Helloo:) I’m working on fatty acid composition analysis (from bird preen oil samples), with very little previous knowledge of statistics, and I’m looking for some help understanding what’s happening.
Background: I have a dataset with treatment group, sex, aviary of the sampled bird, and 10 fatty acids found in the preen oil samples. There are 3 treatment groups: germ-free, conventionalised, and parent-raised (although only the germ-free and conventionalised can be realistically compared). Between these 2 treatment groups, there are only a total of 27 samples, 13 in one and 14 in the other. The fatty acid cells contain proportions rather than absolute values (they always sum up to 1), so an example row looks like this: 

The main end goal is to see if the germ-free condition results in any changes to preen oil.

Now, for the analysis issues: I’ve computed robust Aitchison distances, which are supposedly a better choice for this data than Bray-Curtis(?) and ran a PCoA with them, followed by PERMANOVAs for treatment group, sex, and aviary (none of which were significant, unlike the marginally significant and high R2 results on B-C). 
I’ve also been advised to run some form of a linear model on each individual fatty acid, but I don’t quite understand where to start with that… Or which one would be suitable.
I would appreciate any feedback, comments, or advice on what I’ve done so far and what I’m trying to do!:)

Hi all, I'm working on a student project and would like a sanity check on our design.

We treat regional wage orders as the policy intervention. These orders have been issued ever since, so there is no single policy date, and a region can have an order in one year, none in the next, then another. The outcome is a gap measure observed in a few survey years (not every year).

The dummy is 1 if the region had a wage order that changed the wage in that year, and 0 otherwise. Standard errors are clustered by region. The idea is to compare the change in the outcome for regions with a wage order against regions without one in the same year, instead of a classic pre/post comparison.

Questions:

1. Is it fair to call this a DiD even without a single pre and post period?

2. Is it a problem that treatment is repeated and switches on and off?

3. Any papers or methods I should read for this kind of setup?

greetings everyone, I am a grad student from forestry background and i am doing a research project on biomass estimation via sentinel 2 variables. I have 47 plots with field measured biomass value and i ve analyzed 18 different remote sensing variables that acts as the predictors of biomass. predictors correlated well with the measured biomass but the regression model stated lower R square value (i.e. 0.40) at which isnt like i expected. I ran five different models MLR, PLSR, RF, GBR, linear SVR, Ridge (im a beginner in coding and all, so claude helped me) etc but neither improved the coeft of determination. I followed the exact methodological framework from the literature but lost somewhere. My main objective is to publish the paper but the lower accuracy is becoming the problem. Sorry if i couldnt explain it clearly, feel free to ask via comment or dm/ any suggestions and feedbacks are appreciated. Thankyou!

Hello!

I have minor revisions on my scientific paper. One of reviewers comment was: "please include the statistical model equation in the Methods section.". My doubt is: I performed a Beta Regression Model in R: model_beta <- betareg(proportion ~ plant + week, data = plants).

Is this what I´m supposed to add to the paper? Im a biologist, not a statistician, so im not sure what to write ... Thanks!

Please provide examples. Thank you!