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Fig. 2 Univariate and multivariate statistical analysis.

Univariate and multivariate statistical analysis were performed.

The qualitative methods such as AHP subjectively help to rank the causal factors leading to classification of an area based on the priority scale whereas, the quantitative methods (bivariate or multivariate statistical analysis) use the observed landslide data for asserting the spatial relationship of the problem with the prevailing geo-environmental parameters (J. Corominas et al. 2014).

A secondary analysis was conducted using univariate, bivariate and multivariate statistical methods.

We employed bivariate and multivariate statistical tests.

Univariate, bivariate and multivariate statistical models were used for the evaluation of data.

IBM SPSS Statistics version 20 was used for all descriptive, bivariate and multivariate statistical analyses.

The results from both the bivariate and multivariate statistical modeling confirmed these assumptions.

Bivariate and multivariate statistical analyses were performed using SPSS (version 15.0) (SPSS , Inc Chicago, Ill, USA) and R package [ 24].

Bivariate and multivariate statistical analyses, including multiple regression modeling, were performed to examine reliability and validity of the R-OHLI.

A conceptual framework for predictors of a hybrid dependent variable was developed and both bivariate and multivariate statistical techniques employed.