Relevant bibliographies by topics / Analysis of variance

Contents

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

Academic literature on the topic 'Analysis of variance'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Analysis of variance"

1

Butt, Maggie. "Variance analysis." Accounting, Auditing & Accountability Journal 23, no. 6 (August 3, 2010): 816. http://dx.doi.org/10.1108/09513571011065899.

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Finkler, Steven A. "Variance Analysis." JONA: The Journal of Nursing Administration 21, no. 7 (July 1991): 19???25. http://dx.doi.org/10.1097/00005110-199107000-00006.

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Finkler, Steven A. "Variance Analysis." JONA: The Journal of Nursing Administration 21, no. 9 (September 1991): 9???15. http://dx.doi.org/10.1097/00005110-199109000-00004.

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Gaddis, Monica L. "Statistical Methodology: IV. Analysis of Variance, Analysis of Co variance, and Multivariate Analysis of Variance." Academic Emergency Medicine 5, no. 3 (March 1998): 258–65. http://dx.doi.org/10.1111/j.1553-2712.1998.tb02624.x.

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Rice, W. R., and S. D. Gaines. "One-way analysis of variance with unequal variances." Proceedings of the National Academy of Sciences 86, no. 21 (November 1, 1989): 8183–84. http://dx.doi.org/10.1073/pnas.86.21.8183.

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Larson, Martin G. "Analysis of Variance." Circulation 117, no. 1 (January 2008): 115–21. http://dx.doi.org/10.1161/circulationaha.107.654335.

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Rouhani, Shahrokh. "Variance Reduction Analysis." Water Resources Research 21, no. 6 (June 1985): 837–46. http://dx.doi.org/10.1029/wr021i006p00837.

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Smeeton, Nigel C., G. R. Iverson, and H. Norpoth. "Analysis of Variance." Statistician 37, no. 3 (1988): 351. http://dx.doi.org/10.2307/2348185.

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Hess, Aaron S., and John R. Hess. "Analysis of variance." Transfusion 58, no. 10 (September 10, 2018): 2255–56. http://dx.doi.org/10.1111/trf.14790.

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Tybout, Alice, Brian Sternthal, Geoffrey Keppel, Joseph Verducci, Joan Meyers-Levy, James Barnes, Scott Maxwell, Greg Allenby, Sachin Gupta, and Jan-Benedict Steenkamp. "Analysis of Variance." Journal of Consumer Psychology 10, no. 1-2 (2001): 5–35. http://dx.doi.org/10.1207/s15327663jcp1001&2_03.

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Dissertations / Theses on the topic "Analysis of variance"

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Ramazi, Pouria. "Variance Analysis of Parallel Hammerstein Models." Thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102169.

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In this thesis we generalize some recent results on variance analysis of Hammerstein models. A variance formula for an arbitrary number of parallel blocks is derived. This expression shows that the variance increases in one block due to the estimation of parameters in other blocks but levels off when the number of parameters in other blocks reach the number of parameters in that block. As a second contribution, the problem of how to design the input so that the identification process leads to a more accurate model is considered. In other words, how to choose the input signal so that the model error described previously is minimized, is studied. The investigations show that the optimal input probability density function has a surprisingly simple format. In summary, some of the derived results can be used directly in practice, while some might be used for further research.
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Zoglat, Abdelhak. "Analysis of variance for functional data." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/10136.

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In this dissertation we present an extension to the well known theory of multivariate analysis of variance. In various situations data are continuous stochastic functions of time or space. The speed of pollutants diffusing through a river, the real amplitude of a signal received from a broadcasting satellite, or the hydraulic conductivity rates at a given region are examples of such processes. After the mathematical background we develop tools for analyzing such data. Namely, we develop estimators, tests, and confidence sets for the parameters of interest. We extend these results, obtained under the normality assumption, and show that they are still valid if this assumption is relaxed. Some examples of applications of our techniques are given. We also outline how the latter can apply to random and mixed models for continuous data. In the appendix, we give some programs which we use to compute the distributions of some of our tests statistics.
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Di, Gessa Giorgio. "Simple strategies for variance uncertainty in meta-analysis." Connect to e-thesis, 2007. http://theses.gla.ac.uk/128/.

