Relevant bibliographies by topics / ANOVA (Analysis of variance)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'ANOVA (Analysis of variance)'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "ANOVA (Analysis of variance)"

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St»hle, Lars, and Svante Wold. "Analysis of variance (ANOVA)." Chemometrics and Intelligent Laboratory Systems 6, no. 4 (1989): 259–72. http://dx.doi.org/10.1016/0169-7439(89)80095-4.

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Thompson, Hilary W., Robertino Mera, and Chandan Prasad. "The Analysis of Variance (ANOVA)." Nutritional Neuroscience 2, no. 1 (1999): 43–55. http://dx.doi.org/10.1080/1028415x.1999.11747262.

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Maxwell, Scott E., Harold D. Delaney, and Jerry M. Manheimer. "Anova of Residuals and Ancova: Correcting an Illusion by Using Model Comparisons and Graphs." Journal of Educational Statistics 10, no. 3 (1985): 197–209. http://dx.doi.org/10.3102/10769986010003197.

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Analysis of covariance is often conceptualized as an analysis of variance of a single set of residual scores that are obtained by regressing the dependent variable on the covariate. Although this conceptualization of an equivalence between the two procedures may be intuitively appealing, it is mathematically incorrect. If residuals are obtained from the pooled within-groups regression coefficient ( bw), an analysis of variance on the residuals results in an inflated α-level. If the regression coefficient for the total sample combined into one group ( bT) is used, ANOVA on the residuals yields an inappropriately conservative test. In either case, analysis of variance of residuals fails to provide a correct test, because the significance test in analysis of covariance requires consideration of both bw and bT, unlike analysis of residuals. It is recommended that the significance test of treatment effects in analysis of covariance be conceptualized, not as an analysis of residuals, but as a comparison of models whose parameters are estimated by the principle of least squares. Focusing on model comparisons and their associated graphs can be used effectively here as in other cases to teach simply and correctly the logic of the statistical test.
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Shelton, Heather K. "BASIC PREMISES OF FACTORIAL ANALYSIS OF VARIANCE (ANOVA)." Experimental Techniques 27, no. 6 (2003): 64–66. http://dx.doi.org/10.1111/j.1747-1567.2003.tb00142.x.

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Emerson, Robert Wall. "MANOVA (Multivariate Analysis of Variance): An Expanded Form of the ANOVA (Analysis of Variance)." Journal of Visual Impairment & Blindness 112, no. 1 (2018): 125–26. http://dx.doi.org/10.1177/0145482x1811200113.

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Reed, James F. "Analysis of Variance (ANOVA) Models in Lower Extremity Wounds." International Journal of Lower Extremity Wounds 2, no. 2 (2003): 87–95. http://dx.doi.org/10.1177/1534734603256075.

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Fitzgerald, Shawn M., and Sharon Flinn. "Evaluating Research Studies Using the Analysis of Variance (ANOVA)." Journal of Hand Therapy 13, no. 1 (2000): 56–60. http://dx.doi.org/10.1016/s0894-1130(00)80054-x.

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Aarts, Sil, and Eveline Wouters. "De t-toets en de analysis of variance, ANOVA." Podosophia 26, no. 1 (2018): 28–33. http://dx.doi.org/10.1007/s12481-018-0187-8.

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Rasmussen, Jeffrey Lee. "ANOVA MultiMedia: A Program for Teaching ANOVA Designs." Teaching of Psychology 23, no. 1 (1996): 55–56. http://dx.doi.org/10.1207/s15328023top2301_15.

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A multimedia program for teaching analysis of variance (ANOVA) designs is described. The program tests students' understanding of independent groups and repeated measures variables, as well as the source and degrees of freedom columns of the ANOVA source table. The program uses colorful images, brief animations, interactive tasks, and immediate feedback. Information about availability is provided.
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Lakens, Daniël, and Aaron R. Caldwell. "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs." Advances in Methods and Practices in Psychological Science 4, no. 1 (2021): 251524592095150. http://dx.doi.org/10.1177/2515245920951503.

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Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analyses often need [Formula: see text] or Cohen’s f as input, but these effect sizes are not intuitive and do not generalize to different experimental designs. We have created the R package Superpower and online Shiny apps to enable researchers without extensive programming experience to perform simulation-based power analysis for ANOVA designs of up to three within- or between-participants factors. Predicted effects are entered by specifying means, standard deviations, and, for within-participants factors, the correlations. The simulation provides the statistical power for all ANOVA main effects, interactions, and individual comparisons. The software can plot power across a range of sample sizes, can control for multiple comparisons, and can compute power when the homogeneity or sphericity assumption is violated. This Tutorial demonstrates how to perform a priori power analysis to design informative studies for main effects, interactions, and individual comparisons and highlights important factors that determine the statistical power for factorial ANOVA designs.
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Dissertations / Theses on the topic "ANOVA (Analysis of variance)"

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Prosser, Robert James. "Robustness of multivariate mixed model ANOVA." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25511.

