Relevant bibliographies by topics / ANOVA test

Contents

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

Academic literature on the topic 'ANOVA test'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "ANOVA test"

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Davison, Mark L., and Anu R. Sharma. "ANOVA and ANCOVA of pre- and post-test, ordinal data." Psychometrika 59, no. 4 (December 1994): 593–600. http://dx.doi.org/10.1007/bf02294394.

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Tarlow, Kevin R. "Teaching principles of inference with ANOVA." Teaching Statistics 38, no. 1 (July 24, 2015): 16–21. http://dx.doi.org/10.1111/test.12085.

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Cuevas, Antonio, Manuel Febrero, and Ricardo Fraiman. "An anova test for functional data." Computational Statistics & Data Analysis 47, no. 1 (August 2004): 111–22. http://dx.doi.org/10.1016/j.csda.2003.10.021.

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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 (September 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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Chen, Tansheng, and Lukun Zheng. "One-Way High-Dimensional ANOVA." Journal of Mathematics 2023 (March 28, 2023): 1–11. http://dx.doi.org/10.1155/2023/9350523.

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ANOVA is one of the most important tools in comparing the treatment means among different groups in repeated measurements. The classical F test is routinely used to test if the treatment means are the same across different groups. However, it is inefficient when the number of groups or dimension gets large. We propose a smoothing truncation test to deal with this problem. It is shown theoretically and empirically that the proposed test works regardless of the dimension. The limiting null and alternative distributions of our test statistic are established for fixed and diverging number of treatments. Simulations demonstrate superior performance of the proposed test over the F test in different settings.
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Camilli, Gregory, and Lorrie A. Shepard. "The Inadequacy of ANOVA for Detecting Test Bias." Journal of Educational Statistics 12, no. 1 (March 1987): 87–99. http://dx.doi.org/10.3102/10769986012001087.

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The inadequacy of ANOVA for detecting bias in test items should already be well understood, yet it persists as a popular method. Here, previous arguments are extended to explain why ANOVA may obscure test bias when it exists, as well as create false impressions of bias. In fact, it is demonstrated in this paper that ANOVA will fail to detect even absurdly large amounts of bias. More specifically, it is shown that bias contributes relatively more to the group main effect than to the group-by-item interaction.
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Hecke, T. Van. "Power study of anova versus Kruskal-Wallis test." Journal of Statistics and Management Systems 15, no. 2-3 (May 2012): 241–47. http://dx.doi.org/10.1080/09720510.2012.10701623.

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Wetzels, Ruud, Raoul P. P. P. Grasman, and Eric-Jan Wagenmakers. "A Default Bayesian Hypothesis Test for ANOVA Designs." American Statistician 66, no. 2 (May 2012): 104–11. http://dx.doi.org/10.1080/00031305.2012.695956.

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Camilli, Gregory, and Lorrie A. Shepard. "The Inadequacy of ANOVA for Detecting Test Bias." Journal of Educational Statistics 12, no. 1 (1987): 87. http://dx.doi.org/10.2307/1164630.

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Jung, Byoung Cheol, Myoungshic Jhun, and Seuck Heun Song. "A new random permutation test in ANOVA models." Statistical Papers 48, no. 1 (January 2007): 47–62. http://dx.doi.org/10.1007/s00362-006-0315-x.

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Dissertations / Theses on the topic "ANOVA test"

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Liu, Hangcheng. "Comparing Welch's ANOVA, a Kruskal-Wallis test and traditional ANOVA in case of Heterogeneity of Variance." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3985.

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Analysis of variance (ANOVA) is a robust test against the normality assumption, but it may be inappropriate when the assumption of homogeneity of variance has been violated. Welch ANOVA and the Kruskal-Wallis test (a non-parametric method) can be applicable for this case. In this study we compare the three methods in empirical type I error rate and power, when heterogeneity of variance occurs and find out which method is the most suitable with which cases including balanced/unbalanced, small/large sample size, and/or with normal/non-normal distributions.
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Ning, Wei. "A new approach to test for interactions in two-way ANOVA models." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.

