Journal articles: '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 (November 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 (January 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 (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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Shelton, Heather K. "BASIC PREMISES OF FACTORIAL ANALYSIS OF VARIANCE (ANOVA)." Experimental Techniques 27, no. 6 (November 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 (January 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 (June 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 (January 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 (February 9, 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 (February 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 (January 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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Verma, Sourav. "Role of analysis of variance (One way-anova) in music." International Journal of Applied Research 6, no. 8 (August 1, 2020): 12–15. http://dx.doi.org/10.22271/allresearch.2020.v6.i8a.6968.

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Davidson, Lisa. "Smoothing spline analysis of variance (ANOVA) for tongue curve comparison." Journal of the Acoustical Society of America 118, no. 3 (September 2005): 2023–24. http://dx.doi.org/10.1121/1.4785761.

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Gardini, Aldo, Carlo Trivisano, and Enrico Fabrizi. "Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable." Psychometrika 86, no. 2 (June 2021): 619–41. http://dx.doi.org/10.1007/s11336-021-09769-y.

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AbstractThe analysis of variance, and mixed models in general, are popular tools for analyzing experimental data in psychology. Bayesian inference for these models is gaining popularity as it allows to easily handle complex experimental designs and data dependence structures. When working on the log of the response variable, the use of standard priors for the variance parameters can create inferential problems and namely the non-existence of posterior moments of parameters and predictive distributions in the original scale of the data. The use of the generalized inverse Gaussian distributions with a careful choice of the hyper-parameters is proposed as a general purpose option for priors on variance parameters. Theoretical and simulations results motivate the proposal. A software package that implements the analysis is also discussed. As the log-transformation of the response variable is often applied when modelling response times, an empirical data analysis in this field is reported.

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Kim, Hae-Young. "Analysis of variance (ANOVA) comparing means of more than two groups." Restorative Dentistry & Endodontics 39, no. 1 (2014): 74. http://dx.doi.org/10.5395/rde.2014.39.1.74.

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Swanberg, Marika, Ira Globus-Harris, Iris Griffith, Anna Ritz, Adam Groce, and Andrew Bray. "Improved Differentially Private Analysis of Variance." Proceedings on Privacy Enhancing Technologies 2019, no. 3 (July 1, 2019): 310–30. http://dx.doi.org/10.2478/popets-2019-0049.

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Abstract Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the restrictions of differential privacy. We show that the F -statistic, the optimal test statistic in the public setting, is no longer optimal in the private setting, and we develop a new test statistic F1 with much higher statistical power. We show how to rigorously compute a reference distribution for the F1 statistic and give an algorithm that outputs accurate p-values. We implement our test and experimentally optimize several parameters. We then compare our test to the only previous work on private ANOVA testing, using the same effect size as that work. We see an order of magnitude improvement, with our test requiring only 7% as much data to detect the effect.

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Glenn, D. Michael. "Statistical Analysis of Root Count Data." HortScience 30, no. 4 (July 1995): 907A—907. http://dx.doi.org/10.21273/hortsci.30.4.907a.

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The minirhizotron approach for studying the dynamics of root systems is gaining acceptance; however, problems have arisen in the analysis of data. The purposes of this study were to determine if analysis of variance (ANOVA) was appropriate for root count data, and to evaluate transformation procedures to utilize ANOVA. In peach, apple, and strawberry root count data, the variance of treatment means was positively correlated with the mean, violating assumptions of ANOVA. A transformation based on Taylor's power law as a first approximation, followed by a trial and error approach, developed transformations that reduced the correlation of variance and mean.

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Araújo, Adelson Paulo. "Analysis of variance of primary data on plant growth analysis." Pesquisa Agropecuária Brasileira 38, no. 1 (January 2003): 1–10. http://dx.doi.org/10.1590/s0100-204x2003000100001.