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In experimental or quasi-experimental studies in which a repeated measures design is used, it is common to obtain scores on several dependent variables on each measurement occasion. Multivariate mixed model (MMM) analysis of variance (Thomas, 1983) is a recently developed alternative to the MANOVA procedure (Bock, 1975; Timm, 1980) for testing multivariate hypotheses concerning effects of a repeated factor (called occasions in this study) and interaction between repeated and non-repeated factors (termed group-by-occasion interaction here). If a condition derived by Thomas (1983), multivariate multi-sample sphericity (MMS), regarding the equality and structure of orthonormalized population covariance matrices is satisfied (given multivariate normality and independence for distributions of subjects' scores), valid likelihood-ratio MMM tests of group-by-occasion interaction and occasions hypotheses are possible. To date, no information has been available concerning actual (empirical) levels of significance of such tests when the MMS condition is violated. This study was conducted to begin to provide such information. Departure from the MMS condition can be classified into three types— termed departures of types A, B, and C respectively: (A) the covariance matrix for population ℊ (ℊ = 1,...G), when orthonormalized, has an equal-diagonal-block form but the resulting matrix for population ℊ is unequal to the resulting matrix for population ℊ' (ℊ ≠ ℊ'); (B) the G populations' orthonormalized covariance matrices are equal, but the matrix common to the populations does not have equal-diagonal-block structure; or (C) one or more populations has an orthonormalized covariance matrix which does not have equal-diagonal-block structure and two or more populations have unequal orthonormalized matrices. In this study, Monte Carlo procedures were used to examine the effect of each type of violation in turn on the Type I error rates of multivariate mixed model tests of group-by-occasion interaction and occasions null hypotheses. For each form of violation, experiments modelling several levels of severity were simulated. In these experiments: (a) the number of measured variables was two; (b) the number of measurement occasions was three; (c) the number of populations sampled was two or three; (d) the ratio of average sample size to number of measured variables was six or 12; and (e) the sample size ratios were 1:1 and 1:2 when G was two, and 1:1:1 and 1:1:2 when G was three. In experiments modelling violations of types A and C, the effects of negative and positive sampling were studied. When type A violations were modelled and samples were equal in size, actual Type I error rates did not differ significantly from nominal levels for tests of either hypothesis except under the most severe level of violation. In type A experiments using unequal groups in which the largest sample was drawn from the population whose orthogonalized covariance matrix has the smallest determinant (negative sampling), actual Type I error rates were significantly higher than nominal rates for tests of both hypotheses and for all levels of violation. In contrast, empirical levels of significance were significantly lower than nominal rates in type A experiments in which the largest sample was drawn from the population whose orthonormalized covariance matrix had the largest determinant (positive sampling). Tests of both hypotheses tended to be liberal in experiments which modelled type B violations. No strong relationships were observed between actual Type I error rates and any of: severity of violation, number of groups, ratio of average sample size to number of variables, and relative sizes of samples. In equal-groups experiments modelling type C violations in which the orthonormalized pooled covariance matrix departed at the more severe level from equal-diagonal-block form, actual Type I error rates for tests of both hypotheses tended to be liberal. Findings were more complex under the less severe level of structural departure. Empirical significance levels did not vary with the degree of interpopulation heterogeneity of orthonormalized covariance matrices. In type C experiments modelling negative sampling, tests of both hypotheses tended to be liberal. Degree of structural departure did not appear to influence actual Type I error rates but degree of interpopulation heterogeneity did. Actual Type I error rates in type C experiments modelling positive sampling were apparently related to the number of groups. When two populations were sampled, both tests tended to be conservative, while for three groups, the results were more complex. In general, under all types of violation the ratio of average group size to number of variables did not greatly affect actual Type I error rates. The report concludes with suggestions for practitioners considering use of the MMM procedure based upon the findings and recommends four avenues for future research on Type I error robustness of MMM analysis of variance. The matrix pool and computer programs used in the simulations are included in appendices.<br>Education, Faculty of<br>Educational and Counselling Psychology, and Special Education (ECPS), Department of<br>Graduate
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Halldestam, Markus. "ANOVA - The Effect of Outliers." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295864.

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This bachelor’s thesis focuses on the effect of outliers on the one-way analysis of variance and examines whether the estimate in ANOVA is robust and whether the actual test itself is robust from influence of extreme outliers. The robustness of the estimates is examined using the breakdown point while the robustness of the test is examined by simulating the hypothesis test under some extreme situations. This study finds evidence that the estimates in ANOVA are sensitive to outliers, i.e. that the procedure is not robust. Samples with a larger portion of extreme outliers have a higher type-I error probability than the expected level.
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Patrick, Joshua Daniel. "Simulations to analyze Type I error and power in the ANOVA F test and nonparametric alternatives." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000158.

