Determining the percentage of points away from the Mean

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The discussion focuses on analyzing lab data of fluctuating flow patterns in a flume to determine the proximity of measurements to the mean. The user initially considers using standard deviation and coefficient of variance (CoV) but is unsure about calculating CoV for multiple standard deviations. They plan to conduct a paired t-test to compare total sample data with a downsampled version at 1 Hz but face challenges due to differing sample sizes. Clarifications are sought on the appropriateness of paired t-tests and the methodology for aggregating data points. The user expresses a need for foundational statistical knowledge to effectively analyze their data and demonstrate variance.
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I am doing a summer project where i have got lab data of fluxuating flow patterns in a flume, the data i have plots discharge against time and my aim is to find whether a majority of measurements are close to the mean.

I thought the best way to do this would be with standard deviation where i could find out coeffient of variance in one SD (SD/mean*100), but my question is related to finding the CoV in second and third SD - is this even possible?
 
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I am unfamiliar with the terms you are using. However variance is the average of the square of the differences from the mean while standard deviation is square root of variance.
 
Yeah it seems like i should expand on what I am trying to do to best describe the work i want to do.

I have a lot of data where i have to show mathimatically there is a deviation from the mean of the sample data, i have decided i should do a paired t-test to show the variance. However, i am not entirely clear how to conduct a paired t-test for my given data - the sample i have in question has measured the velocity of flow in a flume at 50hz (so measured velocity once every 0.02sec), what i plan to do is use the data to show the data trend over a 1hz (or every 1 sec) then use a paired t-test to analyse the total sample data and the 1hz one.

I am just unsure how i can do that because for the total data i have over 2000 points but for the 1hz sample i have a little over 100, so working out the difference is a little awkward - and the SD's... I hope this makes a little more sence
 
I am not particularly familiar with statistic analysis, so it is hard for me to help. I don't know what a paired t-test is.
 
If I'm understanding your "explanation" then I have these comments:

You mention doing a paired t test, but your sample sizes are different? Paired t tests require equal sample sizes (the "classical" setting is is comparing results on pre- and post-test data)

This: " thought the best way to do this would be with standard deviation where i could find out coeffient of variance in one SD (SD/mean*100), but my question is related to finding the CoV in second and third SD - is this even possible?" - do you mean 2 and 3 standard deviations from the mean, or are you somehow referring to higher moments of the data?

Finally, "what i plan to do is use the data to show the data trend over a 1hz (or every 1 sec) then use a paired t-test to analyse the total sample data and the 1hz one."
Does this mean: for the data collected every .02 sec, begin at the first measurement and combine every 50 to represent 1 second, and use this to compare to the other data? if so, perhaps some more explanation - but even so, I doubt that would work.
 
Yeah maybe i should start again from the beginning (PS thanks for the reply and sry for the late post but i lost internet for a few days so only just saw the update), also i should point out my background is not in stats at all so i may be throwing some terms about that i picked up from the internet.

I have a lot of data and i have split this data into smaller segments so i have a graph that looks like the attachment.

What i want to do is compare the blue variance to the green line which is fairly steady, i was thinking i would use a paired/unpaired t-test to do this but again i am have no bases in stats and don't know if that is the correct way to go about the situation.
 

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I don't know if I am too far off here, but, AFAIK, paired t-tests are used to
t-test whether m-means of different populations are e-equal (Sorry for the
bad joke). Is that what you are trying to do.?
 
Im just trying to show that there is a different bettween the two sets of data that would reject a null hypothesis, i thought a paired (or to be exact a unpaired) test would show this
 
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