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Last update: doc. Ing. Marek Omelka, Ph.D. (30.11.2020)
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Last update: doc. Ing. Marek Omelka, Ph.D. (02.12.2020)
To explain regression models for non-normal data. |
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Last update: doc. Ing. Marek Omelka, Ph.D. (30.11.2020)
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. |
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Last update: doc. Mgr. Michal Kulich, Ph.D. (27.01.2023)
J.W. Hardin and J.M. Hilbe: Generalized Linear Model and Extensions. StataPress, 2007. A. Agresti: Categorical Data Analysis. Wiley, 1990. |
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Last update: doc. Ing. Marek Omelka, Ph.D. (30.11.2020)
Lecture+exercises. |
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Last update: doc. Mgr. Michal Kulich, Ph.D. (27.01.2023)
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. |
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Last update: doc. Ing. Marek Omelka, Ph.D. (01.12.2020)
1. Generalized linear model 2. Binary response regression 3. Loglinear model 4. Extensions of generalized linear model, quasilikelihood, sandwich estimator of variance
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Last update: doc. Ing. Marek Omelka, Ph.D. (30.11.2020)
This course assumes mid-level knowledge of linear regression (both theory and applications) and good understanding of maximum likelihood theory. |