ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488142

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

摘要:

This article deals with some techniques for analyzing ordered categorical data from industrial experiments for quality improvement. Taguchi's accumulation analysis method is shown to have reasonable power fordetecting important location effects; however, it is an unnecessarily complicated procedure. For detecting dispersion effects, it need not even be as powerful as Pearson's chi-squared test. Instead two simple and easy to use scoring schemes are suggested for identifying the location and dispersion effects separately. The techniques are illustrated on data from an experiment to optimize the process of forming contact windows in complementary metal-oxide semiconductor circuits.

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488143

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488144

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488145

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488146

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488147

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488148

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

摘要:

A simple way of encouraging a nonlinear effect in a regression model to show itself in a partial residual (parres) plot (component-plus-residual plot) is to include a single quadratic term in the regression. It is computationally cheap to do this for each of the independent variables. Examples show that the resulting set ofaugmented partial residualplots gives insights that are not available from standard parres or added-variable plots.

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488149

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

摘要:

In model-building investigations the response of interestyis often a function of two or more components. The usual procedure in such situations is to use this function to compute the values ofyfor each run and then obtain the fitted equation ŷ =F(x1,x2, …,xk) relating ŷ to relevant process variables. In this article we suggest a different method that will sometimes be preferable: first fit equations to the component responses (either individually or in subsets) and then combine these equations to obtain the desired functionF. The proposed method is especially useful when the usual procedure fails to produce a satisfactory model. A chemical example illustrates how, by using this method, a second-order model can be obtained from a first-order design (a two-level factorial design).

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488150

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor

摘要:

Analysis of a process with continuous sample curves can be carried out in a manner similar to principal components analysis of vector processes. By appropriate definition of a best linear model in the continuous case, we show that principal modes of variation consist of eigenfunctions of the process covariance functionC(s,t). Procedures for estimation of these eigenfunctions from a finite sample of observed curves are given, and results are compared with principal components analysis of the same data.

ISSN:0040-1706    

DOI:10.1080/00401706.1986.10488151

出版商:Taylor & Francis Group    

年代:1986

数据来源: Taylor