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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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Senteney, Michael H. "A Monte Carlo Study to Determine Sample Size for Multiple Comparison Procedures in ANOVA." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou160433478343909.

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Opoku-Nsiah, Richard. "A computationally efficient bootstrap-equivalent test for ANOVA in skewed populations with a large number of factor levels." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38155.

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Doctor of Philosophy
Department of Statistics
Haiyan Wang
Advances in technology easily collect a large amount of data in scientific research such as agricultural screening and micro-array experiments. We are particularly interested in data from one-way and crossed two-way designs that have a large number of treatment combinations but small replications with heteroscedastic variances. In this framework, several test statistics have been proposed in the literature. Even though the form of these proposed test statistics may be different, they all use limiting normal or chi-square distribution to conduct their tests. Such approximation approaches the true distribution very slowly when the sample size ni is small while the number of levels of treatments a gets large. A strategy to obtain better accuracy in the classical large sample size setting is to use the bootstrap procedure with studentized statistic. Unfortunately, the available bootstrap method fails when the number of treatment level combinations is large while the number of replications is small. The Fisher and Hall (1990) asymptotic pivotal statistic under large sample size setting is no longer pivotal under small sample size setting with large number of treatment levels. In the first part of this dissertation, we start with describing suitable bootstrap statistics and procedures for hypothesis tests in one- and two-way ANOVA with a large number of levels and small sample sizes. We prove that the theoretical type I error-rate of Akritas and Papadatos (2004) and Wang and Akritas (2006) test statistics and their corresponding bootstrap versions have accuracy of order O(1/√a). We then modify their statistics to obtain asymptotically pivotal statistics in our current framework. We prove that the theoretical type I error-rate of the bootstrap version of the pivotal statistics is accurate up to order O(1/√a). In the second part of the dissertation, we propose a new test statistic in one-way ANOVA which is asymptotically pivotal in the current setting. We improve the accuracy of approximation of the distribution of the test statistic by deriving asymptotic expansion of the statistic under the current framework and define a new test rejection region through Cornish-Fisher expansion of quantiles. The type I error-rate of the new test has a faster convergence rate and is accurate up to order O(1/a). Simulation studies show that our tests performs better in terms of type I error-rate but comparable power with that of Akritas and Papadatos (2004) in the large a small ni setting. The connection between our asymptotic expansions and bootstrap distribution in the large a small ni setting is discussed. Our proposed test based on asymptotic expansion and Cornish-Fisher expansion of quantiles have both the advantage of higher accuracy and computational efficiency due to no resampling is needed.
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Larsson, Stefan. "Mixing Processes for Ground Improvement by Deep Mixing." Doctoral thesis, KTH, Civil and Architectural Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3667.

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The thesis is dealing with mixing processes havingapplication to ground improvement by deep mixing. The mainobjectives of the thesis is to make a contribution to knowledgeof the basic mechanisms in mixing binding agents into soil andimprove the knowledge concerning factors that influence theuniformity of stabilised soil.

A great part of the work consists of a literature surveywith particular emphasis on literature on the processindustries. This review forms a basis for a profounddescription and discussion of the mixing process and factorsaffecting the process in connection with deep mixingmethods.

The thesis presents a method for a simple field test for thestudy of influential factors in the mixing process. A number offactors in the installation process of lime-cement columns havebeen studied in two field tests using statistical multifactorexperiment design. The effects of retrieval rate, number ofmixing blades, rotation speed, air pressure in the storagetank, and diameter of the binder outlet on the stabilisationeffect and the coefficient of variation determined byhand-operated penetrometer tests for excavated lime-cementcolumns, were studied.

The literature review, the description of the mixingprocess, and the results from the field tests provide a morebalanced picture of the mixing process and are expected to beuseful in connection to ground improvement projects and thedevelopment of mixing equipments.