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Plant growth analysis presents difficulties related to statistical comparison of growth rates, and the analysis of variance of primary data could guide the interpretation of results. The objective of this work was to evaluate the analysis of variance of data from distinct harvests of an experiment, focusing especially on the homogeneity of variances and the choice of an adequate ANOVA model. Data from five experiments covering different crops and growth conditions were used. From the total number of variables, 19% were originally homoscedastic, 60% became homoscedastic after logarithmic transformation, and 21% remained heteroscedastic after transformation. Data transformation did not affect the F test in one experiment, whereas in the other experiments transformation modified the F test usually reducing the number of significant effects. Even when transformation has not altered the F test, mean comparisons led to divergent interpretations. The mixed ANOVA model, considering harvest as a random effect, reduced the number of significant effects of every factor which had the F test modified by this model. Examples illustrated that analysis of variance of primary variables provides a tool for identifying significant differences in growth rates. The analysis of variance imposes restrictions to experimental design thereby eliminating some advantages of the functional growth analysis.

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Vercruyssen, Max, and James C. Edwaeds. "ANOVA/TT: Analysis of variance teaching template for lotus 1-2-3." Behavior Research Methods, Instruments, & Computers 20, no. 3 (May 1988): 349–54. http://dx.doi.org/10.3758/bf03203857.

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Ziegel, Eric. "Annotated Computer Output for Analysis of Variance of Unbalanced Data: SPSSX ANOVA." Technometrics 31, no. 3 (August 1989): 397–98. http://dx.doi.org/10.1080/00401706.1989.10488585.

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Zhou, Shi Lei, Ya Lin Guan, and Xin Kun Tang. "Analysis of PCB via for Signal Integrity Using ANOVA." Applied Mechanics and Materials 446-447 (November 2013): 956–60. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.956.

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This paper based on ANOVA (ANalysis Of VAriance) presents an investigation in the design of signal via in multilayered printed circuit boards (PCB) technology from a signal integrity point of view. Using the concept of the orthogonal array (OA), different vias physical aspect ratios have been set in the analysis. The impacts of these parameters are investigated with the help for a full-wave electromagnetic simulation soft HFSS. This study demonstrates the factors which is the most influence on the signal integrity.

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de los Campos, Gustavo, Torsten Pook, Agustin Gonzalez-Reymundez, Henner Simianer, George Mias, and Ana I. Vazquez. "ANOVA-HD: Analysis of variance when both input and output layers are high-dimensional." PLOS ONE 15, no. 12 (December 14, 2020): e0243251. http://dx.doi.org/10.1371/journal.pone.0243251.

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Modern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. We propose and evaluate two methods to determine the proportion of variance of an output data set that can be explained by an input data set when both data panels are high dimensional. Our approach uses random-effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on an orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. Using simulations, we show that the MC-ANOVA method gave nearly unbiased estimates. Estimates produced by Eigen-ANOVA were also nearly unbiased, except when the shared variance was very high (e.g., >0.9). We demonstrate the potential insight that can be obtained from the use of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken (Gallus gallus) genomes and to the assessment of inter-dependencies between gene expression, methylation, and copy-number-variants in data from breast cancer tumors from humans (Homo sapiens). Our analyses reveal that in chicken breeding populations ~50,000 evenly-spaced SNPs are enough to fully capture the span of whole-genome-sequencing genomes. In the study of multi-omic breast cancer data, we found that the span of copy-number-variants can be fully explained using either methylation or gene expression data and that roughly 74% of the variance in gene expression can be predicted from methylation data.

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Harrison, S. Kent, and Emilie E. Regnier. "Assessing Herbicide Phytotoxicity with Covariance Analysis." Weed Technology 4, no. 4 (December 1990): 828–32. http://dx.doi.org/10.1017/s0890037x00026488.

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Greenhouse experiments were conducted to determine the statistical precision of estimating herbicide dose-response treatment effects by covariance analysis (ANOCOVA) relative to standard analysis of variance (ANOVA). Analyses of corn seedling response to the translocated herbicides fluazifop-P, sethoxydim, and quizalofop at 10 to 60 g ai ha-1 indicated that treatment effects were estimated with 26 to 116% greater precision by ANOCOVA than ANOVA. Covariance analyses of treatment effects for corn response to the contact herbicides paraquat, acifluorfen, and lactofen at 50 to 300 g ai ha-1 gave 8 to 13% greater precision than ANOVA. Gains in precision by ANOCOVA for all experiments were generally greatest when shoot dry weight was analyzed as the response variable and pretreatment fifth leaf length served as the covariate.

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Govindasami, S., D. Sivakumar, and P. B Sakthivel. "A Study on Adsorption Capacity of Activated Carbons through Analysis of Variance (Anova)." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 449. http://dx.doi.org/10.14419/ijet.v7i3.34.19357.

