Splot Statistics
Minitab is the leading provider of software and services for quality improvement and statistics education. More than 90% of Fortune 100 companies use Minitab Statistical Software, our flagship product, and more students worldwide have used Minitab to learn statistics than any other package. A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results. It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials.In the last twenty years, similar meta-analytical techniques. Wikimedia Commons has media related to Forest plots.: A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results.
What Are Split Statistics
Abstract: The paper advocates the use of a statistical tool dedicated to theexploration of data samples populated by several sources of events. This newtechnique, called sPlot, is able to unfold the contributions of the differentsources to the distribution of a data sample in a given variable. The sPlottool applies in the context of a Likelihood fit which is performed on the datasample to determine the yields of the various sources.
Submission history
From: Muriel Pivk [view email][v1] Tue, 17 Feb 2004 16:26:42 UTC (17 KB)
[v2]Fri, 24 Jun 2005 14:27:27 UTC (37 KB)
[v3] Fri, 2 Sep 2005 13:35:57 UTC (37 KB)
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Splot Statistics
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Abstract: A novel method called sPlot, painless to implement, is presented. It projectsout the signal and background distributions from a data sample for a variablethat is used or not in the original likelihood fit. In each bin of thatvariable, optimal use is made of the existing information present in the wholeevent sample, in contrast to the case of the usual likelihood-ratio-cutprojection plots. The thus reduced uncertainties in the low statistics bins,for the variable under consideration, makes it possible to detect small sizebiases such as pdf/data mismatches for a given species, and/or presence of anunexpected background contamination, that was not taken into account in the fitand therefore was biasing it. After presenting pedagogical examples, a briefapplication to Dalitz plots and measurements of branching ratios is given. Acomparison with the projection plots shows the interest of the method. Finallyare given the differents steps to implement the sPlot tool in an analysis.
Submission history
From: Muriel Pivk [view email][v1]Tue, 17 Feb 2004 16:26:42 UTC (17 KB)
[v2] Fri, 24 Jun 2005 14:27:27 UTC (37 KB)
[v3] Fri, 2 Sep 2005 13:35:57 UTC (37 KB)
Full-text links:Download:
Current browse context:References & Citations
Alpha Split Statistics
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs and how to get involved.