Página 1 dos resultados de 28432 itens digitais encontrados em 0.058 segundos

Uma revisão da análise de experimentos unifatoriais com tratamentos de natureza quantitativa: comparações múltiplas ou análise de regressão?; A review of the analysis of unifactorial experiments with quantitative treatments: Multiple Comparisons or Regression Analysis?

Rodrigues, Josiane
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 21/06/2011 PT
Relevância na Pesquisa
65.95%
O presente trabalho teve por objetivo fazer uma reflexão acerca do uso de testes de comparações múltiplas e da análise de regressão no estudo de experimentos unifatoriais cujos tratamentos são níveis de um fator quantitativo, para comparar os resultados e informações que são trazidas por cada uma dessas análises, verificando suas eventuais vantagens e limitações. De acordo com os objetivos propostos pelo presente trabalho, foi feita, depois de realizada a revisão bibliográfica sobre a análise de regressão e alguns dos testes de comparação de médias, um levantamento acerca de artigos cujo objetivo principal era o de fazer uma investigação de trabalhos publicados em jornais, revistas ou periódicos nos quais se utilizou algum procedimento de comparação de médias verificando assim a adequação desses testes às análises estatísticas realizadas. Essa revisão demonstrou que um número significativo de pesquisadores utiliza de procedimentos de comparações múltiplas em análises estatísticas de experimentos unifatoriais nos quais os tratamentos envolvidos são níveis de um fator quantitativo, o que é considerado por alguns como um procedimento inadequado. Assim sendo, foram analisados também dados de experimentos unifatoriais com tratamentos dessa ordem...

Comparing Double Minimization and Zigzag Algorithms in Joint Regression Analysis: the Complete Case

Pereira, Dulce
Fonte: Centro de Estatística e Aplicações(CEAUL); Instituto Nacional de Estatística (INE); International Statistical Institute (ISI) Publicador: Centro de Estatística e Aplicações(CEAUL); Instituto Nacional de Estatística (INE); International Statistical Institute (ISI)
Tipo: Parte de Livro Formato: 34117 bytes; application/pdf
ENG
Relevância na Pesquisa
65.83%
Joint Regression Analysis is a widely used technique for cultivar comparison. For each cultivar a linear regression is adjusted on a non observable regressor: the environmental index. This index measures, for each block, the corresponding productivity. When all cultivars are present in all the blocks in the field trials the series of experiments is complete. To carry out the minimization of the sum of sums of squares of residuals in order to estimate the coefficients of the regressions and the environmental indexes an iterative algorithm, the zigzag algorithm, see Mexia et al. (1999), was introduced. This algorithm performs well, see, e.g., Mexia et al. (2001) and Mexia & Pereira (2001), but it has not been shown that it converges to the absolute minimum of the goal function. We presented, see Pereira & Mexia (2007) an alternative algorithm and showed that, in the complete case, it converges to the absolute minimum. Through an example it was shown that the results obtained using both algorithms agreed. We now analyse the reason behind the agreement between both algorithms.

Comparing Double Minimization and Zigzag Algorithms in Joint Regression Analysis: the Complete Case

Pereira, Dulce
Fonte: Universidade de Évora Publicador: Universidade de Évora
Tipo: Aula Formato: 34117 bytes; application/pdf
ENG
Relevância na Pesquisa
65.83%
Joint Regression Analysis is a widely used technique for cultivar comparison. For each cultivar a linear regression is adjusted on a non observable regressor: the environmental index. This index measures, for each block, the corresponding productivity. When all cultivars are present in all the blocks in the field trials the series of experiments is complete. To carry out the minimization of the sum of sums of squares of residuals in order to estimate the coefficients of the regressions and the environmental indexes an iterative algorithm, the zigzag algorithm, see Mexia et al. (1999), was introduced. This algorithm performs well, see, e.g., Mexia et al. (2001) and Mexia & Pereira (2001), but it has not been shown that it converges to the absolute minimum of the goal function. We presented, see Pereira & Mexia (2007) an alternative algorithm and showed that, in the complete case, it converges to the absolute minimum. Through an example it was shown that the results obtained using both algorithms agreed. We now analyse the reason behind the agreement between both algorithms.

