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 Determine sigma from many datasets
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gintea

3 Posts

Posted - 03/19/2015 :  11:06:25 AM  Show Profile  Edit Topic  Reply with Quote  View user's IP address  Delete Topic
Origin Ver. and Service Release (Select Help-->About Origin): OriginPro8 SR4
Operating System: Windows 8

Hello,
I have a lot of datasets to analyse. Each one contains about 20 X,Y pairs of columns. For data contained at each Y column I have to find sigma from Gaussian fit (I don't need plots of Gaussian curves, just sigma) and then collect these sigma values for further analysis. I've been doing this the slow way - create a histogram for every Y column, change bin size to something sensible, and then fit gaussian curve. But it's very time consuming and it takes 3 workbooks for each Y column. Is there any faster way to do this? Not necessary in Origin, in case anyone has a better suggestion.

JacquelineHe

287 Posts

Posted - 03/20/2015 :  04:57:21 AM  Show Profile  Edit Reply  Reply with Quote  View user's IP address  Delete Reply
Hi,

You can use the "Statistics on Columns" tool to calculate the sigma values. The result "Standard Deviation" is "Sigma" value.

1. Highlight the columns you need to calculate and select "Statistics: Descriptive Statistics: Statistics on Columns" in the menu.

2. In the dialog, you also can add other columns as input dataset.
To know more detail about specifying your input data, please refer to this help page:
http://www.originlab.com/doc/Origin-Help/AnalyDialog-SpecifyInput

3.Make sure the "Standard Deviation" is checked under "Quantities to Compute: Moments" node. Then click "OK"



4.You could get the result in the report sheet.




To know more detail, please refer to the local help file:
Select "Help: Origin: Statistics: Descriptive Statistics: Statistics on Columns: The DescStats Dialog Box"
Or
Press "F1" key, when the dialog is open.


Thanks
Jacqueline
OriginLab




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gintea

3 Posts

Posted - 03/21/2015 :  09:38:37 AM  Show Profile  Edit Reply  Reply with Quote  View user's IP address  Delete Reply
Thanks a lot!
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