# variance explained by model

This is a question for beginners.

edited 19/11.

I am really confused by the term variance and so many other variants. For example, the figure below shows the variance of two models to compare. Is shared terms (eg. R^2 or SD of the slope?)+ variation of residuals = total tendency of the data? What does the variance of residuals present? Is it the uncertainty of data? (*: Var_Res increase uncertainty decrease. more preferred or less preferred) What does uncertainty mean? High variance low uncertainty, is it true for all variance? What does variation mean? is variation the same as variance?

Thanks to John's answer, it's very easy to understand. But I would like to know more about the meaning of the variance term expressed in the analysis tech. Thank you!

• The context is in OLS. I am not a native speaker so even the description of the question might be vague... I am sorry about that. Commented Nov 15, 2022 at 22:53
• Duplicate?
– Dave
Commented Nov 16, 2022 at 2:40

Variance and variants are both nouns.

Variance, in general, describes how much range there is from a central tendency, like a mean, to the extreme values. A standard deviation is a specific measure of variance. The most common types of measurements are standard deviation and variance, in the statistical sense, the standard deviation squared.

Variants are different examples of a variable or a process or something that is measured. If you're measuring weights of mice, you might have mice that are given a drug. You could consider a mouse without the drug, usually "the control", one variant and one with the drug a different variant.

I tried to use plain language. Hope this helps.