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Analyzing_Linguistic_Data

Spring 2012 Instructors: Colin Bannard. Tuesday, Thursday 11-12:30 PAR 10. Many areas of linguistics require statistical analysis: phonetics and phonology, psycholinguistics, computational linguistics, or empirical studies in syntax or semantics. Statistical analysis helps detect regularities in the numbers, test whether observed effects are statistically significant, and implement and test hypotheses about the data. Back to the UTCL website.

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Spring 2012 Instructors: Colin Bannard. Tuesday, Thursday 11-12:30 PAR 10. Many areas of linguistics require statistical analysis: phonetics and phonology, psycholinguistics, computational linguistics, or empirical studies in syntax or semantics. Statistical analysis helps detect regularities in the numbers, test whether observed effects are statistically significant, and implement and test hypotheses about the data. Back to the UTCL website.
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Analyzing_Linguistic_Data | analyzing-linguistic-data.utcompling.com Reviews

https://analyzing-linguistic-data.utcompling.com

Spring 2012 Instructors: Colin Bannard. Tuesday, Thursday 11-12:30 PAR 10. Many areas of linguistics require statistical analysis: phonetics and phonology, psycholinguistics, computational linguistics, or empirical studies in syntax or semantics. Statistical analysis helps detect regularities in the numbers, test whether observed effects are statistically significant, and implement and test hypotheses about the data. Back to the UTCL website.

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The R project page. At the first page you get to, you first have to choose your mirror, i.e. the webpage closest to you from which you will download R. You can get the languageR package. From here, or install it somewhat more comfortably from within your R shell. To search for all things R, use RSeek. Baayen's Analyzing Linguistic Data: A Practical Introduction to Statistics Using R is also available as a. Useful R code snippets. More on correlation and causation. And yet another linear regression problem.

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Schedule - Analyzing_Linguistic_Data

http://analyzing-linguistic-data.utcompling.com/home/schedule

Jan 19 Getting started with R. Readings: ALD ch. 1. Jan 24 Descriptive statistics: samples and distributions. Jan 26 Descriptive statistics: central tendencies, standard deviation. Readings: SE ch. 2 and 3. Graphical data exploration in R: visualizing a single vector, and central tendencies. Readings: ALD ch. 2 pp. 20-32. Feb 2 Graphical data exploration in R: two or more vectors. Readings: ALD ch. 2 pp. 32-37. File novice.txt for use with the plotting demo. Readings: ALD ch. 2 pp. 37-42. Readings: ALD c...

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ALD_Syllabus - Analyzing_Linguistic_Data

http://analyzing-linguistic-data.utcompling.com/home/ald_syllabus

Syllabus for Analyzing Linguistic Data: LIN392. Course: Analyzing Linguistic Data, LIN392. Course unique number: 40895. Course website: http:/ analyzing-linguistic-data.utcompling.com. Office hours: Tuesday 3:30-5pm, Wednesday 11-12:30am. Email: bannard at mail dot utexas dot edu. Office hours: Monday 1-3pm, Tuesday 10-11am. Email: katrin dot erk at mail dot utexas dot edu. This page serves as the syllabus for this course. Many research topics in linguistics require or can benefit from sophisticated stat...

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Courses - Katrin Erk's homepage

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LIN 350: Computational Semantics. LIN 389C: Research in Computational Linguistics. LIN 353C: Introduction to Computational Linguistics. LIN 389C: Research in Computational Linguistics. LIN 350: Analyzing Linguistic Data (undergraduate). LIN 392: Analyzing Linguistic Data (graduate). LIN 389C: Research in Computational Linguistics (instructor: Jason Baldridge). LIN 353C: Introduction to Computational Linguistics. LIN 350: Computational Semantics. LIN 392: Working with Corpora. LIN 350: Words in a Haystack.

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Analyzing_Linguistic_Data

Spring 2012 Instructors: Colin Bannard. Tuesday, Thursday 11-12:30 PAR 10. Many areas of linguistics require statistical analysis: phonetics and phonology, psycholinguistics, computational linguistics, or empirical studies in syntax or semantics. Statistical analysis helps detect regularities in the numbers, test whether observed effects are statistically significant, and implement and test hypotheses about the data. Back to the UTCL website.

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