Load:

1. komponenta
Lecture type  Total 
Lectures 
30 
Exercises 
15 
* Load is given in academic hour (1 academic hour = 45 minutes)

Description:

Elements of probability, conditional probability, Bayes theorem, Bayes factor, persistence as conditional probability. Random variables and vectors, mathematical expectation, joint, marginal and conditional distributions, independence. Descriptive statistics. Empirical distributions and parameter fitting. Some theoretical distributions with application in geophysics. Hypothesis testing. Simple and multiple linear regression with geometrical interpretation. Bivariate normal distribution. Basics of timeseries analysis. Tests for data homogeneity.
LEARNING OUTCOMES:
Students will be able to:
define and discuss basic probability terms
distinguish types of random variables and describe their properties
apply mathematical expectation in practice
relate the independence of random variables with the intuitive idea of independence
relate properties of random variables with statistical attributes of empirical data sets
explain and apply methods for parameter estimation
define and recognize applicability of theoretical probability distributions
critically apply statistical tests
explain and apply the method of linear regression using the geometrical
representation.

Literature:

 Wilks, D.S.: Statistical Methods in the Atmospheric Sciences, Academic Press, New Yorak, 2011.
 Penzar B., B. Makjanić: Osnovna statistička obrada podataka u klimatologiji, Sveučilište u Zagrebu, 1978.
