Box-Cox transformation of firm size data in statistical analysis
نویسندگان
چکیده
منابع مشابه
Improving your data transformations: Applying the Box-Cox transformation
Many of us in the social sciences deal with data that do not conform to assumptions of normality and/or homoscedasticity/homogeneity of variance. Some research has shown that parametric tests (e.g., multiple regression, ANOVA) can be robust to modest violations of these assumptions. Yet the reality is that almost all analyses (even nonparametric tests) benefit from improved the normality of var...
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BACKGROUND In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and...
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The Box–Cox transformation [1,2] (Box and Cox, 1964; Sakia, 1992) has been regarded as a parametric pre-processing technique aimed at making the distribution of a set of points approximately Gaussian. Since normality represents an assumption underlying many statistical data analysis tools, such technique has been widely applied in different fields of Computer Science. In this paper we will prov...
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In the use of ANOVA for hypothesis testing in animal science experiments, the assumption of homogeneity of errors often is violated because of scale effects and the nature of the measurements. We demonstrate a method for transforming data so that the assumptions of ANOVA are met (or violated to a lesser degree) and apply it in analysis of data from a physiology experiment. Our study examined wh...
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ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2014
ISSN: 1742-6596
DOI: 10.1088/1742-6596/490/1/012182