Regression analysis is used to predict the dependent variables individually concerning the explanatory available variables and find out the “best fit” line or curve through a series of data in a graph. It is also minimizing the sum of the squares deviations of any experimental data points from the theoretical curve. An important aspect of this analysis is to ensure that the x values are as accu...

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity mode...

Vegetation models based on multiple logistic regression are of growing interest in environmental studies and decision making. The relatively simple sigmoid Gaussian optimum curves are most common in current vegetation models, although several different other response shapes are known. However, improvements in the technical means for handling statistical data now facilitate fast and interactive ...

in cite{p}, park introduced the quadratic $rho$-functional inequalitiesbegin{eqnarray}&& |f(x+y)+f(x-y)-2f(x)-2f(y)| && qquad le  left|rholeft(2 fleft(frac{x+y}{2}right) + 2 fleft(frac{x-y}{2}right)- f(x) -  f(y)right)right|,  nonumberend{eqnarray}where $rho$ is a fixed complex number with $|rho|andbegin{eqnarray}&& left|2 fleft(frac{x+y}{2}right) + 2 fleft(frac{x-y}{2}r...

This paper describes an Arc add-in for the delta method for regression models. The method works for linear models, generalized linear models, nonlinear models and also for generalized nonlinear models. The delta method is a procedure for finding estimates and approximate standard errors of arbitrary functions of normally distributed random variables. As implemented in Arc, the delta method is u...