Prediction of Standard Times in Assembly Lines Using Least Squares in Multivariable Linear Models

Abstract We present a unified approach to the use of linear models and matrix least squares with the intention of providing a better understanding of the techniques themselves and of the statistics that arise from these techniques as they are used in clinical chemistry. Emphasis is placed on the importance of appropriate experimental designs and adequately precise measurement processes for efficiently obtaining the desired information.

Download Full-text

Fitting generalized linear models and their nonlinear extensions with least squares calculations

Computational Statistics & Data Analysis ◽

10.1016/0167-9473(88)90059-x ◽

1988 ◽

Vol 6 (1) ◽

pp. 7-19 ◽

Cited By ~ 2

Author(s):

W.Douglas Stirling

Keyword(s):

Least Squares ◽

Generalized Linear Models ◽

Linear Models ◽

Nonlinear Extensions

Download Full-text

Fuzzy C-Regression Models

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.278-280.1323 ◽

2013 ◽

Vol 278-280 ◽

pp. 1323-1326

Author(s):

Yan Hua Yu ◽

Li Xia Song ◽

Kun Lun Zhang

Keyword(s):

Linear Regression ◽

Least Squares ◽

Linear Models ◽

Least Squares Method ◽

Least Square Method ◽

Least Square ◽

Estimation Methods ◽

Statistical Regression ◽

Fuzzy Linear Regression ◽

Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

Download Full-text

Functional Linear Regression

10.1093/oxfordhb/9780199568444.013.2 ◽

2018 ◽

Author(s):

Hervé Cardot ◽

Pascal Sarda

Keyword(s):

Linear Regression ◽

Least Squares ◽

Linear Models ◽

Estimation Error ◽

Asymptotic Properties ◽

Principal Component Regression ◽

Principal Component ◽

Penalized Least Squares ◽

Open Problems ◽

Functional Linear Regression

This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

Download Full-text