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Thesis (M.Sc.(R)) - University of Glasgow, 2007.
M.Sc.(R) thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2007. Includes bibliographical references. Print version also available.
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Nisa, Khoirin. "On multivariate dispersion analysis." Thesis, Besançon, 2016. http://www.theses.fr/2016BESA2025.

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Cette thèse examine la dispersion multivariée des modelés normales stables Tweedie. Trois estimateurs de fonction variance généralisée sont discutés. Ensuite dans le cadre de la famille exponentielle naturelle deux caractérisations du modèle normal-Poisson, qui est un cas particulier de modèles normales stables Tweedie avec composante discrète, sont indiquées : d'abord par fonction variance et ensuite par fonction variance généralisée. Le dernier fournit la solution à un problème particulier d'équation de Monge-Ampère. Enfin, pour illustrer l'application de la variance généralisée des modèles Tweedie stables normales, des exemples à partir des données réelles sont fournis
This thesis examines the multivariate dispersion of normal stable Tweedie (NST) models. Three generalize variance estimators of some NST models are discussed. Then within the framework of natural exponential family, two characterizations of normal Poisson model, which is a special case of NST models with discrete component, are shown : first by variance function and then by generalized variance function. The latter provides a solution to a particular Monge-Ampere equation problem. Finally, to illustrate the application of generalized variance of normal stable Tweedie models, examples from real data are provided
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Khalilzadeh, Amir Hossein. "Variance Dependent Pricing Kernels in GARCH Models." Thesis, Uppsala universitet, Analys och tillämpad matematik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-180373.

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Nagarajan, Balaji. "Analytic Evaluation of the Expectation and Variance of Different Performance Measures of a Schedule under Processing Time Variability." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/31264.

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The realm of manufacturing is replete with instances of uncertainties in job processing times, machine statuses (up or down), demand fluctuations, due dates of jobs and job priorities. These uncertainties stem from the inability to gather accurate information about the various parameters (e.g., processing times, product demand) or to gain complete control over the different manufacturing processes that are involved. Hence, it becomes imperative on the part of a production manager to take into account the impact of uncertainty on the performance of the system on hand. This uncertainty, or variability, is of considerable importance in the scheduling of production tasks. A scheduling problem is primarily to allocate the jobs and determine their start times for processing on a single or multiple machines (resources) for the objective of optimizing a performance measure of interest. If the problem parameters of interest e.g., processing times, due dates, release dates are deterministic, the scheduling problem is relatively easier to solve than for the case when the information is uncertain about these parameters. From a practical point of view, the knowledge of these parameters is, most often than not, uncertain and it becomes necessary to develop a stochastic model of the scheduling system in order to analyze its performance. Investigation of the stochastic scheduling literature reveals that the preponderance of the work reported has dealt with optimizing the expected value of the performance measure. By focusing only on the expected value and ignoring the variance of the measure used, the scheduling problem becomes purely deterministic and the significant ramifications of schedule variability are essentially neglected. In many a practical cases, a scheduler would prefer to have a stable schedule with minimum variance than a schedule that has lower expected value and unknown (and possibly high) variance. Hence, it becomes apparent to define schedule efficiencies in terms of both the expectation and variance of the performance measure used. It could be easily perceived that the primary reasons for neglecting variance are the complications arising out of variance considerations and the difficulty of solving the underlying optimization problem. Moreover, research work to develop closed-form expressions or methodologies to determine the variance of the performance measures is very limited in the literature. However, conceivably, such an evaluation or analysis can only help a scheduler in making appropriate decisions in the face of uncertain environment. Additionally, these expressions and methodologies can be incorporated in various scheduling algorithms to determine efficient schedules in terms of both the expectation and variance. In our research work, we develop such analytic expressions and methodologies to determine the expectation and variance of different performance measures of a schedule. The performance measures considered are both completion time and tardiness based measures. The scheduling environments considered in our analysis involve a single machine, parallel machines, flow shops and job shops. The processing times of the jobs are modeled as independent random variables with known probability density functions. With the schedule given a priori, we develop closed-form expressions or devise methodologies to determine the expectation and variance of the performance measures of interest. We also describe in detail the approaches that we used for the various scheduling environments mentioned earlier. The developed expressions and methodologies were programmed in MATLAB R12 and illustrated with a few sample problems. It is our understanding that knowing the variance of the performance measure in addition to its expected value would aid in determining the appropriate schedule to use in practice. A scheduler would be in a better position to base his/her decisions having known the variability of the schedules and, consequently, can strike a balance between the expected value and variance.
Master of Science
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Meterelliyoz, Kuyzu Melike. "Variance parameter estimation methods with re-use of data." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26490.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.
Committee Co-Chair: Alexopoulos, Christos; Committee Co-Chair: Goldsman, David; Committee Member: Kim, Seong-Hee; Committee Member: Shapiro, Alexander; Committee Member: Spruill, Carl. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Chao, Jackson Sheng-Kuang. "Analysis of variance impact on manufacturing flow time." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13339.

