Error Bars In Regression
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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 linear regression data with error bars Us Learn more about Stack Overflow the company Business Learn more about hiring developers error bars in excel or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question how to calculate 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 a error bars matlab question Anybody can answer The best answers are voted up and rise to the top Error bars, linear regression and “standard deviation” for point up vote 3 down vote favorite 1 I have a set of experimental data points. I performed the measurements in triplicate, for each of the point of the data set. Therefore, I can draw each data point with the standard deviation of each
Error Bars In Excel 2013
triplicate. See the picture attached. In experimental sciences, it is common to report a value with its standard deviation. Ex: a mean, +/- the std. I can calculate a linear regression for the data set. If I have the equation of the linear regression, I can calculate x for any y. Let's take y=50 -> x=11.69 Now, is there a way to evaluate the "dispersion" of this extrapolated point ? Something like 11.69 +/- something. I know it should be the other way around, like 50 +/- something for x=11.69, but then I could use the equation to transform it to x. Basically what I'm asking: is there a global "standard deviation" for a complete linear regression ? EDIT: When I say "any y", I mean that y will not be an experimental value. I choose it to be 50. regression standard-deviation share|improve this question edited Apr 11 at 9:37 asked Apr 10 at 15:38 Rififi 1556 This is sometimes called "inverse regression." I have described it in replies at stats.stackexchange.com/questions/38023 and stats.stackexchange.com/questions/31528, but I haven't posted any detailed explanation. The latter does contain a link to a reference. Several other people have also mentioned this--but again without providin
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Error Bars In R
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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 http://stackoverflow.com/questions/21469620/how-to-do-linear-regression-taking-errorbars-into-account this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up error bars How to do linear regression, taking errorbars into account? up vote 5 down vote favorite 1 I am doing a computer simulation for some physical system of finite size, and after this I am doing extrapolation to the infinity (Thermodynamic limit). Some theory says that data should scale linearly with system size, so I am doing linear regression. The data I have is error bars in noisy, but for each data point I can estimate errorbars. So, for example data points looks like: x_list = [0.3333333333333333, 0.2886751345948129, 0.25, 0.23570226039551587, 0.22360679774997896, 0.20412414523193154, 0.2, 0.16666666666666666] y_list = [0.13250359351851854, 0.12098339583333334, 0.12398501145833334, 0.09152715, 0.11167239583333334, 0.10876248333333333, 0.09814170444444444, 0.08560799305555555] y_err = [0.003306749165349316, 0.003818446389148108, 0.0056036878203831785, 0.0036635292592592595, 0.0037034897788415424, 0.007576672222222223, 0.002981084130692832, 0.0034913019065973983] Let's say I am trying to do this in Python. First way that I know is: m, c, r_value, p_value, std_err = scipy.stats.linregress(x_list, y_list) I understand this gives me errorbars of the result, but this does not take into account errorbars of the initial data. Second way that I know is: m, c = numpy.polynomial.polynomial.polyfit(x_list, y_list, 1, w = [1.0 / ty for ty in y_err], full=False) Here we use the inverse of the errorbar for the each point as a weight that is used in the least square approximation. So if a point is not really that reliable it will not influence result a lot, which is reasonable. But I can not figure out how to get something that combines both these methods. What I really want is what second method does, meaning use regression when every point i
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