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Recycling and reusing the waste is one of the agenda of sustainable development. Hence this investigation was carried out on utilizing the industrial sludge as activated carbon. The sludge was collected from treatment unit of sugar mill industry, paper mill industry and tannery industry and activated carbon was prepared and named as sugar mill sludge activated carbon (SSAC), paper mill sludge activated carbon (PSAC) and tannery industry sludge activated carbon (TSAC). Batch studies were performed between dye solution and activated carbon to determine adsorption capacity of adsorbent and optimum contact time for the contact process. The optimum contact time and optimum dosage was found to be 120 min and 3.0 g respectively. The maximum adsorption capacity of SSAC, PSAC & TSAC on the concentrations of 10 mg/l of reactive dye solution for the dosage of 3.0 g was 100 %, 92.88 % and 88.54 %. The experimental data and analysis was checked by ANOVA table analysis to justify mathematically. As per ANOVA table the mean performance of SSAC is higher and TSAC is lower. So, finally it is concluded that the experimental results are justified by the ANOVA table analysis on the adsorption capacity of activated carbons.

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Sutrisno, Sutrisno, and Dewi Wulandari. "Multivariate Analysis of Variance (MANOVA) untuk Memperkaya Hasil Penelitian Pendidikan." AKSIOMA : Jurnal Matematika dan Pendidikan Matematika 9, no. 1 (July 30, 2018): 37. http://dx.doi.org/10.26877/aks.v9i1.2472.

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MANOVA merupakan solusi teknik analisis data kuantitatif bagi peneliti di dunia pendidikan yang ingin mengamati hasil belajar peserta didik dalam rangka menerapkan prinsip kebulatan dalam Kurikulum 2013 (prinsip evaluasi hasil belajar meliputi aspek kognitif, afektif, dan psikomotor). MANOVA mampu mengungkapkan perbedaan yang tidak ditampilkan ANOVA secara terpisah, sehingga dapat meningkatkan kesempatan untuk menemukan perubahan sebagai akibat dari perlakuan yang berbeda dan interaksinya. Dengan demikian, temuan hasil penelitian akan semakin kaya dan sangat berguna bagi perkembangan ilmu pengetahuan. Terdapat dua model analisis variansi yaitu model overparameterized dan model rerata sel. Model rerata sel memberikan pendekatan sederhana dan tidak ambigu, yang dapat digunakan pada data seimbang atau data tidak seimbang. Model ini menggunakan kontras untuk menyatakan efek utama dan interaksi. Uji persyaratan MANOVA meliputi uji normalitas multivariat dengan uji Mardia dan uji homogenitas matriks kovariansi dengan uji Box’s M. Terdapat beberapa statistik uji MANOVA yaitu Wilks’ Lambda, Pillai, Lawley-Hotelling, dan Roy’s Largest Root. Ketika hipotesis nol MANOVA ditolak, maka dilanjutkan ANOVA pada setiap variabel terikat. Apabila hipotesis nol ANOVA ditolak dan variabel bebas memiliki lebih dari dua nilai, maka dilakukan uji post hoc dengan metode Scheffe’. Prosedur ini menjaga taraf kesalahan α. Uji komparasi rerata antar sel tidak dapat dilakukan secara langsung menggunakan General Linear Model (GLM) pada SPSS. Prosedur yang dapat dilakukan adalah memanipulasi data dengan merubah kondisi eksperimentasi menjadi nilai-nilai yang dianggap satu variabel bebas, sehingga dapat dianalisis dengan One-Way ANOVA atau GLM. Kesulitan analisis multivariat pada perhitungannya yang terlalu rumit, sudah terpecahkan dengan adanya software statistik yang semakin canggih.Kata kunci: MANOVA, analisis multivariat, memperkaya hasil, penelitian pendidikan

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Armstrong, R. A., F. Eperjesi, and B. Gilmartin. "The application of analysis of variance (ANOVA) to different experimental designs in optometry." Ophthalmic and Physiological Optics 22, no. 3 (May 2002): 248–56. http://dx.doi.org/10.1046/j.1475-1313.2002.00020.x.