The concept of sufficient mixture quality, i.e. theinteraction between the mixing process and the mechanicalsystem, is discussed in the last section. By means ofgeostatistical methods, the analysis considers thevolume-variability relationship with reference to strengthproperties. According to the analysis, the design values forstrength properties depends on the mechanical system, the scaleof scrutiny, the spatial correlation structure, and the conceptof safety, i.e. the concept of sufficient mixture quality isproblem specific.

Key words:Deep Mixing, Lime cement columns, Mixingmechanisms, Mixture quality, Field test, ANOVA, Variancereduction.

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Sahlström, Linda. "Att klättra, springa, krypa och kasta : En effektutvärdering inom området rörelseförståelse bland barn: har interventionen ”Rörelsesatsning i skolan” gett någon effekt?" Thesis, Mälardalens högskola, Akademin för hälsa, vård och välfärd, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-45321.

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Övervikt är en av de ledande folkhälsoutmaningarna i världen där cirka en tredjedel av världens befolkning är övervikta eller har fetma vilket medför negativa hälsoeffekter och sjukdomar. Övervikt och fetma drabbar även barn och unga där en kraftig ökning har skett under de senaste 40 åren. Fysisk aktivitet utgör en viktig komponent för att minska fetmaepidemin, dock förekommer även en ökad andel fysiskt inaktiva barn och unga i Sverige. Skolan ses som en bra arena för folkhälsoarbete men det förekommer dock spridd forskning gällande effekterna av interventioner som syftar att öka den fysiska aktiviteten. Syftet med studien var att genomföra en effektutvärdering av projektet ”Rörelsesatsning i skolan” genom en före- och eftermätning av primärutfallet som var skolelevernas fysiska aktivitet samt sekundärutfall som idrottsengagemanget, välmåendet samt klassrumsro. En kvantitativ metod genomfördes med en Single Case Experimental Design där totalt 148 elever deltog vid baslinjemätning och 125 elever vid uppföljningen, eleverna gick i årskurserna 3–6 på en skola i Västerås. Resultatet analyserades genom chi2-test samt ANOVA. Resultatet visar att interventionen gett effekt på utövandet av bollsporter/klättra/springa på rasterna samt att utöva bollsport med ledare på fritiden. Även liten effekt på ökad andel cyklande till skolan och andel som orienterade/cyklade på fritiden med ledare kunde ses.
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Giosa, Francesca. "I test d'ipotesi e la loro declinazione in ambito medico." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15992/.

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Una domanda di rilevante interesse in ambito medico e in particolare nell'ambito della ricerca medica è: avendo a disposizione un certo numero di farmaci quale di questi è più efficace? E' chiaro quanto sia importante conoscere l'efficacia di un farmaco prima di somministrarlo e la statistica medica con lo strumento statistico comunemente chiamato Test d'ipotesi si occupa di rispondere a queste domande e di limitare il più possibile gli errori di valutazione che si possono commettere. Lo scopo della mia tesi è spiegare l'utilizzo dei test d'ipotesi e in particolare di analizzare il confronto tra due e più popolazioni (gaussiane e non), ponendo attenzione alle varie possibilità di impostazione del problema a seconda delle ipotesi che vengono di volta in volta soddisfatte. Infatti a seconda del disegno sperimentale del problema che si vuole trattare, molto spesso si fa un uso diverso della stessa tecnica e la maggior parte degli errori nella letteratura biomedica riguarda proprio errori basilari nel disegno sperimentale, come per esempio una errata procedura di randomizzazione o l'omissione del gruppo di controllo, oppure l'utilizzo non appropriato del test t per confronti multipli. Pertanto è bene fare attenzione alle diverse possibilità e capire quali sono le più adatte nei vari casi. Per questo motivo il percorso seguito nella mia tesi è basato sulla importanza della adeguatezza del modello utilizzato e della verifica delle ipotesi che permettono di utilizzare un metodo di risoluzione piuttosto che un altro. Inoltre ho presentato un esempio tratto da una situazione reale, con dati forniti dall'istituto per lo scompenso cardiaco di Bologna. Questo esempio è volto a mettere in evidenza l'importanza della adeguatezza del modello e i problemi pratici in cui si può incorrere.
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Mu, Zhiqiang. "Comparing the Statistical Tests for Homogeneity of Variances." Digital Commons @ East Tennessee State University, 2006. https://dc.etsu.edu/etd/2212.