Overview of Joint Regression Analysis

Pereira, Dulce
Fonte: Universidade de Évora Publicador: Universidade de Évora
Tipo: Aula Formato: 135298 bytes; application/pdf
ENG
Relevância na Pesquisa
65.86%
Joint Regression Analysis (JRA) has been widely used to compare cultivars. In this technique a linear regression is adjusted per cultivar. The slope of each regression measures the ability of the corresponding cultivar to answer to variations in productivity. Presently we are manly interested in cultivars with better responses to high productivity. To extend the application range of JRA to connected series of designs in incomplete blocks, thus going beyond the classic case of series of randomized blocks, we introduced the L2 environmental indexes. Nowadays, comparison trials for cultivars are mainly ®-designs, which have in- complete blocks. Moreover, the introduction of these indexes: enables the inte- gration of JRA into the statistical inference for normal models; allows a better approach to the study of speci¯c interactions. These interactions occur when a cultivar behaves abnormally well or abnormally badly, for a (location , year) pair. We will also, use JRA to obtain and update of lists of recommended cultivars. Appropriate algorithms have been developed for the adjustments: the zig zag algorithm and the double minimization algorithm.

A comparison between joint regression analysis and the AMMI model: a case study with barley

Pereira, Dulce G.; Rogrigues, Paulo C.; Mejza, Stanislaw; Mexia, João T.
Fonte: Taylor & Francis Publicador: Taylor & Francis
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.81%
Joint regression analysis (JRA) and additive main effects and multiplicative interaction (AMMI) models are compared in order to (i) access the capacity of describing a genotype by environment interaction effects and (ii) evaluate the agreement between the winners of mega-environments obtained from the AMMI analysis and the genotypes in the upper contour of the JRA. An iterative algorithm is used to obtain the environmental indexes for JRA, and standard multiple comparison procedures are adapted for genotype comparison and selection. The study includes three data sets from spring barley (Hordeum vulgare L.) breeding program carried out between 2004 and 2006 in Czech Republic. The results from both techniques are integrated in order to advice plant breeders, farmers and agronomists for better genotype selection and prediction for new years and/or new environments.

A comparison between Joint Regression Analysis and the Additive Main and Multiplicative Interaction model: the robustness with increasing amounts of missing data

Rodrigues, Paulo C.; Pereira, Dulce G.; Mexia, João T.
Fonte: Piracicaba, Braz. Publicador: Piracicaba, Braz.
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.83%
This paper joins the main properties of joint regression analysis (JRA), a model based on the Finlay-Wilkinson regression to analyse multi-environment trials, and of the additive main effects and multiplicative interaction (AMMI) model. The study compares JRA and AMMI with particular focus on robustness with increasing amounts of randomly selected missing data. The application is made using a data set from a breeding program of durum wheat (Triticum turgidum L., Durum Group) conducted in Portugal. The results of the two models result in similar dominant cultivars (JRA) and winner of mega-environments (AMMI) for the same enironments. However, JRA had more stable results with the increase in the incidence rates of missing values.

A comparison between joint regression analysis and the AMMI model: a case study with barley

Pereira, Dulce G.; Rodrigues, Paulo C.; Mejza, Stanislaw; Mexia, João T.
Fonte: Taylor & Francis Publicador: Taylor & Francis
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.81%
Joint regression analysis (JRA) and additive main effects and multiplicative interaction (AMMI) models are compared in order to (i) access the ability of describing a genotype by environment interaction effects and (ii) evaluate the agreement between the winners of mega-environments obtained from the AMMI analysis and the genotypes in the upper contour of the JRA. An iterative algorithm is used to obtain the environmental indexes for JRA, and standard multiple comparison procedures are adapted for genotype comparison and selection. This study includes three data sets from a spring barley (Hordeum vulgare L.) breeding programme carried out between 2004 and 2006 in Czech Republic. The results from booth techniques are integrated in order to advice plant breeders, farmers and agronomists for better genotype selection and prediction for new years and/or new environments.

