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Lecturer(s)
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Fišerová Eva, doc. RNDr. Ph.D.
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Course content
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he course will cover regression models that are widely used in solving problems related to the environment, including the modelling of indicators associated with important economic, social and environmental concepts, as well as in environmental monitoring. Simple linear regression model: least squares method, estimation, inference and prediction Multiple regression model: estimation, inference and prediction Multiple regression model: heteroscedasticity, model verification and measures of model quality Regression with qualitative explanatory variables Simple methods for panel data: pooling and differencing Advanced methods for panel data: fixed-effects and random-effects models Generalized linear model: logistic regression Generalized linear model: logistic regression for panel data
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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The aim of the course is to introduce students to regression models and their application in data analysis and in solving practical problems related to the environment and sustainable innovations.
Upon completion of the course, the student is able to: - apply basic regression models in data analysis, - assess the assumptions of a regression model, - interpret results in the context of the environment and sustainable innovations, - use regression analysis for prediction and decision-making support, - clearly present the results of the analysis.
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Prerequisites
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Basic knowledge of mathematical analysis.
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Assessment methods and criteria
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Oral exam, Seminar Work
Credit: The student actively participates in seminars, independently solves assigned tasks. Exam: The student demonstrates knowledge and understanding of the course content during the oral examination.
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Recommended literature
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Croissant, Y., Millo, G. (2018). Panel Data Econometrics with R. 1st edition. Wiley.
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Faraway, J. J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. 2nd edition. Chapman & Hall/CRC Texts in Statistical Science.
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Frost, J. (2020). Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models.
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Heiss, F. (2020). Using R for Introductory Econometrics. 2nd edition. Düsseldorf.
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Hoffmann, P. (2021). Linear Regression Models: Applications in R. 1st edition. Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences.
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Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data.
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Wooldridge, J. M. (2015). Introductory Econometrics: A Modern Approach. 6th edition. Cengage Learning.
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