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Testing the homogeneity of variances is an important problem in many applications since statistical methods of frequent use, such as ANOVA, assume equal variances for two or more groups of data. However, testing the equality of variances is a difficult problem due to the fact that many of the tests are not robust against non-normality. It is known that the kurtosis of the distribution of the source data can affect the performance of the tests for variance. We review the classical tests and their latest, more robust modifications, some other tests that have recently appeared in the literature, and use bootstrap and permutation techniques to test for equal variances. We compare the performance of these tests under different types of distributions, sample sizes and true ratios of variances of the populations. Monte-Carlo methods are used in this study to calculate empirical powers and type I errors under different settings.
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Laird, Daniel T. "Analysis of Covariance with Linear Regression Error Model on Antenna Control Unit Tracking." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596393.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV
Over the past several years DoD imposed constraints on test deliverables, requiring objective measures of test results, i.e., statistically defensible test and evaluation (SDT&E) methods and results. These constraints force the tester to employ statistical hypotheses, analyses and perhaps modeling to assess test results objectively, i.e., based on statistical metrics, probability of confidence and logical inference to supplement rather than rely solely on expertise, which is too subjective. Experts often disagree on interpretation. Numbers, although interpretable, are less variable than opinion. Logic, statistical inference and belief are the bases of testable, repeatable and refutable hypothesis and analyses. In this paper we apply linear regression modeling and analysis of variance (ANOVA) to time-space position information (TSPI) to determine if a telemetry (TM) antenna control unit (ACU) under test (AUT) tracks statistically, thus as efficiently, in C-band while receiving both C- and S-band signals. Together, regression and ANOVA compose a method known as analysis of covariance (ANCOVA). In this, the second of three papers, we use data from a range test, but make no reference to the systems under test, nor to causes of error. The intent is to present examples of tools and techniques useful for SDT&E methodologies in testing.
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Books on the topic "ANOVA test"

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Solari, Aldo, Luigi Salmaso, Fortunato Pesarin, and Dario Basso. Permutation Tests for Stochastic Ordering and ANOVA. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-85956-9.

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Goos, Peter. Statistics with JMP: Hypothesis tests, ANOVA, and regression. Chichester, West Sussex: John Wiley & Sons, Inc., 2016.

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Dario, Basso, ed. Permutation tests for stochastic ordering and ANOVA: Theory and applications with R. London ; New York: Springer, 2009.

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Dario, Basso, ed. Permutation tests for stochastic ordering and ANOVA: Theory and applications with R. London ; New York: Springer, 2009.

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Chen, Danxia, Joanne Hix, and Jon Reid. Using Paired Samples t-Tests, Repeated Measures ANOVA, and MLM Techniques to Measure Business Progress. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529670622.

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C. A. . Pradip Kumar Ghosh and Er Soumadeep Ghosh. Anova Smplified: Test Your Research Hypothesis. Independently Published, 2017.

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Białowąs, Sylwester, ed. Experimental design and biometric research. Toward innovations. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, 2021. http://dx.doi.org/10.18559/978-83-8211-079-1.