A comparison between Joint Regression Analysis and Additive Main and Multiplicative Interaction model: the robustness with increasing amounts of missing data

Rodrigues, Paulo Canas; Pereira, Dulce Gamito; Mexia, João Tiago
Fonte: Piracicaba, Braz. Publicador: Piracicaba, Braz.
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.83%
This paper joins the main properties of joint regression analysis (JRA), a model based on the Finlay-Wilkinson regression to analyse multi-environment trials, and of the additive main effects and multiplicative interaction (AMMI) model. The study compares JRA and AMMI with particular focus on robustness with increasing amounts of randomly selected missing data. The application is made using a data set from a breeding program of durum wheat (Triticum turgidum L., Durum Group) conducted in Portugal. The results of the two models result in similar dominat cultivars (JRA) and winner of mega-environments (AMMI) for the same environments. However, JRA had more stable results with the increase in the incidence rates of missing values.

Joint Regression Analysis and Completed Joint Regression Analysis

Pereira, Dulce G.; Rodrigues, Paulo C.; Oliveira, Amílcar; Mexia, João T.
Fonte: Nova Science Publishers, Inc Publicador: Nova Science Publishers, Inc
Tipo: Parte de Livro
POR
Relevância na Pesquisa
65.97%
Joint Regression Analysis (JRA) is a well known technique for the joint analysis of series of cultivar comparison trials. Formerly these trials were usually designed as complete randomized block designs. Now, the -designs are the mostly used. To perform a JRA, a linear regression is adjusted for each cultivar. The dependent variable is continuous (usually the yield) and there is a non observable regressor, the environmental index, which measures the productivity of the plots in which the field trials are divided. An algorithm, the zig-zag, was developed in order to adjust both the regression coefficients and the environmental indexes. Thus the adjusted regression lines, when represented simultaneously, define a convex polygonal, the upper contour, which may be used for cultivar comparison and selection. The cultivars whose adjusted regressions partake in the upper contour will be called dominant. For each dominant cultivar there will be a dominance range and the non dominant cultivars should be compared with the dominant ones within their dominance ranges. In this chapter we intend to apply the JRA to an oat breeding program and introduce the completed JRA which may be used when additional information, besides yields (such as a density measure which represents the economic value)...

Comparing double minimization and zigzag algorithms in Joint Regression Analysis: the complete case

Pereira, Dulce G.; Mexia, João T.
Fonte: Taylor & Francis Publicador: Taylor & Francis
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.83%
Joint Regression Analysis is a widely used technique for cultivar comparison. For each cultivar a linear regression is adjusted on a non observable regressor: the environmental index. This index measures, for each block, the corresponding productivity. When all cultivars are present in all the blocks in the field trials the series of experiments is complete. To carry out the minimization of the sum of sums of squares of residuals in order to estimate the coefficients of the regressions and the environmental indexes an iterative algorithm, the zigzag algorithm was introduced, see Mexia et al. (1999). This algorithm performs well, see, e.g., Mexia et al. (2001) and Mexia and Pereira (2001), but it has not been shown that it converges to the absolute minimum of the goal function. We presented, see Pereira and Mexia (2008) an alternative algorithm and showed that, in the complete case, it converges to the absolute minimum. Through an example it was shown that the results obtained using both algorithms agreed. We now analyse the reason behind the agreement between both algorithms.