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Lind, Ingela. "Regressor and Structure Selection : Uses of ANOVA in System Identification." Doctoral thesis, Linköping : Linköpings universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7000.

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An, Qian. "A Monte Carlo study of several alpha-adjustment procedures using a testing multiple hypotheses in factorial anova." Ohio : Ohio University, 2010. http://www.ohiolink.edu/etd/view.cgi?ohiou1269439475.

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Jordaan, Aletta Gertruida. "Empirical Bayes estimation of the extreme value index in an ANOVA setting." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86216.

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Thesis (MComm)-- Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Extreme value theory (EVT) involves the development of statistical models and techniques in order to describe and model extreme events. In order to make inferences about extreme quantiles, it is necessary to estimate the extreme value index (EVI). Numerous estimators of the EVI exist in the literature. However, these estimators are only applicable in the single sample setting. The aim of this study is to obtain an improved estimator of the EVI that is applicable to an ANOVA setting. An ANOVA setting lends itself naturally to empirical Bayes (EB) estimators, which are the main estimators under consideration in this study. EB estimators have not received much attention in the literature. The study begins with a literature study, covering the areas of application of EVT, Bayesian theory and EB theory. Different estimation methods of the EVI are discussed, focusing also on possible methods of determining the optimal threshold. Specifically, two adaptive methods of threshold selection are considered. A simulation study is carried out to compare the performance of different estimation methods, applied only in the single sample setting. First order and second order estimation methods are considered. In the case of second order estimation, possible methods of estimating the second order parameter are also explored. With regards to obtaining an estimator that is applicable to an ANOVA setting, a first order EB estimator and a second order EB estimator of the EVI are derived. A case study of five insurance claims portfolios is used to examine whether the two EB estimators improve the accuracy of estimating the EVI, when compared to viewing the portfolios in isolation. The results showed that the first order EB estimator performed better than the Hill estimator. However, the second order EB estimator did not perform better than the “benchmark” second order estimator, namely fitting the perturbed Pareto distribution to all observations above a pre-determined threshold by means of maximum likelihood estimation.<br>AFRIKAANSE OPSOMMING: Ekstreemwaardeteorie (EWT) behels die ontwikkeling van statistiese modelle en tegnieke wat gebruik word om ekstreme gebeurtenisse te beskryf en te modelleer. Ten einde inferensies aangaande ekstreem kwantiele te maak, is dit nodig om die ekstreem waarde indeks (EWI) te beraam. Daar bestaan talle beramers van die EWI in die literatuur. Hierdie beramers is egter slegs van toepassing in die enkele steekproef geval. Die doel van hierdie studie is om ’n meer akkurate beramer van die EWI te verkry wat van toepassing is in ’n ANOVA opset. ’n ANOVA opset leen homself tot die gebruik van empiriese Bayes (EB) beramers, wat die fokus van hierdie studie sal wees. Hierdie beramers is nog nie in literatuur ondersoek nie. Die studie begin met ’n literatuurstudie, wat die areas van toepassing vir EWT, Bayes teorie en EB teorie insluit. Verskillende metodes van EWI beraming word bespreek, insluitend ’n bespreking oor hoe die optimale drempel bepaal kan word. Spesifiek word twee aanpasbare metodes van drempelseleksie beskou. ’n Simulasiestudie is uitgevoer om die akkuraatheid van beraming van verskillende beramingsmetodes te vergelyk, in die enkele steekproef geval. Eerste orde en tweede orde beramingsmetodes word beskou. In die geval van tweede orde beraming, word moontlike beramingsmetodes van die tweede orde parameter ook ondersoek. ’n Eerste orde en ’n tweede orde EB beramer van die EWI is afgelei met die doel om ’n beramer te kry wat van toepassing is vir die ANAVA opset. ’n Gevallestudie van vyf versekeringsportefeuljes word gebruik om ondersoek in te stel of die twee EB beramers die akkuraatheid van beraming van die EWI verbeter, in vergelyking met die EWI beramers wat verkry word deur die portefeuljes afsonderlik te ontleed. Die resultate toon dat die eerste orde EB beramer beter gevaar het as die Hill beramer. Die tweede orde EB beramer het egter slegter gevaar as die tweede orde beramer wat gebruik is as maatstaf, naamlik die passing van die gesteurde Pareto verdeling (PPD) aan alle waarnemings bo ’n gegewe drempel, met behulp van maksimum aanneemlikheidsberaming.
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Zhang, Bairu. "Functional data analysis in orthogonal designs with applications to gait patterns." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/44698.

