Statistical modelling
| 2025_FS_StatMod | |
| 17.03.2025 - 19.03.2025 | |
| 3 full days + work in between | |
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Start registration period: 21.11.2024 End of registration period: 24.02.2025 |
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| ETH Zurich, centre | |
| 7 | |
| 16 | |
| Students enrolled in PSC PhD Programs: CHF 0 LSZGS PhD students: CHF 0 All others: CHF 300 | |
| This comprehensive course is designed to equip participants with a deep understanding of linear regression and related advanced techniques using the statistical software R. Over three intensive days, we will cover essential concepts, hands-on exercises, and practical applications, ensuring that participants leave with the knowledge and skills needed to confidently apply these methods in real-world scenarios. Day 1: Introduction to Linear Regression and OLS Estimation - participants will delve into the fundamentals of linear regression, gaining insights into its principles and application. We will explore Ordinary Least Squares (OLS) estimation as a cornerstone technique for parameter estimation. Additionally, we will examine various goodness-of-fit measures and hypothesis testing to assess model accuracy. Day 2: Model Diagnostics, Robust Regression, and Variable Selection - participants will learn how to identify and address potential issues in their models. Robust regression techniques will be introduced to handle outliers and non-normally distributed data. Furthermore, we will explore variable selection methods to refine and optimize models. Day 3: Outline on advanced regression topics: Nonlinear Regression, Splines, and General Additive Models These techniques are essentially used to uncover non-linearities and improve the linear model through the insights gained from the non-linear techniques. Participants will showcase their newfound knowledge and insights in presentations. | |
| Prof. Matthias Templ | |
| 1 | |
| PhD students Postdocs if places available | |
| Basic knowledge of the R language would be ideal, but is not essential. Participants without prior knowledge in R will be sent some preparatory material in advance. Please demand it. | |
| English | |
| In order to obtain the credit points, participants are required to attend all course days and hand in an assignment to be carried out at home. The details will be ex-plained during the course. The assignment is due no later than one week after the course has ended. | |
By registering you agree to the PSC course terms and conditions AGBs | |
| Arrange cancellation with the PSC coordination office (psc_phdprogram@ethz.ch): Up to 2 weeks prior to course start without a fine. Later cancellations and incomplete attendance without documented justification will incur a fee of 200 CHF. | |
| Students are required to bring their own computers, with the latest version of R down- loaded from https://cran.r-project.org/. As an editor for R, we recommend to install the free desktop version of https://www.rstudio.com as well. | |
| Dr. Bojan Gujas (bojan.gujas@usys.ethz.ch) | |
| FS25_StatisticalModelling.pdfvertical_align_bottom |