# How to find error in experimental data using MATLAB code?

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Safi ullah il 7 Ago 2018
Commentato: Safi ullah il 9 Ago 2018
Hi everyone, I downloaded experimental raw data and then analyse with a software. I have used a standard analysis software package (GUISDAP) and extract electron density, Ne = 1×1000. Then I used the following Matlab code to get data from some specific columns,
A=Ne(1, 90:90:1000); %%this gives A=1×11
As the Matlab code is giving me data exactly after 90 data points. So in this case how I estimate or minimise the error/noise from the data. Someone give me the suggestion to find standard deviation. Actually I do not know in this case what means the noise and how to deal with it. Any guidance will be appreciated. Thanks
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Adam Danz il 7 Ago 2018
Modificato: Adam Danz il 8 Ago 2018
Someone give me the suggestion to find standard deviation. Actually I do not know in this case what means the noise and how to deal with it.
Are you asking what 'noise' means in regard to data or are you asking how to interpret the noise in your data?
Noise is just variability of a measurement. If I step on a digital scale 5 times my weight might be [79.9, 80.1, 80.0, 79.8, 80.1] kg. The variation might be explained in how I was centered on the scale each time, my posture, or the lousy mechanics within the scale. Of course I have a True (capital T) weight at any point in time but the 'noisy' scale is the only way to estimate that True weight and along with the estimates comes noise (variability).
For example, let's say my True weight (which isn't knowable) is 80.0001425458541254789 kg. The mean of my 5 measurements is 79.98 and the standard deviation is 0.13038.
x = [79.9, 80.1, 80.0, 79.8, 80.1];
m = mean(x)
s = std(x) % <- That's how to calculate standard deviation
If your data are normally distributed, ~68% of your data will be within +/- 1 standard deviation from the mean. If the scale continues to behave the same and I quickly weight myself 1000 more times, ~68% of the measurement will be between 79.85 and 80.11 kg.
upper = m + 1*s
lower = m - 1*s
If your data are not approximately normally distributed, you should not be using stand deviations but you could use a non parametric test such as bootstrapped confidence intervals. It might be difficult to determine the distribution of your data since you only seem to have 11 data points in 'A'.
I'm not certain that this explanation is what you were looking for so please feel free to follow up or tell me how far off base my interpretation of your noisy question was ;)
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Adam Danz il 9 Ago 2018
My pleasure. Be sure to read the documentation on isoutlier() and choose parameter values that makes sense for your data and that you can explain in your paper.
Safi ullah il 9 Ago 2018
@ Dear Adam, sure I will.

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