论文标题
回归器：组合回归的C计划
Regressor: A C program for Combinatorial Regressions
论文作者
论文摘要
在统计数据中，研究人员使用回归模型来进行数据分析和预测，许多生产部门（行业，商业，学院等）。回归模型是数学函数，代表n自变量$ x_i \ in x $中的因变量$ y $的近似值。文献介绍了许多回归方法分为单个和多个回归。有几个程序来生成实施这些程序的回归模型以及商业和学术工具集。这项工作提出了一个名为“回归器”的开源程序，该程序从多项式回归的特定变化中制造了模型。这些模型将自变量联系起来，以生成原始输出依赖数据的近似值。在许多测试中，回归器能够构建比商业工具准确五倍的型号。
In statistics, researchers use Regression models for data analysis and prediction in many productive sectors (industry, business, academy, etc.). Regression models are mathematical functions representing an approximation of dependent variable $Y$ from n independent variables $X_i \in X$. The literature presents many regression methods divided into single and multiple regressions. There are several procedures to generate regression models and sets of commercial and academic tools that implement these procedures. This work presents one open-source program called Regressor that makes models from a specific variation of polynomial regression. These models relate the independent variables to generate an approximation of the original output dependent data. In many tests, Regressor was able to build models five times more accurate than commercial tools.
