CurFi：一个使用曲线拟合来寻找最佳回归分析模型的自动化工具
CurFi: An automated tool to find the best regression analysis model using curve fitting
摘要：
回归分析是一种众所周知的定量研究方法，主要探索一个或多个自变量与因变量之间的关系。在具有多个自变量的大型数据集上手动进行回归分析可能是乏味的。回归分析的自动化系统将对研究人员和非专家用户有很大的帮助。因此，本研究的目的是设计并开发一个自动化曲线拟合系统。作为结果，开发了一个名为“CurFi”的曲线拟合系统，该系统使用线性回归模型来将曲线拟合到数据集并找出最佳拟合模型。该系统便于上传数据集，将数据集分为训练集和测试集，从数据集中选择相关特征和标签;并且系统将在训练完成后返回最佳拟合线性回归模型。对于技术知识有限的用户来说，开发的工具将是一个很好的资源，他们也将能够使用开发的“CurFi”系统为数据集找到最适合的回归模型。
英文摘要：
Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system for regression analysis will be of great help for researchers as well as non-expert users. Thus, the objective of this research is to design and develop an automated curve fitting system. As outcome, a curve fitting system named "CurFi" was developed that uses linear regression models to fit a curve to a dataset and to find out the best fit model. The system facilitates to upload a dataset, split the dataset into training set and test set, select relevant features and label from the dataset; and the system will return the best fit linear regression model after training is completed. The developed tool would be a great resource for the users having limited technical knowledge who will also be able to find the best fit regression model for a dataset using the developed "CurFi" system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