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Petersen, DR, RE Link, IA Golinkin, DD Ruff, EP Kvam, GP McCabe, and AF Grandt. "Application of Analysis of Variance (ANOVA) Statistical Methods to Breaking Load Corrosion Test." Journal of Testing and Evaluation 25, no. 6 (1997): 565. http://dx.doi.org/10.1520/jte11496j.

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邵, 梦瑶. "Construction of Four Factor Analysis of Variance (ANOVA) Model and Cancer Risk Assessment." Advances in Applied Mathematics 10, no. 06 (2021): 2155–65. http://dx.doi.org/10.12677/aam.2021.106225.

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Walsh, John F. "Using Summary Statistics as Data in ANOVA: A SYSTAT Macro." Teaching of Psychology 18, no. 4 (December 1991): 249–51. http://dx.doi.org/10.1207/s15328023top1804_17.

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In laboratory and classroom assignments, students are asked to review research findings. Often the data available include only the means, standard deviations, and number of subjects. A SYSTAT macro is given that generates sufficient information from the data available to compute an analysis of variance (ANOVA) and post hoc analyses.

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Johnson, David E. "An Intuitive Approach to Teaching Analysis of Variance." Teaching of Psychology 16, no. 2 (April 1989): 67–68. http://dx.doi.org/10.1207/s15328023top1602_5.

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A significant number of students in introductory statistics courses may function at Piaget's concrete operational level of thought. These students may find it difficult to understand the complex correlations and interactions between variables that typify many statistical procedures. A technique for introducing analysis of variance (ANOVA) in a concrete fashion is presented. This technique leads students to an intuitive understanding of the concepts of between- and within-groups variance and their relationship to each other.

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Eskridge, Kent M. "999 STATISTICAL ANALYSIS OF DISEASE REACTION DATA USING NONPARAMETRIC METHODS." HortScience 29, no. 5 (May 1994): 572e—572. http://dx.doi.org/10.21273/hortsci.29.5.572e.

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Breeders need powerful and simply understood statistical methods when analyzing disease reaction data. However, many disease reaction experiments result in data which do not adhere to the classical analysis of variance (ANOVA) assumptions of normality, homogeneity variance and a correctly specified model. Nonparametric statistical methods which require fewer assumptions than classical ANOVA, are applied to data from several disease reaction experiments. It is concluded that nonparametric methods are easily understood, can be productively applied to plant disease experiments and many times result in improved chances for detecting differences between treatments.

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Masood, M. Asif, Khalid Mahmood Khokhar, and Irum Raza. "Evaluation of Some Selected Agronomic Characters on Yield of Chilli Cultivars/Lines Using Analysis of Covariance." Bangladesh Journal of Agricultural Research 37, no. 2 (July 14, 2012): 301–6. http://dx.doi.org/10.3329/bjar.v37i2.11233.

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The study was carried out to see the effect of some agronomic variables on yield of chilli cultivars/lines using covariance analysis technique. Data were recorded for yield and other six agronomic variables, namely time to flowering (days), time to maturity (days), fruit weight per plant in grams, average fruit weight in grams, fruit width in centimeters, and fruit length in centimeters. Among six agronomic variables, fruit weight per plant (grams) is highly significant and linearly related to the plant yield having value of correlation coefficient (r) 0.99 whereas average fruit weight (grams) was significant at 5 percent and linearly related to the yield having correlation coefficient value 0.55. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) were run by taking fruit weight per plant (grams) as covariate. The error mean square (EMS) without covariate was 1.344 under ANOVA, while error mean square was 0.007 under ANCOVA with covariate. The results depicted that use of covariate reduced error mean square in ANCOVA. It indicated that ANCOVA is more efficient than ANOVA for improving the results of the experiment. DOI: http://dx.doi.org/10.3329/bjar.v37i2.11233 Bangladesh J. Agril. Res. 37(2): 301-306, June 2012

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Nikje, Mir Mohammad Alavi, Mohammadreza Khanmohammad, Amir Bagheri Garmarudi, and Keyvan Ghasemi. "Analysis of Variance (ANOVA) for Optimizing the Nano-SiO2Content of High Performance Epoxy Nanocomposites." Journal of Macromolecular Science, Part A 46, no. 1 (November 26, 2008): 116–20. http://dx.doi.org/10.1080/10601320802514608.