Joint Regression Analysis and Incorporation of Environmental Variables in Stochastic Frontier Production Function: An Application to Experimental Data of Winter Rye

Pereira, D. G.; Sampaio, Ana
Fonte: Springer Berlin Heidelberg Publicador: Springer Berlin Heidelberg
Tipo: Parte de Livro
ENG
Relevância na Pesquisa
65.88%
This chapter joins the main properties of two specific regression techniques, joint-regression analysis (JRA) and stochastic frontier approach (SFA) in the analysis of experimental data sets from a breeding program of winter rye (Secale cereale L.), conducted in Poland, Research Center for Cultivar Testing de Słupia Wielka, over the period 1997–1998. With JRA, a meta-model, based on several linear regressions, had been estimated in order to analyze multilocation trials of winter rye production and to select the best cultivars (more productive) for a related stratum (locality/genotype). With SFA, another regression model had been investigated to predict production rankings of cultivars, through individual efficiency estimates. These measures resulted from a stochastic production frontier on experimental data of production and different climate conditions. Both techniques show similar dominant cultivars for the same environments.

A well-log regression analysis for P-wave velocity prediction in the namorado oil field, Campos basin

Augusto,Fabrício de Oliveira Alves; Martins,Jorge Leonardo
Fonte: Sociedade Brasileira de Geofísica Publicador: Sociedade Brasileira de Geofísica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2009 EN
Relevância na Pesquisa
65.83%
Geophysical well log measurements are key information for the development of oil and gas reservoirs. However, the absence of a certain fundamental well log, for instance, the compressional-wave (P-wave) sonic log, prevents the application of specific risk-assessment techniques. Therefore, the application of methodologies for estimating log records absent in wells is of great importance in the reservoir characterization and development procedures. In this paper, we use the regression analysis methodology for estimating P-wave sonic log measurements. Effective porosity, shaliness and electrical resistivity are established, individually or in a combined way, as the parameters for describing P-wave velocity variation in Namorado oil field, Campos basin. Two general equations provide 28 empirical models with potential use in estimating P-wave velocity variation from well log measurements. Application of least-squares technique leads to the determination of lithology-related regression coefficients at the surroundings of two wells chosen for verifying the empirical models. The results show the equivalence of both general equations used for obtaining empirical models for P-wave velocity estimation. As confirmation of papers published previously...

A comparison between Joint Regression Analysis and the Additive Main and Multiplicative Interaction model: the robustness with increasing amounts of missing data

Rodrigues,Paulo Canas; Pereira,Dulce Gamito Santinhos; Mexia,João Tiago
Fonte: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz" Publicador: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2011 EN
Relevância na Pesquisa
65.83%
This paper joins the main properties of joint regression analysis (JRA), a model based on the Finlay-Wilkinson regression to analyse multi-environment trials, and of the additive main effects and multiplicative interaction (AMMI) model. The study compares JRA and AMMI with particular focus on robustness with increasing amounts of randomly selected missing data. The application is made using a data set from a breeding program of durum wheat (Triticum turgidum L., Durum Group) conducted in Portugal. The results of the two models result in similar dominant cultivars (JRA) and winner of mega-environments (AMMI) for the same environments. However, JRA had more stable results with the increase in the incidence rates of missing values.

Modelling and optimization of the surface roughness in the dry turning of the cold rolled alloyed steel using regression analysis

Tanikić,Dejan; Marinković,Velibor
Fonte: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM Publicador: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2012 EN
Relevância na Pesquisa
65.86%
Surface quality of the machined parts is one of the most important product quality indicators and one of the most frequent customer requirements. The average surface roughness (Ra) represents a measure of the surface quality, and it is mostly influenced by the following cutting parameters: the cutting speed, the feed rate, and the depth of cut. Quantifying the relationship between surface roughness and cutting parameters is a very important task. In this study regression analysis was used for modelling and optimization of the surface roughness in dry single-point turning of the alloyed steel, using coated tungsten carbide inserts. The experiment has been designed and carried out on the basis of a three-level full factorial design. The linear, the quadratic and the power (non-linear) mathematical models were selected for the analysis. Obtained results are in good accordance with the experimentally obtained data, confirming the effectiveness of regression analysis in modelling and optimization of surface roughness in the turning process. The general conclusion is that the surface roughness has a clear downward trend with the cutting speed increase and decrease in the feed rate and the depth of cut.

