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Univariate analysis included verification of normality distribution and calculation of mean values for continuous variables and of frequency distribution for categorical variables.
Data was managed using a Sapphire Laboratory Information Management System (LabVantage, New Brunswick, NJ), exported in Microsoft Excel (Redmond, WA) and statistical analyses (descriptive statistics and tests of normality, distribution and correlation) were performed using SAS (Cary, NC) software.
Continuous variables are presented as mean ± SD or as median and interquartile range (in the absence of normality distribution) and were compared between groups using the unpaired t test or the Mann–Whitney test, respectively.
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While making no assumption about the distribution of data, normality distribution testing of the continuous variables was performed using the non-parametric test; Kolmogorov-Smirnov test (KS-test).
Fig. 1 Shapiro-Wilk test of Normality and normal distribution curve for maternity care expenditure distribution.
Normality distribution of variables was examined using Kolmogorov-Smirnov test.
Inverse distance weighting (IDW) method was used due to lack of normality of distribution of annualized average incidence and difficulty of transformation (into to normal distribution).
Testing of normality of distribution was performed with Q–Q plots.
This violated a key criterion for parametric testing, that of normality of distribution, so non-parametric tests were selected.
A preliminary analysis was conducted to ensure that there were no violations to the assumption of normality of distribution.
The analysis of variance is subjected to the assumptions of normality of distributions as well as homogeneity of variance for all groups of the considered factor.
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