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How to cite this paper
APA: Black, G., Ard, D., Smith, J & Schibik, S. (2010). The impact of the Weibull distribution on the performance of the single-factor ANOVA model. International Journal of Industrial Engineering Computations, 1(2), 185-198.
Chicago/Turabian: Black, G., Ard, D., Smith, J & Schibik, S. 2010. "The impact of the Weibull distribution on the performance of the single-factor ANOVA model." International Journal of Industrial Engineering Computations 1, no. 2 (2010): 185-198.
AMA: Black, G., Ard, D., Smith, J & Schibik, S. The impact of the Weibull distribution on the performance of the single-factor ANOVA model. International Journal of Industrial Engineering Computations. 2010;1(2):185-198.
References
Barringer and Associates, Inc. (2001). Weibull Reliability Database for failure data for various components. Computer database (http://www.barringer1.com/wdbase.htm).
Brown, M. B., & Forsythe, A. B. (1974). The small sample behavior of some statistics which test the equality of several means. Technometrics, 16, 385-389.
David, F.N., & N.L. Johnson. (1951). The effect of Non-normality on the Power Function of the F-Test the Analysis of Variance. Biometrika, 38, 43-57.
Donaldson, T. S. (1968). Robustness of the F-test to Errors of both kinds and the Correlation Between the Numerator and the Denominator of the F-Ratio. Journal of American Statistical Association (June), 660-676.
Driscoll, W. C. (1990). Bootstrapping: An Alternative to ANOVA. Computers & Industrial Engineering 19, 562-566.
Driscoll, W. C. (1996). Robustness of the ANOVA and Tukey-Kramer statistical tests. Computers & Industrial Engineering, 31, 265-268.
Games, P. A., & Lucas, P.A. (1966). Power of the Analysis of Variance of Independent Groups and Non-normal and Normally Transformed Data. Educational and Psychological Measurement, 26(2), 311-327.
Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972). Consequences of Failure to meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance. Review of Educational Research, 42 (3), 237-288.
Harwell, M., Rubinstein, E., Hayes, W., & Olds, C. (1992). Summarizing Monte Carlo Results in Methodological Research: The One- and Two-Factor Fixed Effects ANOVA Cases. Journal of Educational Statistics, 17(4), 315-339.
James, G.S. (1951). The Comparison of Several Groups of Observations When the Ratios of the Population Variances are Unknown. Biometrika, 38, 324-329.
Kruskal, W. H. & Wallis, W. A. (1952) Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47, 583–621.
Li Li (2007). Monte Carlo Methods for Modified ANOVA. Thesis. Kaiserslautern University of Technology, Germany.
Lix, Lisa M., Keselman, J. C. & Keselman, H.J. (1996). Consequences of Assumption Violations Revisited: A Quantitative Review of Alternatives to the One-Way Analysis of Variance F-Test. Review of Educational Research (Winter), 579-620.
Mendes, M. (2007). The Effects of Non-Normality on Type III Error for Comparing Independent Means. Journal of Applied Quantitative Methods, 2(4), 444-454.
Montgomery, D. C. (2001). Design and Analysis of Experiments (5th Edition). John Wiley and Sons, Inc, New York, NY. 684pp.
Montgomery, D. & George Runger (2010). Applied Statistics and Probability for Engineers (5th Edition), John Wiley and Sons, Inc, New York, NY. 784pp.
Srivastava, A. B. L. (1959). Effect of Non-normality on the Power Function of the Analysis of Variance. Biometrika 46, 114-122.
Smith, J.R. (1966). The Robustness to Non-Normality of the Size and Power of the F-Test in the One-way Fixed Effects Analysis of Variance. M.S. Thesis. Virginia Polytechnic Institute.
Welch, B. L. (1951). On the comparison of several mean values: An alternative approach. Biometrika, 38, 330-336.
Brown, M. B., & Forsythe, A. B. (1974). The small sample behavior of some statistics which test the equality of several means. Technometrics, 16, 385-389.
David, F.N., & N.L. Johnson. (1951). The effect of Non-normality on the Power Function of the F-Test the Analysis of Variance. Biometrika, 38, 43-57.
Donaldson, T. S. (1968). Robustness of the F-test to Errors of both kinds and the Correlation Between the Numerator and the Denominator of the F-Ratio. Journal of American Statistical Association (June), 660-676.
Driscoll, W. C. (1990). Bootstrapping: An Alternative to ANOVA. Computers & Industrial Engineering 19, 562-566.
Driscoll, W. C. (1996). Robustness of the ANOVA and Tukey-Kramer statistical tests. Computers & Industrial Engineering, 31, 265-268.
Games, P. A., & Lucas, P.A. (1966). Power of the Analysis of Variance of Independent Groups and Non-normal and Normally Transformed Data. Educational and Psychological Measurement, 26(2), 311-327.
Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972). Consequences of Failure to meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance. Review of Educational Research, 42 (3), 237-288.
Harwell, M., Rubinstein, E., Hayes, W., & Olds, C. (1992). Summarizing Monte Carlo Results in Methodological Research: The One- and Two-Factor Fixed Effects ANOVA Cases. Journal of Educational Statistics, 17(4), 315-339.
James, G.S. (1951). The Comparison of Several Groups of Observations When the Ratios of the Population Variances are Unknown. Biometrika, 38, 324-329.
Kruskal, W. H. & Wallis, W. A. (1952) Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47, 583–621.
Li Li (2007). Monte Carlo Methods for Modified ANOVA. Thesis. Kaiserslautern University of Technology, Germany.
Lix, Lisa M., Keselman, J. C. & Keselman, H.J. (1996). Consequences of Assumption Violations Revisited: A Quantitative Review of Alternatives to the One-Way Analysis of Variance F-Test. Review of Educational Research (Winter), 579-620.
Mendes, M. (2007). The Effects of Non-Normality on Type III Error for Comparing Independent Means. Journal of Applied Quantitative Methods, 2(4), 444-454.
Montgomery, D. C. (2001). Design and Analysis of Experiments (5th Edition). John Wiley and Sons, Inc, New York, NY. 684pp.
Montgomery, D. & George Runger (2010). Applied Statistics and Probability for Engineers (5th Edition), John Wiley and Sons, Inc, New York, NY. 784pp.
Srivastava, A. B. L. (1959). Effect of Non-normality on the Power Function of the Analysis of Variance. Biometrika 46, 114-122.
Smith, J.R. (1966). The Robustness to Non-Normality of the Size and Power of the F-Test in the One-way Fixed Effects Analysis of Variance. M.S. Thesis. Virginia Polytechnic Institute.
Welch, B. L. (1951). On the comparison of several mean values: An alternative approach. Biometrika, 38, 330-336.