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Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1991, and Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1991.
Includes bibliographical references (leaves 119-120).
by Jackson Sheng-Kuang Chao.
M.S.
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Pathiravasan, Chathurangi Heshani Karunapala. "Generalized Semiparametric Approach to the Analysis of Variance." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1702.

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The one-way analysis of variance (ANOVA) is mainly based on several assumptions and can be used to compare the means of two or more independent groups of a factor. To relax the normality assumption in one-way ANOVA, recent studies have considered exponential distortion or tilt of a reference distribution. The reason for the exponential distortion was not investigated before; thus the main objective of the study is to closely examine the reason behind it. In doing so, a new generalized semi-parametric approach for one-way ANOVA is introduced. The proposed method not only compares the means but also variances of any type of distributions. Simulation studies show that proposed method has favorable performance than classical ANOVA. The method is demonstrated on meteorological radar data and credit limit data. The asymptotic distribution of the proposed estimator was determined in order to test the hypothesis for equality of one sample multivariate distributions. The power comparison of one sample multivariate distributions reveals that there is a significant power improvement in the proposed chi-square test compared to the Hotelling's T-Square test for non normal distributions. A bootstrap paradigm is incorporated for testing equidistributions of multiple samples. As far as power comparison simulations for multiple large samples are considered, the proposed test outperforms other existing parametric, nonparametric and semi-parametric approaches for non normal distributions.
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Prosser, Robert James. "Robustness of multivariate mixed model ANOVA." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25511.

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

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Institute of Cost and Management Accountants., ed. Variance analysis. London: Financial Training, 1986.

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Iversen, Gudmund, and Helmut Norpoth. Analysis of Variance. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1987. http://dx.doi.org/10.4135/9781412983327.

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Lisa, Custer, Six Sigma Research Institute, and Motorola University Press, eds. Analysis of variance. Reading, Mass: Addison-Wesley, 1993.

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Giri, N. Analysis of Variance. Daryaganj, New Delhi, India: South Asian Publishers, 1986.

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Helmut, Norpoth, ed. Analysis of variance. 2nd ed. Newbury Park: Sage Publications, 1987.

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Bray, James, and Scott Maxwell. Multivariate Analysis of Variance. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1985. http://dx.doi.org/10.4135/9781412985222.

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Sahai, Hardeo, and Mohammed I. Ageel. The Analysis of Variance. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1344-4.

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Hirotsu, Chihiro. Advanced Analysis of Variance. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119303374.

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DeVincenzo, Marie, and Hari Rajagopalan. Analysis of Variance (ANOVA). 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071910443.

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Scheffé, Henry. The analysis of variance. New York: Wiley-Interscience Publication, 1999.

