The course is also offered to the students of the fields other than those the course is directly associated with.
Fields of study the course is directly associated with
there are 7 fields of study the course is directly associated with, display
At the end of the course the students are able to:
Define structure of dataset for statistical analysis;
Visualize the data and interpret data visualisation;
Identify correct methods of descriptive statistics;
Formulate hypothesis for statistical testing;
Select the correct statistical tests for hypotheses confirmation/refusal;
Interpret results of statistical evaluation, both analysis of own data and statistics in scientific literature;
Assess the applicability of statistical methods on various types of data.
Introduction to statistics, testing of hypotheses.
Tables of distribution functions. Sampling from biological populations, data processing.
Introduction to sampling design. Continuous, ordinal and nominal data in biology.
Distribution of continuous and bivariate variables - testing of hypotheses, graphical methods.
Application of binomial and Poisson distribution in biology.
One sample testing: sample mean, median, standard deviation, variance, binomial p and Poisson constant.
Two sample testing. Experimental design - randomized and blocked. Parametric and nonparametric methods.
Application of goodness-of-fit test in biology, analysis of R x C contingency tables, discrimination of categorical data.
Measures of similarity in ecology (covariance, correlation coefficients, similarity coefficients).
Analysis of variance (ANOVA): one-way and two-way model.
Simple linear regression. Linear regression. Introduction to multivariate linear regression.
Experimental design: one-way and two-way models; factorial design, randomized blocks. Laboratory and field trials. Nested design of ANOVA in genetics and ecology. Nonparametric ANOVA.
Petrie, A., Watson, P. (2006) Statistics for Veterinary and Animal Science, Wiley-Blackwell; 2nd ed