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2017/01/27 15:29 ufal 2017/01/12 10:05 ufal 2017/01/12 10:04 ufal 2017/01/12 10:04 ufal created Go Next revision		Previous revision 2017/01/27 15:29 ufal 2017/01/12 10:05 ufal 2017/01/12 10:04 ufal 2017/01/12 10:04 ufal created Go Last revision Both sides next revision
cost-training-school-2017:synopsis_sc [2017/01/12 10:04] ufal created		cost-training-school-2017:synopsis_sc [2017/01/12 10:05] ufal
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-	**** Statistics in linguistics - basics and case examples ****	+	==== Statistics in linguistics - basics and case examples ====
	//(Silvie Cinková)//		//(Silvie Cinková)//
	The tutorial seeks to provide students with a basic understanding of data analysis applied to a particular linguistic data set and to a set of working hypotheses concerning the association between genre and discourse structure, using a few common statistical methods.		The tutorial seeks to provide students with a basic understanding of data analysis applied to a particular linguistic data set and to a set of working hypotheses concerning the association between genre and discourse structure, using a few common statistical methods.
-	The dataset contains annotations of discourse connectors extracted from the Prague Dependency Treebank 3.0. The individual occurrences of discourse connectors are annotated with two different label sets (“discourse type” and “discourse class”). In addition, the data contains sentence ID and information about the genre and size of the document for each occurrence. This data set will be used to exemplify how to:	+	The dataset contains annotations of discourse connectives extracted from the Prague Dependency Treebank 3.0. The individual occurrences of discourse connectives are annotated with two different label sets (“discourse type” and “discourse class”). In addition, the data contains sentence ID and information about the genre and size of the document for each occurrence. This data set will be used to exemplify how to:
	1. describe and summarize the data set, as well as prepare it for further statistical analysis (key words: "tidy data" and "data wrangling")		1. describe and summarize the data set, as well as prepare it for further statistical analysis (key words: "tidy data" and "data wrangling")

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