Soft narrate  
◆ 
People will obtain many relating data of two or more than two dimension during experiments and production. These data will help them to solve problems of reality on contrary, which need data processing to make them become mathematics model reflecting the data variation regulation. The application of the Least Square Method can only make linear regression, but to the nonlinear problems it must construct relating mathematics relationship expression, namely mechanism model through procedure supposing to do linearization processing of mechanism model and then do regression modeling computation. Some relating data of the recursive models are good, but the data of reality are changeable, some deduce mechanism models. After the linear process the correlation property of the regression model is not good, and some relating data even can't deduce in the mechanism model. It is even more harder to build mathematics models. 

◆ 
Least Cubic Method solves problems that Least Square Method Data Regression met in the regression of relating data. Since the computers are widely used and applied in experiment, designing and production, it makes the regression computation based on the theory of least Cubic method into reality. People can not only process the mechanism model through the regression linearization processing better, but can also give a sound mathematics model to the relating data which can't deduce a mechanism models. 

Soft nomenclature 




Explanation 

◆ 
Two Dimensions Function:(x_{1} , x_{2})
During
the regression computing while x_{1}
is the variant, then x_{2}
is the objective function, if x_{2}
is the variant, x_{1}
is the objective function. 

◆ 
Three Dimensions Function:(x_{1} , x_{2} , x_{3}) During the regression computing, when x_{1} and x_{2} are variants, x_{3} is the objective function; if x_{2} and x_{3} are variants, then x_{1 }is the objective function. 

◆ 
Four Dimensions Function:(x_{1} , x_{2} , x_{3} , x_{4}) During the regression computing, when x_{1}, x_{2} and x_{3} are variants, x4 is the objective function, if x_{2}, x_{3} and x_{4} are variants, then x_{1} is the objective function. 

Exempli gratia 

◆ 
y = a_{0 } +
a_{1 }x_{1}^{k1} + a_{2}
x_{2}^{k2 } + a_{3
}x_{1}^{k3} x_{2}^{k4
} 

◆ 
