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This course is the follow-up to the Applied Statistics lectures. Freely available statistical software R is presented, and students use it to practise common statistical tasks such as, e.g., exploratory data analysis, hypothesis testing, analysis of variance or regression.
Last update: Hladíková Jana (04.01.2018)
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Students will be able to: Use the R software (environment for statistical computing) Do the exploratory analysis and hypotheses testing in R Do the regression analysis in R Analyze multidimensional data in R Present results of statistics analysis using graphical capabilities of R
Last update: Hladíková Jana (04.01.2018)
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The credit is given on the completion of an individual project. The final exam is written. Last update: Svozil Daniel (31.01.2018)
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R:Statisticka analýza dat v R, Vojtech Spiwok, 2015. available at https://web.vscht.cz/~spiwokv/statistika/skripta.pdf R:Pekár, S., Moderní analýza bioologických dat, Scientia, 2009, ISBN 978-80-86960-44-9. Z:Konečná, K., Koláček, J., Jak pracovat s jazykem R. 2014. PřF MU Brno (available at https://www.math.muni.cz/~kolacek/vyuka/vypsyst/navod_R.pdf) A:Venables R.W.N., Smith D.M. and the R Core Team: An Introduction to R. 2018. (available at http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) A:Cohen T., Cohen J.Y.: Statistics and Data with R: An applied approach through examples. 2008. ISBN: 9780470721896 A:Rumsey D. Intermediate Statistics for Dummies; For Dummies, 2007. ISBN 0470045205 Last update: Svozil Daniel (31.10.2018)
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Zápočet je udělen na základě vypracování individuálního projektu. Závěrečná zkouška je písemná. Last update: Svozil Daniel (31.01.2018)
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1. Introduction to R statistics software, help 2. Input and output in R 3. Creating graphs in R 4. Basic statistic distributions, random numbers in R 5. Descriptive one-dimensional statistics, boxplot 6. Standard deviation, mean error, confidence interval and their graphic representation in R 7. Hypotheses testing in R: t-test 8. Hypotheses testing in R: variance analysis 9. Descriptive statistics and their use in R 10. Creating and testing linear models in R 11. Creating and testing non-linear models in R 12. Basic linear algebra in R 13. Principal component analysis, its implementation and use in R 14. Cluster analysis in R Last update: Hladíková Jana (04.01.2018)
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Online course materials at http://e-learning.vscht.cz video course "Computing for Data Analysis" (using and programming in R) by Roger D. Peng, https://www.coursera.org/course/compdata video course "Data Analysis" (applied statistics in R) by Jeff Leek, https://www.coursera.org/course/dataanalysis
Online textbooks OpenIntro Statistics - http://www.openintro.org/stat/index.php OnlineStatBook - http://onlinestatbook.com/2/index.html Hyperstat - http://davidmlane.com/hyperstat/index.html StatPrimer - http://www.sjsu.edu/faculty/gerstman/StatPrimer/ Last update: Hladíková Jana (04.01.2018)
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Mathematics, Applied Statistics Last update: Hladíková Jana (04.01.2018)
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Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 1 | 28 | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 1 | 28 | ||
Práce na individuálním projektu | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1 | 28 | ||
Účast na seminářích | 1 | 28 | ||
5 / 5 | 140 / 140 |