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Book chapters on the topic "Analysis of variance"

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Stevens, P., and B. Kriefman. "Variance Analysis." In Work Out Accounting A-Level, 166–76. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12640-8_17.

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Stevens, P., and B. Kriefman. "Variance Analysis." In Work Out Accounting A Level, 182–92. London: Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13781-7_17.

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Stevens, P., and B. Kriefman. "Variance Analysis." In Work Out Accounting ‘A’ Level, 163–73. London: Macmillan Education UK, 1988. http://dx.doi.org/10.1007/978-1-349-09807-1_17.

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Brandt, Siegmund. "Analysis of Variance." In Data Analysis, 307–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03762-2_11.

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Borgonovo, Emanuele. "Variance-Based Methods." In Sensitivity Analysis, 139–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52259-3_15.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Analysis of Variance." In Multivariate Analysis, 147–203. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_3.

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Brandt, Siegmund. "Analysis of Variance." In Data Analysis, 396–412. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1446-5_11.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Analysis of Variance." In Multivariate Analysis, 147–202. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_3.

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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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Conference papers on the topic "Analysis of variance"

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Teodorescu, Horia-Nicolai. "Using local variance, Allan- and Hadamard variances in speech analysis – Pitch analysis." In 2019 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2019. http://dx.doi.org/10.1109/isscs.2019.8801757.

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Xu, X. F. "Probabilistic Resonance and Variance Spectra." In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.085.

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Milojević, Marko, Lidija Barjaktarović, and Zlatomir Milošev. "VARIANCE ANALYSIS IN MANUFACTURING COMPANIES." In FINIZ 2015. Belgrade, Serbia: Singidunum University, 2015. http://dx.doi.org/10.15308/finiz-2015-102-110.

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Yin, Gang, Federico Lucas, Rolf Balte, and John G. Cherng. "Experimental Variance Analysis of Statistical Energy Analysis." In SAE 2005 Noise and Vibration Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2005. http://dx.doi.org/10.4271/2005-01-2428.

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Bittner, Alvah. "Analysis-of-variance (ANOVA) Assumptions Review: Normality, Variance Equality, and Independence." In XXXIVth Annual International Occupational Ergonomics and Safety Conference. International Society for Occupational Ergonomics and Safety, 2022. http://dx.doi.org/10.47461/isoes.2022_bittner.

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Correia, C., H. F. Raynaud, C. Kulcsár, and J. M. Conan. "Minimum variance control for the woofer-tweeter concept." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/aopt.2009.aowb4.

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Correia, C., J. P. Véran, and L. Poyneer. "Gemini Planet Imager minimum-variance tip-tilt controllers." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/aopt.2011.amb2.

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Saleh, Khaled, Vikrant Aute, Shapour Azarm, and Reinhard Radermacher. "Online Approximation Assisted Multiobjective Optimization with Space Filling, Variance and Pareto Measures with Space Filling, Variance and Pareto Measures." In 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-9103.

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Ferreira, Dário, Sandra S. Ferreira, and Célia Nunes. "Confidence regions for variance components using inducing pivot variables." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756493.

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Kaushik Mahata and Damian Marelli. "Minimum variance interpolation and spectral analysis." In 2009 16th International Conference on Digital Signal Processing (DSP). IEEE, 2009. http://dx.doi.org/10.1109/icdsp.2009.5201230.

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Reports on the topic "Analysis of variance"

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

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

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Goodall, Colin. The Analysis of Averages and the Analysis of Variance. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada198467.

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

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5

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

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

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7

Watts, Adam. Analysis of Variance of Functional Data (F-ANOVA). Office of Scientific and Technical Information (OSTI), January 2024. http://dx.doi.org/10.2172/2282510.

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8

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

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9

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

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10

Khatri, Purvesh, Dechang Chen, Jaques Reifman, Craig M. Lilly, and Larry A. Sonna. Software Tool for Analysis of Variance of DNA Microarray Data. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada460048.

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