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Last update: RNDr. Jitka Zichová, Dr. (10.05.2021)
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Last update: RNDr. Jitka Zichová, Dr. (04.06.2021)
To explain basics of regression models. |
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Last update: doc. RNDr. Michal Pešta, Ph.D. (24.01.2022)
The subject is finalized by a course credit and exam. To be able to take exam, it is necessary to obtain a course credit first.
Course credit requirements:
The nature of these requirements precludes any possibility of additional attempts to obtain the exercise class credit. |
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Last update: doc. RNDr. Michal Pešta, Ph.D. (24.01.2022)
JAMES, G.; WITTEN, D.; HASTIE, T.; TIBSHIRANI, R. An Introduction to Statistical Learning (with Applications in R), 2nd edition. Springer: New York, NY, 2021, xv+607 s. ISBN: 978-1-0716-1417-4.
KHURI, A.I. Linear Model Methodology. Chapman & Hall/CRC: Boca Raton, 2010, xx+542 s. ISBN: 978-1-58488-481-1.
ZVÁRA, K. Regrese. Matfyzpress: Praha, 2008, 253 s. ISBN: 978-80-7378-041-8. |
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Last update: RNDr. Jitka Zichová, Dr. (10.05.2021)
Lecture + exercises. |
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Last update: doc. RNDr. Michal Pešta, Ph.D. (24.01.2022)
Exam is oral and it is composed of two parts:
Problems assigned during exam are based on topics presented during lectures and also correspond to topics covered by exercise classes. Assigned problems correspond to the syllabus into extent covered by lectures.
Exam grade will be based on point evaluation of the written course credit homework and evaluation of the oral part. |
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Last update: doc. RNDr. Michal Pešta, Ph.D. (24.01.2022)
Linear regression model. Least squares method. Coefficient of determination. Quantitative and qualitative regressors, interactions and their interpretations. Analysis of residuals a regression diagnostics. Submodel testing, model building. Logistic regression. |
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Last update: doc. RNDr. Michal Pešta, Ph.D. (24.01.2022)
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