This chapter presents an overview of curve fitting methods in Euclidean
spaces, with a particular emphasis on R
2 and R
3
. In order to represent linear and
nonlinear interactions between numerous variables, a number of methodologies,
including linear and nonlinear least squares methods, are being investigated. The
linear relationship that exists between two variables is broken down in great detail,
and a broad variety of examples are provided to show how curve fitting methods
can be utilized to build models that are an accurate representation of data sets. In
addition to this, the linear relationship that exists between the three variables under
consideration is dissected, and detailed strategies for dealing with this scenario
are discussed. Curve fitting methods are useful for exploring and evaluating data
in Euclidean spaces, as shown by the results and examples shown below, which
demonstrate the utility and versatility of these methods.
Keywords: R 2 space, R 3 space, curve fitting, least squares method, linear least squares method, linear relationship between multiple variables, linear relationship between three variables, linear relationship between two variables, nonlinear least squares method, nonlinear relationship between multiple variables, nonlinear relationship between two variables.