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This thesis presents a contribution to the active research area of functional data analysis (FDA) and is concerned with the analysis of data from complex experimental designs in which the responses are curves. High resolution, closely correlated data sets are encountered in many research fields, but current statistical methodologies often analyse simplistic summary measures and therefore limit the completeness and accuracy of conclusions drawn. Specifically the nature of the curves and experimental design are not taken into account. Mathematically, such curves can be modelled either as sample paths of a stochastic process or as random elements in a Hilbert space. Despite this more complex type of response, the structure of experiments which yield functional data is often the same as in classical experimentation. Thus, classical experimental design principles and results can be adapted to the FDA setting. More specifically, we are interested in the functional analysis of variance (ANOVA) of experiments which use orthogonal designs. Most of the existing functional ANOVA approaches consider only completely randomised designs. However, we are interested in more complex experimental arrangements such as, for example, split-plot and row-column designs. Similar to univariate responses, such complex designs imply that the response curves for different observational units are correlated. We use the design to derive a functional mixed-effects model and adapt the classical projection approach in order to derive the functional ANOVA. As a main result, we derive new functional F tests for hypotheses about treatment effects in the appropriate strata of the design. The approximate null distribution of these tests is derived by applying the Karhunen- Lo`eve expansion to the covariance functions in the relevant strata. These results extend existing work on functional F tests for completely randomised designs. The methodology developed in the thesis has wide applicability. In particular, we consider novel applications of functional F tests to gait analysis. Results are presented for two empirical studies. In the first study, gait data of patients with cerebral palsy were collected during barefoot walking and walking with ankle-foot orthoses. The effects of ankle-foot orthoses are assessed by functional F tests and compared with pointwise F tests and the traditional univariate repeated-measurements ANOVA. The second study is a designed experiment in which a split-plot design was used to collect gait data from healthy subjects. This is commonly done in gait research in order to better understand, for example, the effects of orthoses while avoiding confounded analysis from the high variability observed in abnormal gait. Moreover, from a technical point of view the study may be regarded as a real-world alternative to simulation studies. By using healthy individuals it is possible to collect data which are in better agreement with the underlying model assumptions. The penultimate chapter of the thesis presents a qualitative study with clinical experts to investigate the utility of gait analysis for the management of cerebral palsy. We explore potential pathways by which the statistical analyses in the thesis might influence patient outcomes. The thesis has six chapters. After describing motivation and introduction in Chapter 1, mathematical representations of functional data are presented in Chapter 2. Chapter 3 considers orthogonal designs in the context of functional data analysis. New functional F tests for complex designs are derived in Chapter 4 and applied in two gait studies. Chapter 5 is devoted to a qualitative study. The thesis concludes with a discussion which details the extent to which the research question has been addressed, the limitations of the work and the degree to which it has been answered.
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Gonsalez, Camila Gianini [UNESP]. "Metodologias para reconhecimento de padrões em sistemas SHM utilizando a técnica da Impedância Eletromecânica (E/M)." Universidade Estadual Paulista (UNESP), 2012. http://hdl.handle.net/11449/94506.

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Made available in DSpace on 2014-06-11T19:27:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-02-24Bitstream added on 2014-06-13T19:14:27Z : No. of bitstreams: 1 gonsalez_cg_me_ilha.pdf: 4679748 bytes, checksum: 5f6a627734b2110f92059053c2470814 (MD5)<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Pesquisadores de diversas partes do mundo se empenham em desenvolver técnicas capazes de monitorar a integridade de máquinas, veículos e estruturas, principalmente as que a ruptura ou destruição possa provocar acidentes e catástrofes. Neste contexto, várias técnicas não destrutivas podem ser utilizadas para monitorar estes sistemas permitindo a realização de reparos e, evitando maiores prejuízos econômicos e danos sociais. A técnica da Impedância Eletromecânica está entre as técnicas baseadas na utilização de materiais piezelétricos e, particularmente, utiliza-se de uma curva sensível a pequenas variações na estrutura, característica que faz a técnica ser eficiente na detecção de danos incipientes. No entanto, sob variações das condições ambiente e de teste, a sensibilidade da técnica pode produzir falsos diagnósticos. Desta forma, o desafio atual é aplicar a técnica da Impedância Eletromecânica em sistemas de monitoramento considerando condições mais próximas às condições de operação reais dos sistemas a serem monitorados. Este trabalho apresenta duas metodologias para sistemas SHM, a primeira consiste em utilizar a técnica de agrupamento Fuzzy c-means para entender e considerar o efeito da temperatura nos sinais da Impedância Eletromecânica. A segunda metodologia utiliza análise de variância (ANOVA) para propor uma metodologia de detecção mais robusta, e assim, incorporar variações aleatórias nos sistemas de medição e aquisição sem comprometer o diagnóstico SHM<br>Researchers around the world are engaged to develop techniques for structural health monitoring of machinery, vehicles and structures, especially systems where damage or destruction could induce accidents and disasters. In this context, several non-destructive techniques can be used to monitor these systems allowing repairs and avoiding major economic losses or social losses. The electromechanical impedance technique is among the techniques based on piezoelectric materials use and it is sensible to small variations in the structure which makes it efficient in detecting incipient damages. However, variations in the ambient or test conditions can cause false diagnoses. Therefore, the current challenge is to apply the electromechanical impedance technique considering monitoring conditions closer to real operating conditions of the systems to be monitored. This work presents two methodologies for SHM systems. The first one uses Fuzzy c-means clustering to distinguish the temperature effect on impedance signal. The second method uses analysis of variance (ANOVA) to propose a more robust detection methodology and thus incorporate random variations in measurement systems and acquisition without loss of SHM diagnostic
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Gonsalez, Camila Gianini. "Metodologias para reconhecimento de padrões em sistemas SHM utilizando a técnica da Impedância Eletromecânica (E/M) /." Ilha Solteira : [s.n.], 2012. http://hdl.handle.net/11449/94506.

