I have acquired 21 observations from 21 samples (density difference percentages). For this 21 samples I have also 9 other parameters for each. I would like to do a statistical analysis to find out which of these 9 parameters affect the density observation and what is the correlation between each of the 9 parameters and the density difference reading.
I agree with David. Forget it. This is more coffee grounds reading than science.
Consider a minimum of 10-20 observations per variable, this is 100 in your case. Then don't analyze these correlations in isolation but fit an appropriate multivariate model (if you want/should/must consider interactions, you will need an even larger sample size!). Cooperate with a local statistician if you need help with that.
You have way to many IVs for your n. Better look at increasing n and/or decreasing IVs. If this a prediction problem lasso runs for p>n. you might check that out, see the attached. Best, D. Booth
I agree with David. Forget it. This is more coffee grounds reading than science.
Consider a minimum of 10-20 observations per variable, this is 100 in your case. Then don't analyze these correlations in isolation but fit an appropriate multivariate model (if you want/should/must consider interactions, you will need an even larger sample size!). Cooperate with a local statistician if you need help with that.
Hi, I recommend you to try the Spearman correlation coefficient.
the result would be a table and you can see the ranked value for each variable on your observation.
If your observations of each sample are in different times you can think about panel regression.
That's how my data are. I think I can go down from the 9 parameters to two or 3, but I would like to know what's their correlation to the density difference.
Seyyed Amir Yasin Ahmadi Mohamad-Javad Mehrani David Eugene Booth Jochen Wilhelm
I can't recommend using correlations. That is essentially p-value selection which is worthless. Too unstable. Drop an observation or so and everything changes.
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