Poisson Error Bars Root
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Poisson Errors
2007 15:46 Location: South Korea Asymmetric errors in histograms Quote Unread postby petrakou » Thu Sep 11, histogram errors 2014 15:25 Dear experts, After a quick search I see that there is no way to set asymmetric bin errors in histograms. I hope this is just outdated info, poisson uncertainty because I'm honestly shocked I've changed my fitting code from using histograms to graphs and then to histograms again because of inadequencies in either object (or in my understanding of them!), and I really want to stick to histograms this time. So if this possibility doesn't exist, do you suggest a way to make my fit understand that the
Poisson Standard Error
error bars don't extend below zero? Best regards, Eleni Top couet Posts: 7307 Joined: Tue Sep 02, 2003 9:32 Location: CERN Contact: Contact couet Website Re: Asymmetric errors in histograms Quote Unread postby couet » Mon Sep 15, 2014 10:33 you should use TGraphAsymErrors.http://root.cern.ch/root/html534/TGraphAsymmErrors.html Olivier Couet - CERN/EP/SFT Top moneta Posts: 2346 Joined: Fri Jun 03, 2005 15:38 Location: CERN Re: Asymmetric errors in histograms Quote Unread postby moneta » Mon Sep 15, 2014 11:17 Hi, An histogram should be used to represent counts (or weighted counts). So in the first case you have Poisson errors, and you can get symmetric errors, the Poisson confidence intervals (from Garwood), by calling the function TH1::SetBinErrorOption(TH1::kPoisson). And when fitting an histogram you should use the likelihood method. If you have just random points with some user defined asymmetric errors, you should then use the TGraphAsymErrors class, as suggested by Olivier Best Regards Lorenzo Top Display posts from previous: All posts1 day7 days2 weeks1 month3 months6 months1 year Sort by AuthorPost timeSubject AscendingDescending Post Reply 3 pos
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 error bars for count data Us Learn more about Stack Overflow the company Business Learn more about hiring histogram uncertainty developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question
Histogram Error Bars
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 https://root.cern.ch/phpBB3/viewtopic.php?t=18607 a question Anybody can answer The best answers are voted up and rise to the top Standard error of a count up vote 8 down vote favorite 2 I have a dataset of incident cases by season of a rare disease. For example, say there were 180 cases in the spring, 90 in the summer, 45 in the fall, and 210 in the winter. I'm struggling with http://stats.stackexchange.com/questions/31548/standard-error-of-a-count whether it is appropriate to attach standard errors to these numbers. The research goals are inferential in the sense that we are looking for a seasonal pattern in disease incidence that might recur in the future. Thus, it feels intuitively like it should be possible to attach a measure of uncertainty to the totals. However, I'm not sure how one would compute a standard error in this case since we are dealing with simple counts rather than, e.g., means or proportions. Finally, would the answer depend on whether the data represent the population of cases (every case that has ever occurred) or a random sample? If I am not mistaken, it generally does not make sense to present standard errors with population statistics, since there is no inference. poisson standard-error count-data share|improve this question edited Mar 16 '13 at 0:32 Glen_b♦ 151k19248516 asked Jul 3 '12 at 3:48 half-pass 1,05011126 Count is just unnormalized proportion so you can compute st. error of proportion and "unnormalize" it into count units, if it makes sence for you. You are right that st. error is applicable only to sample. In population, there is no error. –ttnphns Jul 3 '12 at 6:37
Research Business Alumni About Us Visitors Department of Physics You are in: Home ⇨ Department of Physics ⇨ For current students and staff ⇨ Current http://labs.physics.dur.ac.uk/skills/skills/poisson.php students ⇨ Labs Level 1Level 2Level 3Level 4SkillsISEsHealth & Safety Overview Experimental physics is the bedrock from which all our understanding of the universe must come. Without the ability to test nature, even our grandest ideas are just speculation. Even if you plan to avoid experimental work in your career, you will need to understand the provenance of the data with error bars which to test your theories. So how do we set about learning it? The answer is stage by stage, level by level! Level 1 - mastering the basics You prepare for full-scale experiments. Level 1 Labs see you build the skills required to be a competent experimental physicist. By doing small, self-contained experiments that last a single session, You will learn basic poisson error bars lab skills such as: Making observations - for example, how to measure electrical signals with an oscilloscope. Recording what you did in lab book and spreadsheet. Processing the data on a computer and estimating the uncertainty in your measurements and the statistical significance of your results. Interpretation of your data using the Physics learnt in the lecture courses. 'Writing a report of your experiment. Using your time effectively and work harmoniously with a partner. How to do all this safely. Level 2 - putting it all together Doing a complete investigation using what you learnt in Level 1. You will carry out experiments over multiple sessions and have more freedom. You will still be supported throughout so that you can learn the skills needed for experimental physics: Choosing measurements you will need to make, how many and to what accuracy. Planning your activities over multiple sessions and record what you did. Using computers to control hardware. Using cryogens safely. Automating experiments so that you can generate large datasets without breaking into a sweat. Presenting your work - in written reports, seminars and interviews.
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