Subjects

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Statistical methods in meteorology - NAFY041

Title:	Statistické metody v meteorologii
Guaranteed by:	Department of Condensed Matter Physics (32-KFKL)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2011 to 2019
Semester:	summer
E-Credits:	6
Hours per week, examination:	summer s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time
Note:	enabled for web enrollment

Guarantor:	doc. RNDr. Jaroslava Kalvová, CSc. RNDr. Eva Holtanová, Ph.D. doc. Mgr. Jiří Mikšovský, Ph.D.

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: T_KMOP (18.05.2011)

The subject will provide students with knowledge of basic skills in statistical data analysis. The attention will be paid to fundamental concepts of probability calculus, descriptive statistics, probability distributions and parameter estimates, hypothesis testing, linear correlation and linear regression.

Literature -

Last update: RNDr. Eva Holtanová, Ph.D. (01.09.2011)

Wilks, D.S.: Statistical Methods in the Atmospheric Science. Academic Press, San Diego, Academic Press, San Diego, 1995.

Anděl, J.: Statistické metody. MATFYZPRESS, Prah, 1998.

Meloun, M., Militký, J.: Statistická analýza experimentálních dat. Academia, Praha, 2004.

Teaching methods -

Last update: RNDr. Eva Holtanová, Ph.D. (01.09.2011)

Lectures and seminar with active participation of the students (data processing).

Syllabus -

Last update: RNDr. Eva Holtanová, Ph.D. (01.09.2011)

The event, relationships between events, definition of probability.

Descriptive statistics, measures of location and variability, asymmetry and skewness of distribution. Sample covariance, sample correlation coefficient (Pearson’s, Spearman’s), assumptions for their use.

Random variable, distribution function. Continuous and discrete distributions. Continuous distributions - uniform, Gaussian, lognormal, gamma, beta, Chi-square, Student, Fisher, Gumbel, Weibul, GEV. Discrete distributions - binomial, Poisson.

Chebyshev’s inequality, law of large numbers, central limit theorem.

Criteria of estimation, maximum-likelihood method, moments estimation. Probability plots, P-P plot, Q-Q plot. Confidence intervals. Parametric and nonparametric tests of hypothesis. Test for a population mean, test for a population variance. Tests of normality. Tests of the correlation coefficient. Nonparametric tests (sign tests, one-sample and two-sample Wilcoxon rank tests, tests for paired samples). Goodness of fit tests (Kolmogorov-Smirnov, Chi-square test). Contingency tables.

Simple linear regression model, assumptions, parametre estimation, least squares method. Analysis of variance, coefficient of determination, tests and confidence intervals for the regression parameters.

Multiple linear regression and its evaluation. Principal component analysis.

Stochastic processes, basic definitions, covariance and correlation function, stationary processes.