Third, a Multi-Nominal Logistics Regression Analysis was conducted for examining the influences of independent variables (at child and parental level) on the latent profile group according to the change in children's behavioral problems.
A significant difference on the knowledge of respondents among different groups of age, educational status, occupation, monthly household income, parity, knowing someone with cervical cancer and visit to a health institution was detected during the bivariate logistics regression analysis.
Prescreening using univariate logistics regression analysis followed by unconditional binary logistic regression analysis showed that maternal age ≤20 years, low educational level, history of one or more spontaneous abortions, history of one or more stillbirths, pregnancy-induced hypertension, anemia, premature rupture of membranes, oligohydramnios, preterm birth, and BMI <18.5 were risk factors.
*p < 0.05; **p < 0.01; ***p < 0.001; ORU: univariate odds ratio obtained using logistic regression; ORm: odds ratio obtained from stepwise multivariate logistics regression analysis using univariately significant variables as candidate variables; NS: not statistically significant in multivariate analysis.
*p < 0.05; **p < 0.01; ***p < 0.001; ORU: univariate odds ratio obtained from logistic regression models; ORm: odds ratios obtained from stepwise multivariate logistics regression analysis, using univariately significant variables as candidate variables; NS: not statistically significant in multivariate analysis.
Logistics regression analysis showed that those who first visited a health post/clinic (OR = 3.50, 95%CI 1.86 6.57) or a private medical provider (OR = 2.10 95%CI 1.18, 3.71) were significantly associated with increased health systems' delay (See additional file 3).
Linear Discriminant Analysis (LDA) and Multinomial Logistics Regression Analysis (MLR) were used to aggregate the measured soil properties into the landform groups.
Results of the multivariate logistics regression analysis are presented in Table 5.
Logistics regression analysis was carried out to determine predictors of family planning use.
Multivariate logistics regression analysis using the backward elimination method was employed.
