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

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

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

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2

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

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3

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

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Analysis of covariance is often conceptualized as an analysis of variance of a single set of residual scores that are obtained by regressing the dependent variable on the covariate. Although this conceptualization of an equivalence between the two procedures may be intuitively appealing, it is mathematically incorrect. If residuals are obtained from the pooled within-groups regression coefficient ( bw), an analysis of variance on the residuals results in an inflated α-level. If the regression coefficient for the total sample combined into one group ( bT) is used, ANOVA on the residuals yields

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4

Shelton, Heather K. "BASIC PREMISES OF FACTORIAL ANALYSIS OF VARIANCE (ANOVA)." Experimental Techniques 27, no. 6 (2003): 64–66. http://dx.doi.org/10.1111/j.1747-1567.2003.tb00142.x.

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5

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

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6

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

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7

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

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8

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

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9

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

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A multimedia program for teaching analysis of variance (ANOVA) designs is described. The program tests students' understanding of independent groups and repeated measures variables, as well as the source and degrees of freedom columns of the ANOVA source table. The program uses colorful images, brief animations, interactive tasks, and immediate feedback. Information about availability is provided.

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10

Lakens, Daniël, and Aaron R. Caldwell. "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs." Advances in Methods and Practices in Psychological Science 4, no. 1 (2021): 251524592095150. http://dx.doi.org/10.1177/2515245920951503.

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Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analyses often need [Formula: see text] or Cohen’s f as input, but these effect sizes are not intuitive and do not generalize to different experimental desi

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11

Verma, Sourav. "Role of analysis of variance (One way-anova) in music." International Journal of Applied Research 6, no. 8 (2020): 12–15. http://dx.doi.org/10.22271/allresearch.2020.v6.i8a.6968.

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12

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

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13

Gardini, Aldo, Carlo Trivisano, and Enrico Fabrizi. "Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable." Psychometrika 86, no. 2 (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

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14

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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15

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 (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 compu

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16

Glenn, D. Michael. "Statistical Analysis of Root Count Data." HortScience 30, no. 4 (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 tran

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17

Araújo, Adelson Paulo. "Analysis of variance of primary data on plant growth analysis." Pesquisa Agropecuária Brasileira 38, no. 1 (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 transf

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18

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 (1988): 349–54. http://dx.doi.org/10.3758/bf03203857.

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19

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

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20

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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21

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 (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 regress

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22

Harrison, S. Kent, and Emilie E. Regnier. "Assessing Herbicide Phytotoxicity with Covariance Analysis." Weed Technology 4, no. 4 (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

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23

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 (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 optimu

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24

Sutrisno, Sutrisno, and Dewi Wulandari. "Multivariate Analysis of Variance (MANOVA) untuk Memperkaya Hasil Penelitian Pendidikan." AKSIOMA : Jurnal Matematika dan Pendidikan Matematika 9, no. 1 (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 peng

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25

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 (2002): 248–56. http://dx.doi.org/10.1046/j.1475-1313.2002.00020.x.

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26

Petersen, DR, RE Link, IA Golinkin, et al. "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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27

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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28

Walsh, John F. "Using Summary Statistics as Data in ANOVA: A SYSTAT Macro." Teaching of Psychology 18, no. 4 (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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29

Johnson, David E. "An Intuitive Approach to Teaching Analysis of Variance." Teaching of Psychology 16, no. 2 (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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30

Eskridge, Kent M. "999 STATISTICAL ANALYSIS OF DISEASE REACTION DATA USING NONPARAMETRIC METHODS." HortScience 29, no. 5 (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 resu

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31

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 (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)

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32

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 (2008): 116–20. http://dx.doi.org/10.1080/10601320802514608.

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33

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

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34

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 (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 s

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35

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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36

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 (1994): 149–52. http://dx.doi.org/10.1109/95.296382.

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37

Refinetti, Roberto. "Demonstrating the Consequences of Violations of Assumptions in between-Subjects Analysis of Variance." Teaching of Psychology 23, no. 1 (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 vari

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38

Grund, Simon, Oliver Lüdtke, and Alexander Robitzsch. "Pooling ANOVA Results From Multiply Imputed Datasets." Methodology 12, no. 3 (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 simulation

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39

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 i

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40

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). I

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41

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 det

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42

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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43

Wahlsten, Douglas. "Insensitivity of the analysis of variance to heredity-environment interaction." Behavioral and Brain Sciences 13, no. 1 (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. Compar

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44

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 (2018): 137–48. http://dx.doi.org/10.1080/02572117.2018.1463701.

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45

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 (2016): 1165–80. http://dx.doi.org/10.1140/epjst/e2016-02662-3.

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46

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 (2019): 3316–25. http://dx.doi.org/10.1109/tpwrs.2019.2903164.

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47

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 (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,

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48

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, w

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49

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 (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 eq

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50

Marini, Richard P. "Approaches to Analyzing Experiments with Factorial Arrangements of Treatments Plus Other Treatments." HortScience 38, no. 1 (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 A

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