mean这个词有许多用法和含义，在不同的情境下有不同的解释和表达方式。 以下是一些常见的用法： 1. 表示某物或某人的意图、目的或动机。 例如： - What do you mean? 你是什么意思？ - I mean to say that it's not fair. 我的意思是说这不公平。 - What does it mean when he says that?
So we have arithmetic mean (AM), geometric mean (GM) and harmonic mean (HM). Their mathematical formulation is also well known along with their associated stereotypical examples (e.g., Harmonic mea...
When studying two independent samples means, we are told we are looking at the "difference of two means". This means we take the mean from population 1 ($\\bar y_1$) and subtract from it the mean from
What do you mean by "the derivative at 1 SD is +- 1"? Derivative of what? If you mean of a density plot, then what distribution? The normal? Different distributions will have different derivatives at 1 SD from the mean.
I also guess that some people prefer using mean squared deviation as a name for variance because it is more descriptive -- you instantly know from the name what someone is talking about, while for understanding what variance is you need to know at least elementary statistics. Check the following threads to learn more:
Thus we could measure the mean height of men in a sample of the population which we call a statistic and use this to draw inferences about the parameter of interest in the population. It is an inference because there will be some uncertainty and inaccuracy involved in drawing conclusions about the population based upon a sample.
"Can I use 'mean ± SD' for non-negative data when SD is higher than mean?" clearly you can (you already managed it in the question), the issue is more should you do so. However, what is missing here is the intended purpose of doing so. If it's really just to show both the mean and the standard deviation, wouldn't $\bar {x}=1, s = 3$ (whether or not the SD is larger) be less ambiguous and also ...
I have represented standard deviation as "±SD" before in publications. But I like to have opinions on this. Is it appropriate to use the notation '±' with SD ? Or ...
Pretty basic question: What does a normal distribution of residuals from a linear regression mean? In terms of, how does this reflect on my original data from the regression? I'm totally stumped,
