohlf: Biometry. Freeman (1989 or a newer edition).

- A. Łomnicki: Wprowadzenie do statystyki dla przyrodników. PWN (1999 or a newer edition)

- G.A. Ferguson i Y. Takane: Analiza statystyczna w psychologii i pedagogice. PWN (1997).

Handbooks focused on advanced issues:

• Borenstein M., Hedges L. V., Higgins J. P. T., Rothstein H. R. Introduction to meta-analysis. Wiley-Blackwell (2009).

• Chatfield, C. The Analysis of Time Series. Chapman & Hall/CRC (2004).

• Edwards, A. L. An introduction to linear regression and correla-tion. Freeman (1984).

• Edwards, A. L. Multiple regression and the analysis of variance and covariance. Freeman (1985).

• Galwey N. W. Introduction to mixed modelling: beyond regres-sion and analysis of variance. Wiley (2006).

• Harvey P. H.; Pagel, M. D. The Comparative Method in Evolu-tionary Biology. Oxford University Press (1991)

• Littell R., Milliken G., Stroup W., Wolfinger R., Schabenberger O. SAS for mixed models. SAS Press (2006).

• Ludwig, J.A., Reynolds, J. F. Statistical ecology: a primer on methods and computing. Wiley (1988).

• Manly B. F. J. Multivariate statistical methods: a primer. Chap-man & Hall (2004).

• Manly B. F. J. Randomization, bootstrap and Monte Carlo meth-ods in biology. Chapman & Hall (2007).

• Piegorsch, W. W., Bailer, A. J. Analyzing Environmental Data. Wiley (2005).

• Shipley, B. Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference. Cambridge University Press (2002).

Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Jagielloński w Krakowie.