For data that did not meet the criteria for normality, logarithmic transformations were applied.
To meet the linearity assumption, we conducted logarithmic transformation of the Physical Comorbidity Index.
For data that did not meet the criteria for normality, natural logarithmic transformations were applied.
After data exploration, the original data set of indoor air NO2 concentration was transformed into logarithmic base 10 (log10) to meet the assumptions of normality for the analysis of variance (ANOVA) and linear mixed model regression analysis.
Second, we analyzed RA patients with SHS ≥1, characterized by a more severe structural disease at inclusion, because of the logarithmic transformation of SHS necessary to meet the assumption of linear regression analysis.
If E is an ε-set, then the set of r ≥ 1 for which the circle S ( 0, r ) meets has finite logarithmic measure and hence zero upper logarithmic density.
We used logarithmic transformation of variables when needed to meet the requirements of equal variances and to achieve approximately normal distribution.
To ensure that our modelling approaches met the basic assumptions of OLS, the response variable was transformed to logarithmic scale [11,52], while for the predictors both log transformed and non-transformed variables were used.
To ensure that our modelling approaches met the basic assumptions of OLS, the response variable was transformed to logarithmic scale [ 11, 52], while for the predictors both log transformed and non-transformed variables were used.
Moreover, there exists a set of logarithmic density such that for the circle does not meet the -set.
