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Continuation of the course NMSA407 Linear Regression. This course covers regression models for non-normal
data, discrete distributions, and clustered data. The practice sessions include solutions to theoretical excercises but
the focus is on analyses of different types of econometric, medical and technical data. The course is concluded by
a Final Project.
Last update: G_M (28.05.2013)
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To explain regression models for non-normal and/or correlated data. Last update: T_KPMS (07.05.2015)
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The exercise class credit is necessary to sign up for the exam. The credit for the exercise class will be awarded to the student who hands in a satisfactory solution to each assignment by the prescribed deadline. The nature of these requirements precludes any possibility of additional attempts to obtain the exercise class credit. Last update: Zichová Jitka, RNDr., Dr. (23.04.2018)
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J.W. Hardin and J.M. Hilbe: Generalized Linear Model and Extensions. StataPress, 2007. A. Agresti: Categorical Data Analysis. Wiley, 1990. J.W. Hardin and J.M. Hilbe: Generalized Estimating Equations. Chapman & Hall, 2003. P.J. Diggle, K.Y. Liang, S.L. Zeger: Analysis of Longitudinal Data. Oxford University Press, 1994.
Last update: T_KPMS (12.05.2014)
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Lecture+exercises. Last update: T_KPMS (12.05.2014)
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Only for summer 2020: if conduct of an oral exam is impossible, oral part can be performed by distant methods or waived.
The exam has two parts: (1) Evaluation of applied project report and (2) Theoretical oral part. To pass the exam, both parts need to be passed.
Requirements for the exam comprise the entire contents of the lectures and exercise sessions. Last update: Kulich Michal, doc. Mgr., Ph.D. (30.04.2020)
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1. Generalized linear model 2. Binary response regression 3. Loglinear model 4. Extensions of generalized linear model, quasilikelihood, sandwich estimator of variance 5. Generalized estimating equations 6. Linear mixed effects model 7. Generalized linear mixed effects model
Last update: Kulich Michal, doc. Mgr., Ph.D. (04.02.2018)
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This course assumes mid-level knowledge of linear regression (both theory and applications) and good understanding of maximum likelihood theory. Last update: Kulich Michal, doc. Mgr., Ph.D. (25.05.2018)
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