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Anders, Kallner. "Resolution of Students t-tests, ANOVA and analysis of variance components from intermediary data." Biochemia Medica 27, no. 2 (June 15, 2017): 253–58. http://dx.doi.org/10.11613/bm.2017.026.

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Elango, Umamaheswari, Ganesan Sivarajan, Abirami Manoharan, and Subramanian Srikrishna. "Preventive maintenance scheduling using analysis of variance-based ant lion optimizer." World Journal of Engineering 15, no. 2 (April 9, 2018): 254–72. http://dx.doi.org/10.1108/wje-06-2017-0145.

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Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

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Li, Wei, Ling Lin, and Gang Li. "Wavelength selection method based on test analysis of variance: application to oximetry." Anal. Methods 6, no. 4 (2014): 1082–89. http://dx.doi.org/10.1039/c3ay41601a.

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Garling, L. K., and G. P. Woods. "Enhancing the analysis of variance (ANOVA) technique with graphical analysis and its application to wafer processing equipment." IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A 17, no. 1 (March 1994): 149–52. http://dx.doi.org/10.1109/95.296382.

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Refinetti, Roberto. "Demonstrating the Consequences of Violations of Assumptions in between-Subjects Analysis of Variance." Teaching of Psychology 23, no. 1 (February 1996): 51–54. http://dx.doi.org/10.1207/s15328023top2301_14.

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This article describes how to use a personal computer to conduct a classroom demonstration of the effects of violations of the assumptions of analysis of variance (ANOVA) on the probability of Type I error. The demonstration is based on the idea that if many data sets of randomly selected numbers are submitted to an ANOVA, then the frequency distribution of empirical F values should approximate the probability density curve of the F statistic for the specified degrees of freedom. If violations of the assumptions do not impair the approximation, then the test is robust. Results obtained in various trials were consistent with the statistical literature in showing that violations of the assumptions of normality and homogeneity of variances have a measurable but small effect on the probability of Type I error, especially when all groups are the same size.

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Grund, Simon, Oliver Lüdtke, and Alexander Robitzsch. "Pooling ANOVA Results From Multiply Imputed Datasets." Methodology 12, no. 3 (July 2016): 75–88. http://dx.doi.org/10.1027/1614-2241/a000111.

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Abstract. The analysis of variance (ANOVA) is frequently used to examine whether a number of groups differ on a variable of interest. The global hypothesis test of the ANOVA can be reformulated as a regression model in which all group differences are simultaneously tested against zero. Multiple imputation offers reliable and effective treatment of missing data; however, recommendations differ with regard to what procedures are suitable for pooling ANOVA results from multiply imputed datasets. In this article, we compared several procedures (known as D1, D2, and D3) using Monte Carlo simulations. Even though previous recommendations have advocated that D2 should be avoided in favor of D1 or D3, our results suggest that all procedures provide a suitable test of the ANOVA’s global null hypothesis in many plausible research scenarios. In more extreme settings, D1 was most reliable, whereas D2 and D3 suffered from different limitations. We provide guidelines on how the different methods can be applied in one- and two-factorial ANOVA designs and information about the conditions under which some procedures may perform better than others. Computer code is supplied for each method to be used in freely available statistical software.

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Jia, Qing, and Ling Ling Mu. "Multivariate ANOVA of College Students’ Mental Health." Advanced Materials Research 403-408 (November 2011): 1428–31. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1428.

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This paper used multivariate analysis of variance (MANOVA) method to investigate whether changes in the independent variables such as gender, age, family location etc, have significant effects on the dependent variables. Date was collected from students in Hebei University of Technology, by using Chinese College Student Mental Health Scale (CCSMHS). Result shows that interaction from four factors such as interactions among gender, only-child, major and family location have significant influence on anxiety. Different family locations also affect some dimensions. Mental health of students grow in large cities is significantly better than those from small and medium-sized cities, small towns and rural.

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Yang, Wei, and Anni Jia. "Side-Channel Leakage Detection with One-Way Analysis of Variance." Security and Communication Networks 2021 (March 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/6614702.