Entrepreneurship Programs in Developing Countries : A Meta Regression Analysis

Cho, Yoonyoung; Honorati, Maddalena
Fonte: World Bank, Washington, DC Publicador: World Bank, Washington, DC
EN_US
Relevância na Pesquisa
65.82%
This paper provides a synthetic and systematic review on the effectiveness of various entrepreneurship programs in developing countries. It adopts a meta-regression analysis using 37 impact evaluation studies that were in the public domain by March 2012, and draws out several lessons on the design of the programs. The paper observes wide variation in program effectiveness across different interventions depending on outcomes, types of beneficiaries, and country context. Overall, entrepreneurship programs have a positive and large impact for youth and on business knowledge and practice, but no immediate translation into business set-up and expansion or increased income. At a disaggregate level by outcome groups, providing a package of training and financing is more effective for labor activities. In addition, financing support appears more effective for women and business training for existing entrepreneurs than other interventions to improve business performance.

Imputed Welfare Estimates in Regression Analysis

Elbers, Chris; Lanjouw, Jean O.; Lanjouw, Peter
Fonte: World Bank, Washington, D.C. Publicador: World Bank, Washington, D.C.
EN_US
Relevância na Pesquisa
65.95%
The authors discuss the use of imputed data in regression analysis, in particular the use of highly disaggregated welfare indicators (from so-called "poverty maps"). They show that such indicators can be used both as explanatory variables on the right-hand side and as the phenomenon to explain on the left-hand side. The authors try out practical ways of adjusting standard errors of the regression coefficients to reflect the error introduced by using imputed, rather than actual, welfare indicators. These are illustrated by regression experiments based on data from Ecuador. For regressions with imputed variables on the left-hand side, the authors argue that essentially the same aggregate relationships would be found with either actual or imputed variables. They address the methodological question of how to interpret aggregate relationships found in such regressions.

Prevalence of Treponema Species Detected in Endodontic Infections: Systematic Review and Meta-regression Analysis

Leite, Fabio R. M.; Nascimento, Gustavo G.; Demarco, Flavio F.; Gomes, Brenda P. F. A.; Pucci, Cesar R.; Martinho, Frederico C.
Fonte: Elsevier B.V. Publicador: Elsevier B.V.
Tipo: Revisão Formato: 579-587
ENG
Relevância na Pesquisa
65.9%
Introduction: This systematic review and meta-regression analysis aimed to calculate a combined prevalence estimate and evaluate the prevalence of different Treponema species in primary and secondary endodontic infections, including symptomatic and asymptomatic eases. Methods: The MEDLINE/PubMed, Embase, Scielo, Web of Knowledge, and Scopus data-bases were searched without starting date restriction up to and including March 2014. Only reports in English were included. The selected literature was reviewed by 2 authors and classified as suitable or not to be included in this review. Lists were compared, and, in case of disagreements, decisions were made after a discussion based on inclusion and exclusion criteria. A pooled prevalence of Treponema species in endodontic infections was estimated. Additionally, a meta-regression analysis was performed. Results: Among the 265 articles identified in the initial search, only 51 were included in the final analysis. The studies were classified into 2 different groups according to the type of endodontic infection and whether it was an exclusively primary/secondary study (n = 36) or a primary/secondary comparison (n = 15). The pooled prevalence of Treponema species was 41.5% (95% confidence interval...

