Data collected was analyzed using descriptive statistics, analysis of variance, multiple regression (stepwise) and geospatial techniques.
Data was analyzed using frequency, percentage, mean value, standard deviation, correlation Pearson product moment, and multiple regression Stepwise method.
The result of multiple regression (stepwise) indicated that, all the factors related to the success of microcredit entrepreneurs are important element towards the success of microcredit entrepreneurs.
However, appropriate multiple regression stepwise analyses (i.e. a multimarker approach) to detect independent correlations of variables, were performed.
Two-factorial ANOVA, analysis of covariance (ANCOVA), and multiple regression (stepwise method; variable entrance criteria: p < 0.05; variable removal criteria: p > 0.1) were used to coevaluate factors/variables.
All variables significantly associated with echocardiographic characteristics were included in the multiple regression stepwise analyses (serum albumin, PlGF, serum cholesterol, 25OH vitamin D, BNP, FGF23, serum creatinine, EN-RAGE, PTH, PAPP, Pi, sRAGE, serum TAG, MMP2).
Table 1 shows two types of multiple regression analysis: stepwise and fixed step.
The data was then subjected to multiple regression analysis (stepwise method) towards isolating the relevant competencies.
A multiple regression with stepwise method was applied to predict the amount of transfusion.
For the selection of variables in the multiple regression a stepwise approach was applied.
Contrarily to FST, in a multiple regression with stepwise selection, tm is the main explanatory variable of FST and FISS does not enter the model once tm is accounted for.