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Side-channel analysis (SCA) is usually used for security evaluation to test the side-channel vulnerability of a cryptographic device. However, in practice, an analyser may need to cope with enormous amounts of side-channel measurement data to extract valuable information for SCA. Under the circumstances, side-channel leakage detection can be used to identify leakage points which contain secret information and therefore improve the efficiency of security assessment. This investigation proposes a new black-box leakage detection approach on the basis of the one-way analysis of variance (ANOVA). In accordance with the relevance between leakage points and inputs of a cryptographic algorithm, the proposed method divides side-channel samples into multiple classes and tests the difference among these classes by taking advantage of the one-way ANOVA. Afterwards, leakage points and nonleakage points can be distinguished by determining whether the null hypothesis is accepted. Further, we extend our proposed method to multichannel leakage detection. In particular, a new SCA attack with a F -statistic-based distinguisher is capable of developing if the input of the leakage detection approach is replaced by a sensitive intermediate variable. Practical experiments show the effectiveness of the proposed methods.

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Daud, Kamarulazhar, Ahmad Farid Abidin, and Harapajan Singh Nagindar Singh. "ANOVA Based Feature Analysis and Selection in Power Quality Disturbances Identification." Applied Mechanics and Materials 793 (September 2015): 510–15. http://dx.doi.org/10.4028/www.scientific.net/amm.793.510.

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This study was conducted in order to identify the different types of PQD based on a new approach the Analysis Of Variance (ANOVA). ANOVA is used as feature selection for the Power Quality Disturbances (PQD) parameters. The datum of PQD from the PSCAD/EMTDC® simulation has been validated before feature extraction analysis can be commenced. The obtained datum is then analyzed by using cycle windowing technique based on Continuous S-Transform (CST) to extract the features and its characteristics. Moreover, the study focuses an important issue concerning the identification of PQD selection and detection. The feature and characteristics of four types of signal such as Sag, Swell, Transient and sinusoidal normal signal are obtained. The outcome of the analysis shows that a new approach ANOVA have a different result in term of identification of PQD.

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Qin, Dong Liang, and Zhi Fei Li. "Orthogonal Design and Analysis of Variance Based Performance Analysis of Differential Evolution Algorithm." Advanced Materials Research 694-697 (May 2013): 2751–56. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2751.

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In the "call for paper" of 2013 IEEE Congress on Evolutionary Computation (CEC 2013), Special Session on "Differential Evolution: Past, Present and Future", "Experimental design and analysis of DE" is the third area. In this paper, we propose a rapid analysis approach based on Orthogonal Design (OD) and Analysis of Variance (ANOVA) for performance of DE. The analysis results can be the reliable basis of the principles guiding the creation of adapting rules in novel adaptive DE algorithms.

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Wahlsten, Douglas. "Insensitivity of the analysis of variance to heredity-environment interaction." Behavioral and Brain Sciences 13, no. 1 (March 1990): 109–20. http://dx.doi.org/10.1017/s0140525x00077797.

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AbstractIt makes sense to attribute a definite percentage of variation in some measure of behavior to variation in heredity only if the effects of heredity and environment are truly additive. Additivity is often tested by examining the interaction effect in a two-way analysis of variance (ANOVA) or its equivalent multiple regression model. If this effect is not statistically significant at the α = 0.05 level, it is common practice in certain fields (e.g., human behavior genetics) to conclude that the two factors really are additive and then to use linear models, which assume additivity. Comparing several simple models of nonadditive, interactive relationships between heredity and environment, however, reveals that ANOVA often fails to detect nonadditivity because it has much less power in tests of interaction than in tests of main effects. Likewise, the sample sizes needed to detect real interactions are substantially greater than those needed to detect main effects. Data transformations that reduce interaction effects also change drastically the properties ofthe causal model and may conceal theoretically interesting and practically useful relationships. If the goal ofpartitioning variance among mutually exclusive causes and calculating “heritability” coefficients is abandoned, interactive relationships can be examined more seriously and can enhance our understanding of the ways living things develop.

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Mathes, Timothy K., and Andy Monthusi Chebanne. "An SS ANOVA (Smoothing Spline Analysis of Variance) study of high tone lowering in Tsua." South African Journal of African Languages 38, no. 2 (May 4, 2018): 137–48. http://dx.doi.org/10.1080/02572117.2018.1463701.

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Choi, M., T. Bertalan, C. R. Laing, and I. G. Kevrekidis. "Dimension reduction in heterogeneous neural networks: Generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)." European Physical Journal Special Topics 225, no. 6-7 (September 2016): 1165–80. http://dx.doi.org/10.1140/epjst/e2016-02662-3.

