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Proportional Reduction Of Error Formula

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PRE the proportionate reduction in error is a measure of the quizlet measures. Proportional Reduction of Error (PRE) The proportionate reduction in error symbol concept that underlies the definition and interpretation of several measures

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of association, PRE measures are derived by comparing the errors made in predicting the dependent while ignoring the

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independent variable with errors made when making predictions that use information about the independent variable. E1 = errors of prediction made when the independent variable is ignored E2 = errors of prediction made when the prediction proportional reduction in error interpretation is based on the independent variable "All PRE measures are based on comparing predictive error levels that result from each of two methods of prediction" (Frankfort-Nachmias and Leon-Guerrero 2011:366). Table 12.1 on page 366 of the textbook helps us to understand this. The independent variable is number of children; the dependent variable is support for abortion. Content on this page requires a newer version of Adobe Flash Player. Two of the most commonly used PRE measures of association are lambda (λ) and gamma (γ). Two PRE Measures: Lambda and Gamma Lambda λ Appropriate for: Nominal Variables Gamma γ Appropriate for: Ordinal and Dichotomous Nominal Variables

of making observations which are possibly subject to errors of all types. Such measures quantify how much having the observations available has

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reduced the loss (cost) of the uncertainty about the intended quantity proportional reduction in error spss compared with not having those observations. Proportional reduction in error is a more restrictive framework widely used regression to the mean occurs because extreme scores tend to become: in statistics, in which the general loss function is replaced by a more direct measure of error such as the mean square error. Examples are the coefficient https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T02_proportional.html of determination and Goodman and 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. https://en.wikipedia.org/wiki/Proportional_reduction_in_loss Winer (1971). It also provides a general way 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.

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Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top calculating p-value from PRE (proportional reduction in error) up vote 0 down vote favorite In R, I have created a PRE value to test whether or not modelA is significantly different from modelC. How can I get a significance value for either the PRE distribution or the R^2 distribution (pearson's R)? #modelA<-lm(...) #modelC<-lm(...) PRE=(sum(modelC$residuals^2)-sum(modelA$residuals^2))/sum(modelC$residuals^2) r regression multiple-regression linear-model share|improve this question asked Sep 22 '15 at 17:11 Rilcon42 272111 Are they nested models? If so a small rescaling of your formula should yield a standard partial F (discussed in a number of questions here). If they're not nested, most people would use some criterion for comparison rather than a test (such as comparing AIC or BIC, for example), but it's possible to use something like Vuong's test. –Glen_b♦ Oct 15 '15 at 2:55 An F test would work, I was hoping someone knew of a way to get the PRE distribution critical values in R though. –Rilcon42 Oct 16 '15 at 3:33 You can back them straight out of the F-test –Glen_b♦ Oct 16 '15 at 6:45 add a comment| 1 Answer 1 active oldest votes up vote -1 down vote There is no PRE distribution. You just have to use your judgement if the reduction in error seems large enough to justify adding complexity to the model. share|improve this answer answered Oct 15 '15 at 1:55 Michael 1 add a comment| Your Answer draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Post as a guest Name Email Post as a guest Name Email discard By posting your answer, you agree to the privacy policy and terms of service.

 

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