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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.
The VIF shows how much the variance of the coefficient estimate is being inflated by multicollinearity.
The VIF is an index that measures how much the variance of an estimated regression coefficient is increased because of collinearity.
The VIF provided an index that could measure how much the variance of an estimated regression coefficient increased due to collinearity.
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.
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If we try to answer the question how much variance of income is explained by wages and how much by bonus, formally our problem is exactly the same as when we try to predict how much of the variance of the total effect is due to the direct effect and how much to the indirect effect.
With this data set, we compared how much of the variance was explained by our word frequencies and how much was explained by the other word frequency measures.
In the analysis we investigated how much of the variance of FS is explained by NR, and how much in addition is explained by DKO.
Much of the variance is due to the great difficulty of measuring cosmic distances.
The robust nature of the classification results indicates that much of the variance in metabolic response to ATD is accounted for by genotypic and phenotypic category.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com