摘要：Resin mortars belong to the group of concrete-like construction
composites. They are obtained by mixing a synthetic resin with a hardener
and an appropriately selected aggregate. The latter component is usually as
much as 90% of the composite mass and can largely shape the
characteristics of the finished product. The fact that the type of filler used
can significantly differentiate the values of physical and mechanical
parameters of epoxy mortars is confirmed by the results of the exploratory
data analysis method used in this article, which is discriminant analysis.
This allows us to examine differences between groups of objects based on
a set of selected independent variables (predictors). It is used to solve
a wide range of classification and prediction problems. The core of
discriminant analysis is a model presented in the form of a linear
combination of independent variables, which allows classification of
observations (e.g. test mortars) into one of the groups that are of interest to
the researcher. In discriminant analysis one can distinguish the learning
stage (model building), in which classification rules are created based on
research results (training set) and the classification stage, i.e. the use of the
model, e.g. for testing its prognostic accuracy.
其他摘要：Resin mortars belong to the group of concrete-like construction composites. They are obtained by mixing a synthetic resin with a hardener and an appropriately selected aggregate. The latter component is usually as much as 90% of the composite mass and can largely shape the characteristics of the finished product. The fact that the type of filler used can significantly differentiate the values of physical and mechanical parameters of epoxy mortars is confirmed by the results of the exploratory data analysis method used in this article, which is discriminant analysis. This allows us to examine differences between groups of objects based on a set of selected independent variables (predictors). It is used to solve a wide range of classification and prediction problems. The core of discriminant analysis is a model presented in the form of a linear combination of independent variables, which allows classification of observations (e.g. test mortars) into one of the groups that are of interest to the researcher. In discriminant analysis one can distinguish the learning stage (model building), in which classification rules are created based on research results (training set) and the classification stage, i.e. the use of the model, e.g. for testing its prognostic accuracy.
