First I will describe statistics briefly, and then your question.
In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. Often, variation is quantified asvariance; then, the more specific term 'explained variance can be used.
The complementary part of the total variation is called unexplained or residual.
Thus if this is true then the following will explain why you are looking for the wrong answers to your question. Or you were just trying to doop me and failed.
Suppose there is a series of observations from a univariate distribution and we want to estimate the mean of that distribution (the so-called location model). In this case, the errors are the deviations of the observations from the population mean, while the residuals are the deviations of the observations from the sample mean.
A statistical error (or disturbance) is the amount by which an observation differs from its expected value, the latter being based on the wholepopulation from which the statistical unit was chosen randomly. For example, if the mean height in a population of 21-day-old plant is 1.75 meters, and one randomly chosen plant is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen plant is 1.70 meters tall, then the "error" is −0.05 meters. The expected value, being the mean of the entire population, is typically unobservable, and hence the statistical error cannot be observed either!!!!. <<<Point in case here.
A residual (or fitting deviation), on the other hand, is an observable estimateof the unobservable statistical error. Consider the previous example with plant heights and suppose we have a random sample of plants. The sample mean could serve as a good estimator of the population mean. Then we have:
The difference between the height of each one in the sample and the unobservable population mean is a statistical errorThe difference between the height of each plant in the sample and the observable sample mean is a residual.
Note that the sum of the residuals within a random sample is necessarily zero, and thus the residuals are necessarily not independent. The statistical errors on the other hand are independent, and their sum within the random sample is almost surely not zero.
One can standardize statistical errors (especially of a normal distribution) in a z-score (or "standard score"), and standardize residuals in a t, or more generally studentized residuals. But that is up to you to deligate weather or not it is actually worth your time.
Now persay you want to know about variation in/consistency rather than statistics which genraly have no application to plants, then you will have another case entirely. But this is what you asked so I will simply take my mind and use it elsewhere it may be needed lemme know if you have any further questions