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Xu, Yijun, Lamine Mili, and Junbo Zhao. "Probabilistic Power Flow Calculation and Variance Analysis Based on Hierarchical Adaptive Polynomial Chaos-ANOVA Method." IEEE Transactions on Power Systems 34, no. 5 (September 2019): 3316–25. http://dx.doi.org/10.1109/tpwrs.2019.2903164.

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Cake, Leslie J., and Roy C. Hostetter. "ANOVAGEN: A Data Generation and Analysis of Variance Program for Use in Statistics Courses." Teaching of Psychology 19, no. 3 (October 1992): 185–88. http://dx.doi.org/10.1207/s15328023top1903_21.

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This article describes a BASIC program (ANOVAGEN) for use in statistics courses. ANOVAGEN uses population parameters specified by the instructor to generate unique data for each student. The program also calculates the appropriate analysis of variance (ANOVA) for the unique data of each student. We describe the use of the program and provide samples of student and instructor output. ANOVAGEN helps demonstrate some fundamental concepts in ANOVA, reduces the time required for grading of unique statistical exercises, and encourages students to work independently. Other possible uses of ANOVAGEN, including uses in other courses, are described.

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Burr, T., K. Martin, C. Norman, and K. Zhao. "Analysis of Variance for Item Differences in Verification Data with Unknown Groups." Science and Technology of Nuclear Installations 2019 (January 1, 2019): 1–10. http://dx.doi.org/10.1155/2019/1769149.

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For sequentially collected data, this paper introduces a lag-one differencing method to estimate the random error standard deviation δR and then uses the estimate δ^R to calculate a change detection threshold in a moving window method to detect shifts in the short-term systematic error. Performance results on simulated and real data are presented. Fortunately, the impact of having to perform change detection on the estimated short-term systematic and random error variances is anticipated to be modest or small. The motivating example arises from facilities under nuclear safeguards agreements, where inspector data collected during International Atomic Energy Agency (IAEA) verifications are compared to corresponding operator data. The differences between the operator and inspector values are evaluated using an application of analysis of variance (ANOVA). Typically, it is assumed that short-term systematic errors change across inspection periods, so inspection periods form the groups used in the ANOVA. In some data sets, it appears that the short-term errors have changed at other times, so change detection methods could be used to detect the actual change times.

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Larson, David A., and Ko-Cheng Hsu. "Analysis Of Variance With Summary Statistics In Microsoft Excel." American Journal of Business Education (AJBE) 3, no. 4 (April 1, 2010): 7–12. http://dx.doi.org/10.19030/ajbe.v3i4.406.

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Students regularly are asked to solve Single Factor Analysis of Variance problems given only the sample summary statistics (number of observations per category, category means, and corresponding category standard deviations). Most undergraduate students today use Excel for data analysis of this type. However, Excel, like all other statistical software packages, requires an input data set in order to invoke its Anova: Single Factor procedure. The purpose of this paper is therefore to provide the student with an Excel macro that, given just the sample summary statistics as input, generates an equivalent underlying data set. This data set can then be used as the required input data set in Excel for Single Factor Analysis of Variance.

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Marini, Richard P. "Approaches to Analyzing Experiments with Factorial Arrangements of Treatments Plus Other Treatments." HortScience 38, no. 1 (February 2003): 117–20. http://dx.doi.org/10.21273/hortsci.38.1.117.

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Experiments with factorial arrangements of treatments plus one or more other treatment(s) are sometimes analyzed with a one-way analysis of variance (ANOVA) and means are separated with a multiple comparison. A set of single degree-of-freedom contrasts in a one-way ANOVA, provides formal tests for main effects and interactions. Data from a 2 × 3 factorial experiment that also contained a control were analyzed with a one-way ANOVA with a multiple comparison. Results from this analysis were compared to results obtained from a two-way ANOVA, a one-way ANOVA with pre-planned contrasts, a two-way ANOVA with least squares means comparisons obtained with SAS/general linear models procedure, and a regression model with an indicator variable and random blocks obtained with SAS/Mixed procedure. Results and interpretation differed depending on how the data were analyzed and these differences are discussed.

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

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