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nweilinger
Canada
1 Posts |
 Posted - 04/27/2012 : 7:41:36 PM
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Hello,
The answer to my question is probably simple (i hope) but i can't seem to figure it out. I know origin has 2D binning, however i simply want to bin my y-axis data into less frequent x bins (1D bins). For example, my data samples at 3hz (3 per second) and i want origin to bin (or reduce) that data to 1hz (once per second) in mean +/- standard error. Thanks in advance!
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couturier
France
291 Posts |
Posted - 04/30/2012 : 08:13:10 AM
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| Check menu Analysis:Data manipulation:Reduce Duplicate X Data |
Edited by - couturier on 04/30/2012 08:13:32 AM |
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greg
USA
1378 Posts |
Posted - 04/30/2012 : 5:02:08 PM
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Unfortunately, there is no simple answer here. 2D Binning with a 1 bin Y will give you the mean (or min, max, median, etc.) but we do not do the Standard Deviation (SD) calculation. (If we did, then you could use the 2D Binning twice : once to get the mean and once to get the SD.)
A script could be written to calculate mean and SD which would require some prompting/programming on how to bin the X data. The following approach is a bit of a hybrid. I will use Frequency Counts to determine how to do the binning and a script to get the Mean and SD values.
Given a two column worksheet with our data, select the X data column and choose Statistics : Descriptive Statistics : Frequency Counts.
Set Recalculate mode to Auto.
Using Bin Ends is fine as is leaving Min and Max Bin as Auto.
Set the Bin Size to 1 (for 1 second) by selecting the Bin Size radio and unchecking Bin Size Auto.
All we need to compute is Bin Center and Count.
Set the Output as [<input>]<input>! then click OK.
This create two columns with the bin centers and bin counts.
Now add three columns to the worksheet and set the middle of these to be an error column.
Select the last column, right click on it and choose Set Column Values.
Paste the following into the "Before Formula Scripts" box, set Recalculate the Auto and click OK.
The script uses the bin counts to calculate the stats for each sub-range of the original Y data. |
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binning x