Proportionate Reduction In Error Statistics
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of making observations which are possibly subject to errors of all types. Such measures quantify how much having the observations available has reduced the loss proportional reduction in error lambda (cost) of the uncertainty about the intended quantity compared with not the proportionate reduction in error is a measure of the quizlet having those observations. Proportional reduction in error is a more restrictive framework widely used in statistics, in proportionate reduction in error symbol which the general loss function is replaced by a more direct measure of error such as the mean square error. Examples are the coefficient of determination and Goodman and proportional reduction calculator Kruskal's lambda.[1] The concept of proportional reduction in loss was proposed by Bruce Cooil and Roland T. Rust in their 1994 paper. Many commonly used reliability measures for quantitative data (such as continuous data in an experimental design) are PRL measures, including Cronbach's alpha and measures proposed by Ben J. Winer (1971). It also provides a general way
Proportional Reduction In Error Stata
of developing measures for the reliability of qualitative data. For example, this framework provides several possible measures that are applicable when a researcher wants to assess the consensus between judges who are asked to code a number of items into mutually exclusive qualitative categories (Cooil and Rust, 1995). Measures of this latter type have been proposed by several researchers, including Perrault and Leigh (1989). References[edit] ^ Upton G., Cook, I. (2006) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4 Cooil, B., and Rust, R. T. (1994), "Reliability and Expected Loss: A Unifying Principle," Psychometrika, 59, 203-216. (available here) Cooil, B., and Rust, R. T. (1995), "General Estimators for the Reliability of Qualitative Data," Psychometrika, 60, 199-220. (available here) Rust, R. T., and Cooil, B. (1994), "Reliability Measures for Qualitative Data: Theory and Implications," Journal of Marketing Research, 31(1), 1-14. (available here) Winer, B.J. (1971), Statistical Principles in Experimental Design. New York: McGraw-Hill. Perreault, W.D. and Leigh, L.E. (1989), “Reliability of Nominal Data Based on Qualitative Judgments,” Journal of Marketing Research, 26, 135-14
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PRE, proportional reduction of errorPRE, proportional reduction of error having a mental medical condition financial status relatively bad relatively good total yes 390 (97,5 http://www.tankonyvtar.hu/en/tartalom/tamop425/0010_2A_21_Nemeth_Renata-Simon_David_Tarsadalomstatisztika_magyar_es_angol_nyelven_eng/ch08s02.html %) 10 (2,5 %) 400 (100 %) no 40 (6,7 %) 560 (93,3 %) 600 (100 %) total 430 (43 %) 570 (57 %) 1000 (100 %) Using one of the http://oxfordindex.oup.com/view/10.1093/oi/authority.20110803100349896 illustrations from the previous lecture (where we considered mental health to be the independent variable and financial status to be the dependent variable), let’s guess the financial status of the individual reduction in respondents based on our knowledge of the distribution: 57% have relatively good, 43% have relatively worse financial status.Let’s imagine that the respondents turn up one by one and we have to guess their financial status as accurately as possible. What’s the best way to do that? having a mental medical condition financial status relatively bad relatively good total yes 390 reduction in error (97,5 %) 10 (2,5 %) 400 (100 %) no 40 (6,7 %) 560 (93,3 %) 600 (100 %) total 430 (43 %) 570 (57 %) 1000 (100 %) Declareing each respondent to have a relatively good financial status is the safest way: thus we are wrong in 430 cases out of 1000.How does the situation change if we already know Table 1 and we can ask each respondent whether or not they have a mental medical condition?In this case we can improve the chances of our guesswork by categorizing everyone with a mental problem as having worse financial status, while those without mental problems as having better financial status. Thus the number of mistakes we make is down to 50.In other words, the guessing error characterizes the relationship of the two variables. Associational indices that work on this principle are called ’proportional reduction of error’ (PRE) indices.Calculating (λ) to get the connection of two nominal variables:8.1. egyenlet - Where:E1 is the number of categorising mistakes made without considering the independent variableE2 is the number of categorising mistakes made considering the independent variable
Not registered? Sign up.What's NewAboutGuided TourLibrarian ServicesContact UsHelpThe Oxford Index Underbar oi AllAllTitleAuthorKeywordAllTitleAuthorKeywordHomeBrowseLanguageSelect language English Spanish My Content(1)Recently viewed (1)proportional reduction...My Searches(0)PrintSaveEmailShareText size: AAOverviewproportional reduction in errorFind a librarySelect a Library... European Audiovisual Observatory European University Viadrina Hochschule fuer Musik und Darst. Kunst Institute for International Educational Research Universität Frankfurt a.M. Related Overviewsassociation variable coefficient of determination More Like ThisShow all results sharing this subject:Probability and StatisticsGOFeedback »Quick Reference(PRE)A criterion underlying some measures of association. The measures attempt to quantify the extent to which knowledge about one variable helps with the prediction of another variable. Examples include R2 (see coefficient of determination) and Goodman and Kruskal's lambda (see association).From: proportional reduction in error in A Dictionary of Statistics »Subjects: Probability and Statistics. Related content in Oxford IndexSee all related items in Oxford Index »Reference entriesproportional reduction in error in A Dictionary of Statistics Reference EntrySearch for the text `proportional reduction in error' anywhere in Oxford Index »Oxford University PressCopyright © 2016. All rights reserved.Privacy policy and legal noticeCreditsSite indexPowered by: Safari Books Online[93.127.147.10|93.127.147.10]93.127.147.10