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Orientador: Vicente Lopes Junior<br>Banca: Samuel Silva<br>Banca: Michael John Brennan<br>Banca: Carlos Alberto Cimini Junior<br>Resumo: Pesquisadores de diversas partes do mundo se empenham em desenvolver técnicas capazes de monitorar a integridade de máquinas, veículos e estruturas, principalmente as que a ruptura ou destruição possa provocar acidentes e catástrofes. Neste contexto, várias técnicas não destrutivas podem ser utilizadas para monitorar estes sistemas permitindo a realização de reparos e, evitando maiores prejuízos econômicos e danos sociais. A técnica da Impedância Eletromecânica está entre as técnicas baseadas na utilização de materiais piezelétricos e, particularmente, utiliza-se de uma curva sensível a pequenas variações na estrutura, característica que faz a técnica ser eficiente na detecção de danos incipientes. No entanto, sob variações das condições ambiente e de teste, a sensibilidade da técnica pode produzir falsos diagnósticos. Desta forma, o desafio atual é aplicar a técnica da Impedância Eletromecânica em sistemas de monitoramento considerando condições mais próximas às condições de operação reais dos sistemas a serem monitorados. Este trabalho apresenta duas metodologias para sistemas SHM, a primeira consiste em utilizar a técnica de agrupamento Fuzzy c-means para entender e considerar o efeito da temperatura nos sinais da Impedância Eletromecânica. A segunda metodologia utiliza análise de variância (ANOVA) para propor uma metodologia de detecção mais robusta, e assim, incorporar variações aleatórias nos sistemas de medição e aquisição sem comprometer o diagnóstico SHM<br>Abstract: Researchers around the world are engaged to develop techniques for structural health monitoring of machinery, vehicles and structures, especially systems where damage or destruction could induce accidents and disasters. In this context, several non-destructive techniques can be used to monitor these systems allowing repairs and avoiding major economic losses or social losses. The electromechanical impedance technique is among the techniques based on piezoelectric materials use and it is sensible to small variations in the structure which makes it efficient in detecting incipient damages. However, variations in the ambient or test conditions can cause false diagnoses. Therefore, the current challenge is to apply the electromechanical impedance technique considering monitoring conditions closer to real operating conditions of the systems to be monitored. This work presents two methodologies for SHM systems. The first one uses Fuzzy c-means clustering to distinguish the temperature effect on impedance signal. The second method uses analysis of variance (ANOVA) to propose a more robust detection methodology and thus incorporate random variations in measurement systems and acquisition without loss of SHM diagnostic<br>Mestre
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Bourahima, Fazati. "Évolutions microstructurales et défauts générés par laser cladding lors du dépôt de Ni sur des moules de verrerie en alliage de Cu-Ni-Al et en fonte GL." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS017/document.

