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首页> 外文会议>30th annual national conference of the American Society for Engineering Management 2009 >COMPARING THE PREDICTIVE ABILITY OF T-METHOD, LINEAR REGRESSION METHOD AND COBB-DOUGLAS PRODUCTION FUNCTION FOR WARRANTY DATA
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COMPARING THE PREDICTIVE ABILITY OF T-METHOD, LINEAR REGRESSION METHOD AND COBB-DOUGLAS PRODUCTION FUNCTION FOR WARRANTY DATA

机译：T方法的预测能力，线性回归方法和保修数据的Cobb-Douglas生产函数的比较

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摘要

Predictive models are used in an attempt to anticipate future transitions, mitigate losses, and maximize economic gains. In today's market, companies look for high reliability and quality of products due to great market competition. Hence warranty data is of considerable interest to companies. Warranty shows the ability of a system or component to perform its functions within a given customer usage. Many statistical and data mining methods are available to predict the warranty data. This study focuses on analyzing the predictive efficiency of the T-method, Linear Regression Method, and Cobb-Douglas production function on warranty data by comparing their prediction capability. A case study for life of batteries is used for comparison and to demonstrate the benefits and limitations of each method. The T-method, developed by Genichi Taguchi, is founded upon the fundamentals of the Taguchi System of Quality Engineering which is used to calculate an overall prediction based on signal-to-noise ratio. Using this method, the required parameters are calculated to obtain an overall estimate of the true value of the output for each signal member (input). Linear regression analysis is also performed on the dataset. The output of this analysis is a linear equation which defines the change of the independent variable with respect to changes in the dependent variables. The Cobb-Douglas production function is then applied on the same dataset. The Cobb-Douglas functional form of production function is widely used in economics to represent the relationship of the output to inputs. The strength of the relationship is then assessed using the R-squared and adjusted R-squared values.
机译：预测模型用于尝试预测未来的过渡，减轻损失并最大化经济收益。在当今市场上，由于激烈的市场竞争，公司寻求高可靠性和高质量的产品。因此，保修数据对于公司而言非常重要。保修表示系统或组件在给定的客户使用情况下执行其功能的能力。许多统计和数据挖掘方法可用于预测保修数据。本研究着重通过比较T方法，线性回归方法和Cobb-Douglas生产函数对保修数据的预测效率，分析它们的预测能力。以电池寿命为例进行比较，并证明每种方法的优点和局限性。田口健一（Genichi Taguchi）开发的T方法基于田口质量工程系统的基础，该系统用于基于信噪比计算总体预测。使用此方法，可以计算所需的参数，以获得每个信号成员（输入）的输出真实值的整体估计。还对数据集执行线性回归分析。该分析的输出是一个线性方程，该线性方程定义了自变量相对于因变量变化的变化。然后将Cobb-Douglas生产函数应用于相同的数据集。生产函数的柯布-道格拉斯函数形式在经济学中被广泛用来表示产出与投入的关系。然后使用R平方和调整后的R平方值评估关系的强度。

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