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This e-book aims to present the most critical aspects of knowledge about using experiments in economics and practical tools for using them. The topic is extended to the more advanced and increasing in popularity area of biometric research. The book is divided into three parts mirroring experimentation. The first part provides theoretical background and tips about organising own research. The chapter is concluded with a guide focused on writing a research report in APA style. This part includes an example of the actual research report. The next part has two chapters, and both are guided tours allowing to plan and conduct eye-tracking research and electrodermal activity research (EDA). The chapters contain details about preparing experiments, conducting them, using the dedicated software to analyse collected data and interpreting the default charts. The last part is devoted to the data analysis and is universal, goes beyond the biometric experiments. There are three chapters in this part covering the standard procedures used in the analysis of experiments. The first part includes tests for one hypothesis: parametric t-test and One-Way ANOVA and non-parametric siblings: Mann Whitney U test and Kruskal-Wallis test. The next part describes tests allowing testing more hypotheses: ANOVA without repetition and ANOVA with repetitions. Furthermore, the last chapter deals with dependent samples, which are a popular approach in experiments. This part describes the dependent sample t-test and Wilcoxon test. The effect sizes calculations are included; each test is shown with screenshots from SPSS and some additional screenshots from Excel. This approach allows following the procedure step by step. The examples help easily understand procedures and interpretations; they were chosen from areas of sustainability and innovations to match the general idea of the e-books series prepared within the CENETSIE program. The book contains texts that can be useful in the teaching process. It can be helpful in graduate programs in economics and business schools. Programs of doctoral schools cab benefit from this book as well.
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Sharif, Shamshuritawati, Sharipah Soaad Syed Yahaya, and Azizan Saaban. Scientific investigation on univariate quantitative methods. UUM Press, 2016. http://dx.doi.org/10.32890/9789670876757.

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The aim of this book is to deliver the reader with a book where they can discover a brief research idea in statistics and mathematics including a wide range of topics such as t-test, ANOVA, L-moment, centrality measure, Quintic Bézier Triangular Patches, and abelianess in Group Theory.The book is advisable for the readers to have some basic foundation on statistical inference and mathematical formulation prior to reading the chapters in this book.It is also suitable for researchers who want to get up to speed quickly on modern statistical and mathematical approach.
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Goos, Peter, and David Meintrup. Statistics with JMP: Hypothesis Tests, ANOVA and Regression. Wiley & Sons, Incorporated, John, 2016.

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Goos, Peter, and David Meintrup. Statistics with JMP: Hypothesis Tests, ANOVA and Regression. Wiley & Sons, Incorporated, John, 2016.

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Book chapters on the topic "ANOVA test"

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Lecoutre, Bruno, and Jacques Poitevineau. "ANOVA Procedures." In The Significance Test Controversy Revisited, 123–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65705-8_11.

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Goto, Yuichi, Hideaki Nagahata, Masanobu Taniguchi, Anna Clara Monti, and Xiaofei Xu. "Optimal Test for One-Way Random Effect Model." In ANOVA with Dependent Errors, 55–65. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4172-8_6.

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Lecoutre, Bruno, and Jacques Poitevineau. "Generalizations and Methodological Considerations for ANOVA." In The Significance Test Controversy Revisited, 105–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44046-9_9.

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Dormann, Carsten. "The Linear Model: t-test and ANOVA." In Environmental Data Analysis, 147–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_11.

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Dormann, Carsten F. "Das Lineare Modell: t-Test und ANOVA." In Parametrische Statistik, 187–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34786-3_11.

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Dormann, Carsten F. "Das Lineare Modell: $t$-Test und ANOVA." In Parametrische Statistik, 195–218. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54684-0_11.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Analysis of variance (ANOVA)." In Practical R for biologists: an introduction, 155–65. Wallingford: 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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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Analysis of variance (ANOVA)." In Practical R for biologists: an introduction, 155–65. Wallingford: 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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Rayat, Charan Singh. "Variance-Ratio Test and Analysis of Variance (ANOVA)." In Statistical Methods in Medical Research, 95–109. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0827-7_12.