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Dans l’industrie de la verrerie, le laser cladding est une technique de rechargement très innovante permettant de déposer une couche très fine d’un alliage à base de nickel pour protéger les moules (utiles à la fabrication de bouteilles en verre) de la corrosion, de l’abrasion ainsi que de la fatigue thermique. La méthode utilisée ici (fusion de poudre projetée par laser) est très courante en fabrication additive. Cette étude s’intéresse à l’impact du rechargement sur le dépôt et les substrats en Cu-Ni-Al et en fonte GL. L’influence sur la microstructure ainsi que sur le comportement mécanique a pu être étudiée (MEB et microanalyses, dureté, contraintes résiduelles ...). Nous nous sommes focalisés sur l’apparition de défauts tels que le manque d’accroche lors du laser cladding sur du Cu-Ni-Al et sur la possible fissuration lors du rechargement sur de la fonte GL. Le but est bien sûr de s’affranchir de ces défauts. Il a notamment été mis en évidence que le manque d’accroche (cas du Cu-Ni-Al) est lié à la distribution gaussienne de la poudre qui atténue la puissance incidente du laser au niveau du pic de poudre. Le manque d'accroche n'est pas détecté sur le substrat en fonte en raison de sa forte absorptivité et de sa faible conductivité thermique. Néanmoins, des fissures peuvent être observées en raison de contraintes résiduelles thermiques et de la présence d'une zone affectée thermiquement. De plus, l’analyse statistique ANOVA a permis une optimisation des paramètres de rechargement de telle sorte à obtenir une accroche dans toute la section tout en respectant les préconisations géométriques données par les Établissements CHPOLANSKY pour le cordon<br>In glass industry, the laser cladding is an innovative surfacing technique allowing to deposit a very thin layer of nickel to protect glass mold (useful for glass bottle production) against corrosion, abrasion and thermal fatigue. This method (powder fusion by projection) is well known in additive manufacturing. The aim of this work is to observe the impact of the laser cladding on the coating behavior but also on the Cu-Ni-Al and flake-graphite cast iron substrates. The microstructure and the mechanical properties were studied (SEM and microanalysis, microhardness, residual stress …) around the interface cladding/substrate. The work was also focused on the defects like lack of bonding but also on cracking behavior during surfacing on cast iron. The purpose was to prevent from those defects. This work showed that the lack of bonding to the Cu-Ni-Al substrate is due to the gaussian distribution of the powder which attenuates the input laser power at its peak. The lack of bonding is not detected on cast iron substrate thanks to its high absorptivity and low thermal conductivity. Nevertheless, cracks can be observed due to thermal residual stresses and the presence of a thermal affected zone. The ANOVA technique allowed us to optimize the processing parameters in order to obtain a perfect bonding and the geometry wanted by CHPOLANSKY Establishments
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Books on the topic "ANOVA (Analysis of variance)"

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Introducing ANOVA and ANCOVA: A GLM approach. SAGE, 2001.

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Rutherford, Andrew, and Andrew Rutherford. ANOVA and ANCOVA: A GLM approach. 2nd ed. Wiley, 2011.

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ANOVA: An analysis of variance primer. F.E. Peacock Publishers, 1994.

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Rutherford, Andrew. ANOVA and ANCOVA: A GLM approach. 2nd ed. Wiley, 2011.

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ANOVA: Repeated measures. Sage Publications, 1992.

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Jackson, Sally Ann. Random factors in ANOVA. Sage Publications, 1994.

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S, Fidell Linda, ed. Experimental designs using ANOVA. Thomson/Brooks/Cole, 2007.

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M, Borror Connie, and Montgomery Douglas C, eds. Design and analysis of gauge R&R studies: Making decisions with confidence intervals in random and mixed ANOVA models. Society for Industrial Applied Mathematics, 2005.

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Scott Jones, Julie. Learn to Use Factorial Analysis of Variance (ANOVA) in SPSS With Data From the English Health Survey (Teaching Dataset) (2002). SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526485786.

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Scott Jones, Julie. Learn to Use Factorial Analysis of Variance (ANOVA) in R With Data From the English Health Survey (Teaching Dataset) (2002). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498694.

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Book chapters on the topic "ANOVA (Analysis of variance)"

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Analysis of variance (ANOVA)." In Practical R for biologists: an introduction. CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0155.

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Abstract Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specific function aov, but there is a special function oneway.test when there is only a single explanatory variable. For a one-way ANOVA the non-parametric equivalent (if variance assumptions are not met) is the kruskal.test.
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Analysis of variance (ANOVA)." In Practical R for biologists: an introduction. CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0013a.

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Abstract Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specific function aov, but there is a special function oneway.test when there is only a single explanatory variable. For a one-way ANOVA the non-parametric equivalent (if variance assumptions are not met) is the kruskal.test.
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Vasishth, Shravan, and Michael Broe. "Analysis of Variance (ANOVA)." In The Foundations of Statistics: A Simulation-based Approach. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16313-5_5.

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Acton, Ciaran, Robert Miller, John Maltby, and Deirdre Fullerton. "Analysis of Variance (ANOVA)." In SPSS for Social Scientists. Macmillan Education UK, 2009. http://dx.doi.org/10.1007/978-1-137-01390-3_9.

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Shahbaba, Babak. "Analysis of Variance (ANOVA)." In Biostatistics with R. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1302-8_9.

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Miller, Robert L., Ciaran Acton, Deirdre A. Fullerton, John Maltby, and Jo Campling. "Analysis of Variance (ANOVA)." In SPSS for Social Scientists. Macmillan Education UK, 2002. http://dx.doi.org/10.1007/978-0-230-62968-4_8.

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Kaufman, Jörg. "Analysis of Variance (ANOVA)." In Methods and Applications of Statistics in Clinical Trials. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118596333.ch2.

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Lawal, Bayo. "Analysis of Variance (ANOVA)." In Applied Statistical Methods in Agriculture, Health and Life Sciences. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05555-8_6.

