Statistical regression analysis is a powerful and reliable method to determine the impact of
one or several independent variable (s) on a dependent variable. It is the most widely used …

The fuzzy rating method has been introduced in psychometric studies as a tool, which allows
the capture of and accurate reflection of the diversity, subjectivity, and imprecision inherent …

Among the various modeling techniques applied to dataset, multiple linear regression (MLR)
analysis is the most efficient way to figure out the relationship between the response …

The first aim is to emphasize the use of fuzziness in data analysis to capture information that
has been traditionally disregarded with a cost in the precision of the conclusions. Fuzziness …

Flexible modelling of interval-valued data is of great practical importance with the
development of advanced technologies in current data collection processes. This paper …

Interval data are widely used in many fields, notably in economics, industry, and health
areas. Analogous to the scatterplot for single-value data, the rectangle plot and cross plot …

A linear regression model for imprecise random variables is considered. The imprecision of
a random element has been formalized by means of the LR fuzzy random variable …

This work has been developed in a large steel industry in Brazil, which produces railway
and industrial components, and whose aim was to reduce casting defects. Usually, in …

The application of integrated study of water quality and statistics for environmental modelling
is considered as a powerful analytical tool that has been thrived significantly during recent …

When handling fuzzy number data, it is a common practice to make use of a metric to
quantify distances between fuzzy numbers. Several metrics have been suggested in the …