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O'Brien, Daniel T. "Identifying Inequities across Groups: ANOVA and t-Test." In Urban Informatics, 237–58. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003292951-16.

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Conference papers on the topic "ANOVA test"

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Kumar, B. Jyothi, H. Naveen, B. Praveen Kumar, Sai Shyam Sharma, and Jaime Villegas. "Logistic regression for polymorphic malware detection using ANOVA F-test." In 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8275880.

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Girisha, R., and S. Murali. "Self shadow elimination algorithm for surveillance videos using ANOVA F test." In the Third Annual ACM Bangalore Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1754288.1754300.

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Egea-Roca, Daniel, Jose A. Lopez-Salcedo, Gonzalo Seco-Granados, and Wim de Wilde. "Generalized ANOVA Test for GNSS Spoofing Detection with a Dual-Polarized Antenna." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287663.

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Moscalu, Mihaela, Gabriel Dimitriu, Cristina gena Dascalu, and Vasile lucian Boiculese. "ANALYSING THE EFFECT OF THE DEVIATION OF COVARIATES USING MULTIPLE REGRESSION." In eLSE 2018. Carol I National Defence University Publishing House, 2018. http://dx.doi.org/10.12753/2066-026x-18-208.

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Comparing data sets to highlight statistical differences is achieved by applying the t test (two samples) or ANOVA technique (2 or more samples). However, the cause-effect link is not demonstrated using these, even if a statistical significance is obtained. Multiple regression identifies and controls the effect of covariates which can contribute to explaining the observed variation of dependent variable, while reducing the variance error (unexplained variation). For an analysis with known covariates of interest that can affect the dependent variable, an effect adjustment by ANCOVA method is needed. Analysis of Covariance (ANCOVA) is an extension of ANOVA that provides a way to statistically control the (linear) effect of covariates for which no descriptive examination is wanted. ANCOVA's advantage consists in calculating the adjusted averages and applying their comparison test based on analysis of control variables (covariates). A tough condition in ANCOVA is the homogeneity of regression slopes. In the case of biological data, this condition is difficult to achieve, with the effects of covariates presenting major differences. Therefore, we have proposed and analysed a multiple linear regression model inspired by the ANCOVA technique in which the condition of homogeneity of the slopes can be not fulfilled. The proposed model includes the averages of the groups defined by the independent variable and the deviations of the covariates as slopes with different values. The model was applied to medical data. Comparison of the multiple linear regression model that includes all covariates was compiled for comparison, highlighting the differences between the two methods.
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Balta, Berna, Fazıl O¨nder So¨nmez, and Abdu¨lkadir Cengiz. "Gage Repeatability and Reproducibility Investigations of a Test Rig Using ANOVA/Xbar-R Method." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62130.

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In an experimental study, good measurement systems are important for approaching successful decisions. The assessment of a measurement system is known as “Gage Repeatability and Reproducibility” (GR&R). “Measurement System Analysis” (MSA) should be performed at the beginning of an experimental study to ensure that the information to be collected are true representation of what is occurring in the experiment. Experimental data collected under the same condition usually show variation, which arises partly from the experimental system, partly from the measurement devices and partly from the operator who makes the measurements. MSA helps to differentiate the contribution of each source to the randomness of the data. In this way, one may see whether there is a need to reduce the measurement variation so that the data reflects basically the experimental variation. Besides, MSA gives quantitative measures for repeatability and reproducibility. Repeatability is the variation in repeated measurements taken by the same operator under the same experimental conditions. Reproducibility is the variation in data obtained by different operators taking the measurement with the same setup under the same conditions. These are measures of the consistency and precision of the data. GR&R is the most common MSA tool that analyzes the viability of an experimental set-up. Resultant GR&R will indicate overall measurement system variation as the sum of repeatability variation and reproducibility variation. Generally, GR&R % gives a measure of the suitability of the measurement system to yield acceptable data for statistical studies such as “Design Of Experiments” (DOE), “One Factor At a Time” (OFAT), “Response Surface Methodology”, etc. [1, 2]. In this paper, “Analysis of Variance” (ANOVA) and the “Average and Range” (Xbar & R) methods are used to assess the capability of a laboratory made measurement device, which is used for the investigations of a belt drive system efficiency. GR&R is applied at the design stages of the construction of the test rig and final application is presented in this study. The results prove that the test rig is capable of making experimental studies using statistical methods such as DOE and Response Surface Methodology.
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Nurrahma and Rahadian Yusuf. "Comparing Different Supervised Machine Learning Accuracy on Analyzing COVID-19 Data using ANOVA Test." In 2020 6th International Conference on Interactive Digital Media (ICIDM). IEEE, 2020. http://dx.doi.org/10.1109/icidm51048.2020.9339676.