Overview of Joint Regression Analysis

Pereira, Dulce
Fonte: Institut d'Estadística de Catalunya, IDESCAT Publicador: Institut d'Estadística de Catalunya, IDESCAT
Tipo: Parte de Livro Formato: 135298 bytes; application/pdf
ENG
Relevância na Pesquisa
65.86%
Joint Regression Analysis (JRA) has been widely used to compare cultivars. In this technique a linear regression is adjusted per cultivar. The slope of each regression measures the ability of the corresponding cultivar to answer to variations in productivity. Presently we are manly interested in cultivars with better responses to high productivity. To extend the application range of JRA to connected series of designs in incomplete blocks, thus going beyond the classic case of series of randomized blocks, we introduced the L2 environmental indexes. Nowadays, comparison trials for cultivars are mainly ®-designs, which have in- complete blocks. Moreover, the introduction of these indexes: enables the inte- gration of JRA into the statistical inference for normal models; allows a better approach to the study of speci¯c interactions. These interactions occur when a cultivar behaves abnormally well or abnormally badly, for a (location , year) pair. We will also, use JRA to obtain and update of lists of recommended cultivars. Appropriate algorithms have been developed for the adjustments: the zig zag algorithm and the double minimization algorithm.

A comparison between Joint Regression Analysis and the Additive Main and Multiplicative Interaction model: the robustness with increasing amounts of missing data

Rodrigues, Paulo Canas; Pereira, Dulce Gamito Santinhos; Mexia, João Tiago
Fonte: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz Publicador: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; ; ; ; Formato: application/pdf
Publicado em 01/12/2011 ENG
Relevância na Pesquisa
65.83%
This paper joins the main properties of joint regression analysis (JRA), a model based on the Finlay-Wilkinson regression to analyse multi-environment trials, and of the additive main effects and multiplicative interaction (AMMI) model. The study compares JRA and AMMI with particular focus on robustness with increasing amounts of randomly selected missing data. The application is made using a data set from a breeding program of durum wheat (Triticum turgidum L., Durum Group) conducted in Portugal. The results of the two models result in similar dominant cultivars (JRA) and winner of mega-environments (AMMI) for the same environments. However, JRA had more stable results with the increase in the incidence rates of missing values.

PARAMETERS SELECTION OF TIME-INTENSITY CURVES IN SUGAR CANE SPIRITS AGED AND NON-AGED IN OAK BARRELS (Quercus alba L.) BY STEPWISE DISCRIMINANT AND REGRESSION ANALYSIS; SELEÇÃO DE PARÂMETROS DE CURVAS TEMPO-INTENSIDADE DE AGUARDENTES DE CANA SEM ENVELHECIMENTO E ENVELHECIDAS EM TONÉIS DE CARVALHO (Quercus alba L.) POR ANÁLISE DISCRIMINANTE POR PASSOS E ANÁLISE DE CORRELAÇÃO

CARDELLO, HELENA MARIA ANDRÉ BOLINI; Faculdade de Ciências Farmacêuticas, UNESP; FARIA, JOÃO BOSCO; Faculdade de Ciências Farmacêuticas, UNESP
Fonte: UFPR Publicador: UFPR
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; Formato: application/pdf
Publicado em 01/04/2009 POR
Relevância na Pesquisa
65.84%
It was verified the possibility to reduce a number of parameters necessary for the time-intensity-analysis in the sensorial characteristics of aged sugar cane spirits (“cachaça”). It was made a statistical study of all possible parameters, which were submitted to stepwise discriminant and regression analysis. It was sampled aged “cachaças” in oak barrels at zero, 12, 24, 36 and 48 months and six commercial brands, being three aged and three not, for the evaluation of sweetness, aggressive alcoholic taste and wood taste. The parameters obtained from each curve were: time to reach the maximal intensity (TImax), time in which the maximal intensity begins to decline (Td), duration time of the maximal intensity (Plato), total area under the curve (Area), total duration time of incentive (Ttot) e maximal intensity noticed (Imax). The results suggested that the parameters Imax and Plato could be eliminated in the  time-intensity analysis of the sweet taste and wood taste, respectively without changing the final results of the analysis.; Verificou-se a possibilidade de reduzir o número de parâmetros necessários em análise tempo intensidade (T-I) de características sensoriais de aguardente de cana...