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Goodman, Melody S. "Analysis of variance (ANOVA)." In Biostatistics for Clinical and Public Health Research. Routledge, 2017. http://dx.doi.org/10.4324/9781315155661-16.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Analysis of Variance (Anova)." In Regression Analysis in Medical Research. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61394-5_7.

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Conference papers on the topic "ANOVA (Analysis of variance)"

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MUDHOLKAR, GOVIND S., DEO KUMAR SRIVASTAVA, CAROL E. MARCHETTI, and ANIL G. MUDHOLKAR. "TRIMMED ANALYSIS OF VARIANCE: A ROBUST MODIFICATION OF ANOVA." In Proceedings of Statistics 2011 Canada/IMST 2011-FIM XX. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814417983_0012.

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Bokoro, P., and K. Malandala. "Condition assessment of SF6 circuit breakers using analysis of variance (ANOVA)." In 2017 IEEE AFRICON. IEEE, 2017. http://dx.doi.org/10.1109/afrcon.2017.8095682.

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Brusca, S., and R. Lanzafame. "Heat Recovery Steam Generator Optimization Using Analysis of Variance." In ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50008.

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The present work deals with the analysis and optimization of a heat recovery steam generator (HRSG) using the ANalysis Of VAriance (ANOVA). In order to obtain an optimum thermodynamic configuration of a three pressure levels HRSG, a mathematical model of the generator has been implemented using a generic thermodynamic code. Model management and HRSG control logic have been implemented using code’s macros and Microsoft Excel VBA programming languages. The model has been finely tuned and tested using real HRSG running data in the current plant configuration. Using the model, evaporators’ pressure levels have been modified and thermodynamic data elaborated using ANOVA technique. Results’ analysis shows that reducing low and medium pressure level in the HRSG, global steam production rises up. At the same time, exhaust gas temperature decreases showing a certain heat adsorption increase. On the basis of the results it is possible to state that the proposed HRSG configuration involves increases in plant steam and power production as well as in global efficiency with HRSG minimal modification.
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Nasirabadi, Parizad Shojaee, Helene Conseil-Gudla, Sankhya Mohanty, Masoud Jabbari, Rajan Ambat, and Jesper H. Hattel. "Semi-empirical prediction of moisture build-up in an electronic enclosure using analysis of variance (ANOVA)." In 2016 IEEE 18th Electronics Packaging Technology Conference (EPTC). IEEE, 2016. http://dx.doi.org/10.1109/eptc.2016.7861588.

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Zohourkari, Iman, Saeed Assarzadeh, and Mehdi Zohoor. "Modeling and Analysis of Hot Extrusion Metal Forming Process Using Artificial Neural Network and ANOVA." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-25333.

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In this paper, a feed-forward back-propagation artificial neural network (BP-ANN) and analysis of variance (ANOVA) are applied to a hot metal extrusion process, establishing a black box model as well as analyzing the effects of relevant process parameters on required forging load, under different operating conditions. Some finite element simulation data on extruding ck-45 steel, adopted from a published research paper, were used to train the neural model employing Levenberg-Marquardt learning algorithm. Die angle (15°–75°), friction coefficient between billet-die material pair (0.4–0.8), punch velocity (168–203 mm/s), and billet temperature (1000°C–1260°C) were selected as the inputs, while the extrusion load (tone) was considered as the network’s output. Based on the results during modeling attempts, a 4-10-10-1 size neural network has been decided on as the appropriate architecture of the process model. Testing predictive accuracy of the developed model was also done using a new data set (8 data samples), which has not been used in the training phase. The comparative errors with respect to the desired FEM simulations are all in acceptable ranges (less than 12%) thereby the network’s generalization capabilities were confirmed. Having established the appropriate neural model, analysis of variance (ANOVA) technique was then applied to the original training data base to find and recognize the level of importance of each parameters and their possible dual interactions on the extrusion loading force within 95% of confidence interval (α = 0.05). Based on the obtained inferences, the best optimal combination of parametric settings which leads to the minimum required extruding load was then revealed and recommended. The optimally minimized extrusion force was then predicted by the trained network model. Neural network tool box (NNET) of the Matlab software and design of experiments module of Minitab software were employed as platforms to develop neural simulations and ANOVA technique, respectively. The overall results indicate the feasibility and effectiveness of the proposed approach in a real manufacturing environment and eliminate the need to carry out expensive as well as time consuming trial and error experimentations to reach to the optimum operating conditions.
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Karakhan, Ali, John Gambatese, Kasim Alomari, and Ding Liu. "Consideration of Worker Safety in the Design Process: A Statistical-Based Approach Using Analysis of Variance (ANOVA)." In Construction Research Congress 2018. American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481288.037.