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Ismail, Adlil Aizat, Maria Abu Bakar, Abang Annuar Ehsan, Azman Jalar, Erwan Basiron, and Fakhrozi Che Ani. "Intermetallic Compound Thickness of Ball Grid Array Solder Joints Under Thermal Cycling Test Using ANOVA." In 2022 IEEE 39th International Electronics Manufacturing Technology Conference (IEMT). IEEE, 2022. http://dx.doi.org/10.1109/iemt55343.2022.9969477.

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Latuamury, Bokiraiya, Wilma N. Imlabla, John F. Sahusilawane, and Husain Marasabessy. "One-way ANOVA test of five digital filter recursive graphic methods in baseflow separation on Wae Tomu Watershed Ambon City." In THE 7TH INTERNATIONAL CONFERENCE ON BASIC SCIENCES 2021 (ICBS 2021). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0111720.

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Perangin-Angin, Dariswan Janweri, and Fitra A. Bachtiar. "Classification of Stress in Office Work Activities Using Extreme Learning Machine Algorithm and One-way ANOVA F-Test Feature Selection." In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2021. http://dx.doi.org/10.1109/isriti54043.2021.9702802.

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Martinez, Oscar, Abiodun Adeniyi, Paul Nogradi, Bradley Loftin, Coleen E. Martinez, and Blake Van Hoy. "Regulatory Testing and Posttest Analysis of the DPP-3 Type B Shipping Container for NCT and HAC Tests." In ASME 2021 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/pvp2021-62434.

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Abstract Safe transportation of radioactive material using appropriately designed, certified packages ensures protection of the public and environment using rigorous system packaging requirements. This protocol was conducted to determine the effects of drop testing on a Type B shipping package (DPP-3) performance per normal and hypothetical accident conditions tests. Preparation, testing, and post-test evaluations of a Type B shipping package tested to 10 Code of Federal Regulations (CFR) 71.71 and 71.73 requirements are presented. Six packages were subjected to free drop, puncture, and thermal tests per 10 CFR 71.73. The posttest helium leak test showed a leak rate < 1 × 10−7 He-cc/sec, indicating that the containment boundary and leak-tight environment were maintained. Statistical analyses were performed to determine changes in package performance. The null hypothesis was that measurements would not change enough from pre- to post-testing to show statistical significance, which would indicate too much variability in package design. A repeated measures t-test was used to analyze drum lid and containment vessel (CV) torques. All six CV lid torques were statistically significant, but only three drum lid torques were statistically significant. Therefore, lid design caused more variability than necessary when subjected to the horizontal-and-cold, vertical-lid-down, and corner-with-lid-up drop tests. A one-way analysis of variance (ANOVA) analyzed package heights and diameters, with results on height indicating four of the six certification test units (CTUs) were statistically significant. Thus, CTU design caused more variability than necessary when subjected to horizontal-hot, horizontal-cold, corner-lid-up, and corner-lid-down tests. One-way ANOVA analysis of diameter indicated that all six CTUs were statistically significant. Thus, CTU design caused more variability than necessary when subjected to all tests. The DPP-3 has been shown to be an acceptable design for transport of radioactive materials, but statistical analysis revealed that package and test design can be improved to reduce variability in performance before and after testing.
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Reports on the topic "ANOVA test"

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Becker, Sarah, Heather Sussman, S. Blundell, Vern Vanderbilt, and Igor Semyonov. Analysis of spectropolarimetric responses in the visible and infrared for differentiation between similar materials. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45422.

