I don't know how this impacts significance exactly but spurious regression is very common in time series, when you are tracking relationships over time. If two variables have a trend built into them they can show high correlation even though they are not related. One thing to look for, is if you...
If you wanted to do something simple you could take the data (separately for each species and project forward several years) with exponential smoothing. You need at least 50 points of data.
All the time series methods I know which show how one variable influences another over time are way to...
What is the range of your dependent variable. From what to what.
Violations of your assumptions won't impact the slope. They will influence the statistical test. There are a range of solutions for violation none are easy to do. Transformations of the data is the most common way to deal with the...
My computer will not let me copy and paste formulas from that link or code. I am not sure why this occurs.
Thanks for explaining what the  does. One problem with OTEST is the authors assume you know a fair amount of R already.
It is what is in the brackets that I am asking about I don't know what it is doing. It is figure 9.1 in the link below.
We have significant seasonality in our data. The otext suggested that processing days could explain much of this seasonality (in our case, days per month more generally for other phenomenon). I was trying to standardize our data by dividing spending by the days per month for a given month...
If you think that you have the population then you can ignore statistical tests. Whatever the effect size you find, that is the true effect size. So p values, sample size etc are entirely unimportant. I would not even report statistical tests in this case.
Are your predictor variables ordinal. And what is your dependent variable - how is it coded.
I am guessing your dependent variable is not interval which is a problem if true given you appear to be running linear regression.
You don't have a lot of data given what your standardized predicted...
Well first you want to analyze if there is a structural break at that period. Something like a chow test.
If there is one you normally analyze the data separately within the different periods. Essentially you have a new process so past data is of little value before the break.
I was asking about the validity of these kind of tests in general. How much confidence can we put in observational studies. And Hlsmith addressed.
No statistical test, including ones with random assignment can get rid of the possibility of confounds. Some are better than others.