"how much variance" is correct and usable in written English.
You can use it when you want to ask someone how much something varies or changes from individual to individual, or in a broader sense, how much something differs from its usual form or expected outcome. For example, "Given the current economic climate, how much variance should I expect in stock prices?".
Rewarding and penalizing leaders based on outcomes overestimates how much variance people actually control.
Multiple regression was used to determine how much variance could be explained in five hydrologic variables describing the flashiness of peak flow (three original metrics), total seasonal flashiness (Richards Baker flashiness index), and the ratio of total winter stormflow to total discharge (the stormflow index).
How much variance explanation power do recovery-oriented moves offer?
The article does go on to say that this is an estimate of how much variance is explained by genetics.
The AVE values showed how much variance an antecedent explained in comparison to the variance explained by measurement error.
It's not clear how much variance there is the completion rate, or what is the overall course completion rate for all students.
Three tests were used: Spearman's ρ correlation coefficients; the correlation of the regression coefficients to show the correlation of the exposures in relation to 'heart disease and stroke'; and the variance inflation factor (VIF), which is the inverse of 1 R and shows how much the variance of the coefficient estimate is inflated by multi-collinearity in the model [ 30].
The variance inflation factor (VIF) of each explanatory variable, which is an index that measures how much the variance of a coefficient is increased due to collinearity, was calculated to examine the severity of multicollinearity.
The regression models assess how much overall variance the measures together account for, and the relative contribution of each measure to this amount.
For example, when we add the section level variables to the models such as course type or instructor practices, we are interested in how much the variance between sections is reduced.
VIF is a common value which measures how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related, and it assumes that multicollinearity is high when VIF are larger than 5. Variance inflation factors are given Table 4.