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Spectropolarimetric research has focused on target detections of materials that have a high degree of contrast from background materials, such as identification of a manmade object embedded in a vegetative background. This study presents an approach using spectropolarimetric imagery in visible, shortwave infrared, and longwave infrared bands to differentiate between similar natural and manmade materials. The method employs Michelson contrast and Kruskal-Wallis one-way analysis of variance (ANOVA) H-test to determine if a distinction can be found in pairwise comparisons of similar and different materials using the Stokes parameters in the visible, shortwave infrared, and longwave infrared bands. Results showed that similar natural and manmade materials were differentiable in spectropolarimetric imagery using the Michelson contrast and ANOVA. This approach provides a way to use spectropolarimetric imagery to distinguish between materials that are similar to each other.
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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Tucker-Blackmon, Angelicque. Engagement in Engineering Pathways “E-PATH” An Initiative to Retain Non-Traditional Students in Engineering Year Three Summative External Evaluation Report. Innovative Learning Center, LLC, July 2020. http://dx.doi.org/10.52012/tyob9090.

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The summative external evaluation report described the program's impact on faculty and students participating in recitation sessions and active teaching professional development sessions over two years. Student persistence and retention in engineering courses continue to be a challenge in undergraduate education, especially for students underrepresented in engineering disciplines. The program's goal was to use peer-facilitated instruction in core engineering courses known to have high attrition rates to retain underrepresented students, especially women, in engineering to diversify and broaden engineering participation. Knowledge generated around using peer-facilitated instruction at two-year colleges can improve underrepresented students' success and participation in engineering across a broad range of institutions. Students in the program participated in peer-facilitated recitation sessions linked to fundamental engineering courses, such as engineering analysis, statics, and dynamics. These courses have the highest failure rate among women and underrepresented minority students. As a mixed-methods evaluation study, student engagement was measured as students' comfort with asking questions, collaboration with peers, and applying mathematics concepts. SPSS was used to analyze pre-and post-surveys for statistical significance. Qualitative data were collected through classroom observations and focus group sessions with recitation leaders. Semi-structured interviews were conducted with faculty members and students to understand their experiences in the program. Findings revealed that women students had marginalization and intimidation perceptions primarily from courses with significantly more men than women. However, they shared numerous strategies that could support them towards success through the engineering pathway. Women and underrepresented students perceived that they did not have a network of peers and faculty as role models to identify within engineering disciplines. The recitation sessions had a positive social impact on Hispanic women. As opportunities to collaborate increased, Hispanic womens' social engagement was expected to increase. This social engagement level has already been predicted to increase women students' persistence and retention in engineering and result in them not leaving the engineering pathway. An analysis of quantitative survey data from students in the three engineering courses revealed a significant effect of race and ethnicity for comfort in asking questions in class, collaborating with peers outside the classroom, and applying mathematical concepts. Further examination of this effect for comfort with asking questions in class revealed that comfort asking questions was driven by one or two extreme post-test scores of Asian students. A follow-up ANOVA for this item revealed that Asian women reported feeling excluded in the classroom. However, it was difficult to determine whether these differences are stable given the small sample size for students identifying as Asian. Furthermore, gender differences were significant for comfort in communicating with professors and peers. Overall, women reported less comfort communicating with their professors than men. Results from student metrics will inform faculty professional development efforts to increase faculty support and maximize student engagement, persistence, and retention in engineering courses at community colleges. Summative results from this project could inform the national STEM community about recitation support to further improve undergraduate engineering learning and educational research.
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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, October 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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