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Title: Statistical models : lecture at the Bridging gaps: formal, computational and experimental approaches in linguistics 2023 (FEAL 2023), 2. 8. 2023, Chemnitz, Germany Authors: ID Hosseini, Ahmad (Author) Files: https://sites.google.com/view/feal2023-across/speakers?authuser=0   https://sites.google.com/view/feal2023-across/programme?authuser=0   https://ung.si/en/news/event/82/summer-school-formal-computational-and-experimental-approaches-in-linguistics-2023-feal-2023/   Language: English Work type: Unknown Typology: 3.15 - Unpublished Conference Contribution Organization: UNG - University of Nova Gorica Abstract: As a collaboration among the University of Nova Gorica (Slovenia), University of Craiova (Romania), and University of Udine (Italy), FEAL 2023 represents an interdisciplinary training opportunity for BA, MA and PhD students from linguistics, applied mathematics, psychology, and neuroscience. It addresses research topics in contemporary linguistics through formal, computational, and experimental approaches. In this training opportunity, we will explore some of the most fundamental General Linear Models (GLMs) that have a wide range of applications in various fields. GLMs provide a flexible framework for modeling a variety of data types, including continuous, binary, count, and categorical data, making them an essential tool for any data analyst. During the course, we will briefly cover some GLM models that have applications in diverse fields such as Psychology, Linguistics, Viticulture, Neuroscience, Economics, Biology, and beyond. We will review the basic concepts and tools in GLMs and will discuss how to choose the appropriate model for a given data type and how to interpret the model's output. This course will provide a hands-on learning experience, where you will have the opportunity to apply the concepts you learn to real-world datasets (using SPSS and Excel). We will go as long as time permits to cover as many topics as possible and ensure that you have a good understanding of GLM models and the skills to apply them to your own research projects. Keywords: Experimental Linguistics, Statistical Models, Mathematical and Computational Linguistics, General Linear Models (GLMs) Year of publishing: 2023 PID: 20.500.12556/RUNG-9587  COBISS.SI-ID: 220961795  UDC: 519.2 NUK URN: URN:SI:UNG:REP:P8GVYOIX Publication date in RUNG: 06.01.2025 Views: 176 Downloads: 3 Metadata:    Cite this work Plain text BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago 17th Author-Date Harvard IEEE ISO 690 MLA Vancouver : Copy citation

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