Modelling using polynomial regression

被引：430

作者：

Ostertagova, Eva ^{[1
]}

机构：

[1] Tech Univ Kosice, Fac Elect Engn & Informat, Dept Math & Theoret Informat, Kosice 04200, Slovakia

来源：

MODELLING OF MECHANICAL AND MECHATRONICS SYSTEMS | 2012年 / 48卷

关键词：

multiple regression model; mean absolute percentage error; root mean squared error; R-squared; adjusted R-squared;

D O I：

10.1016/j.proeng.2012.09.545

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper is concentrated on the polynomial regression model, which is useful when there is reason to believe that relationship between two variables is curvilinear. The polynomial regression model has been applied using the characterisation of the relationship between strains and drilling depth. Parameters of the model were estimated using a least square method. After fitting, the model was evaluated using some of the common indicators used to evaluate accuracy of regression model. The data were analyzed using computer program MATLAB that performs these calculations. (C) 2012 Published by Elsevier Ltd.Selection and/or peer-review under responsibility of the Branch Office of Slovak Metallurgical Society at Faculty of Metallurgy and Faculty of Mechanical Engineering, Technical University of Kosice

引用

页码：500 / 506

页数：7

共 12 条

[1]

Aczel AD., 1989, Complete Business Statistics, P1056

[2]

Frankovsky P., 2010, METALURGIJA, V49, P208

[3]

Frankovsky P., 2010, THESIS KOSICE

[4]

Lewis C.D., 1982, International and business forecasting methods, V2, P194

[5]

Makridakis SG, 1998, Forecasting Methods and Applications

[6]

Novotny L., 2008, ACTA MECH SLOVACA, P607

[7]

Ostertag O., 2010, DEGRADATION PROCESSE

[8]

Ostertagova E., 2012, APPL STAT MATLAB, P193

[9]

Ostertagova E., 2005, PROBABILITY MATH STA, P123

[10]

Ostertagova E., 2011, APPL STAT, P161

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