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Danila, Adrian, Delia Ungureanu, Sorin Aurel Moraru, and Nicoleta Voicescu. "An implementation of the variance analysis (ANOVA) for the power factor optimization at distribution level in smart grid." In 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). IEEE, 2017. http://dx.doi.org/10.1109/optim.2017.7974946.

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Morsi, Iman. "Discrimination of some atmospheric gases using an integrated sensor array, surface response modeling algorithms, and analysis of variance (ANOVA)." In SAS2008 - IEEE Sensors Applications Symposium. IEEE, 2008. http://dx.doi.org/10.1109/sas13374.2008.4472959.

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Xie, Shengkun, Sridhar Krishnan, and Anna T. Lawniczak. "Analysis of communication network surveillance using functional ANOVA model with unequal variances." In 2012 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA). IEEE, 2012. http://dx.doi.org/10.1109/cisda.2012.6291541.

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Esmaeeli, Roja, Haniph Aliniagerdroudbari, Seyed Reza Hashemi, Hammad Al-Shammari, Muapper Alhadri, and Siamak Farhad. "Univariate and Multivariate Gauge Repeatability and Reproducibility Analysis on the High Frequency Dynamic Mechanical Analysis (DMA) Measurement System." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10986.

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Abstract The quality of the collected data from a measurement system affects eventual decision making process. Therefore, the reliability of any measurement system is an important factor to be studied. Gauge repeatability and reproducibility (Gauge R&amp;R) is the standard method to evaluate the measurement system and assess the adequacy of variation in the measurement data. Gauge R&amp;R is a statistical tool which evaluates two main characteristics of the measurement system: repeatability and reproducibility. The Dynamic Mechanical Analysis (DMA) is a common measurement system for studying the dynamic mechanical properties of viscoelastic materials such as polymers. The newly developed High Frequency Dynamic Mechanical Analysis (HFDMA) is able to directly run the simple shear test at high frequencies without changing the specimen temperature. The complex shear modulus and damping factor of the viscoelastic materials are reported by the HFDMA system. In this study the uni-variable Gauge R&amp;R study based on Analysis of Variance (ANOVA) is done on each measured characteristic of the HFDMA measurement system. The source of variations for each characteristic is distinguished. Then the multivariate Gauge R&amp;R based on the Multivariate Analysis of Variance (MANOVA) is done and the percentage of multivariate Gauge R&amp;R for the measurement with the multiple variables is reported. The results indicate that the HFDMA measurements are both repeatable and reproducible. Thus, the new HFDMA can be used as a measurement system to measure the mechanical properties of viscoelastic materials at high frequencies.
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Reports on the topic "ANOVA (Analysis of variance)"

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Dzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123.

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Dzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123-2020.

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Verrill, Steve, and David E. Kretschmann. Reminder about potentially serious problems with a type of blocked ANOVA analysis. U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2017. http://dx.doi.org/10.2737/fpl-rp-683.

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Kim, Joseph J., Samuel Dominguez, and Luis Diaz. Freight Demand Model for Southern California Freeways with Owner–Operator Truck Drivers. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1931.

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This study evaluates the demand for truck-only toll lanes on Southern California freeways with owner–operator truck drivers. The study implemented the stated preference survey method to estimate the value placed by drivers on time, reliability, and safety measures using various scenarios geared towards assessing those values. The project team met face-to-face with owner- operator truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 31 survey responses is obtained and used for statistical data analysis using analysis of variable (ANOVA) and two sample t-tests. The analysis results showed that 75.27% of the owner– operator truck drivers responded are willing to pay toll fees when they choose routes. The tolerated average toll fees are $13.77/ hr and $12.82/hr for weekdays and weekends, respectively. The analysis results also showed that owner–operator truck drivers will take truck-only toll lanes when they take the routes used in four comparisons out of six comparisons according to the three measures such as values of time, reliability, and safety, despite sharing a common origin and destination. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of time is $0.31/mile or $18.35/hr. The toll fees associated with reliability and safety measures are $0.30/mile or $8.94/hr and $0.22/mile or $11.01/hr, respectively. These results are meaningful for legislators and transportation agencies as the behaviors and route choice characteristics of owner–operator truck drivers help them better understand the utility and demand for truck-only toll lanes.
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Goodall, Colin. The Analysis of Averages and the Analysis of Variance. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198467.

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Taylor, K. E., and C. Covey. Analysis of Variance Including the Diurnal Cycle. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1460516.

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Poyer, D. A. Residential energy consumption: An analysis-of-variance study. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5757461.

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Poyer, D. A. Residential energy consumption: An analysis-of-variance study. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10132294.

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Anderson, T. W., and Michael D. Perlman. Consistency of Invariants for the Multivariate Analysis of Variance. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada591103.

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Ramsey, M. H., M. Thompson, and M. Hale. Objective evaluation of precision requirements for geochemical analysis using analysis of variance. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/193282.

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