Multi-factor analysis of variance (ANOVA) is used to test the null hypothesis that each effect's level means are all equal, simultaneously for each of multiple factors/effects.
The full version of StatGuide for multi-factor analysis of variance (ANOVA) will be available in a future release. In the meantime, to properly analyze and interpret results of multi-factor analysis of variance, you should be familiar with the following terms and concepts:
If you are not familiar with these terms and concepts, you are advised to consult with a statistician. Failure to understand and properly apply multi-factor analysis of variance (ANOVA) may result in drawing erroneous conclusions from your data. Additionally, you may want to consult the following references:
- Brownlee, K. A. 1965. Statistical Theory and Methodology in Science and Engineering. New York: John Wiley & Sons.
- Daniel, Wayne W. 1995. Biostatistics. 6th ed. New York: John Wiley & Sons.
- Lindman, Harold R. 1992. Analysis of Variance in Experimental Design. New York: Springer-Verlag.
- Miller, Rupert G. Jr. 1996. Beyond ANOVA, Basics of Applied Statistics. 2nd. ed. London: Chapman & Hall. of variance)
- Neter, J., Wasserman, W., and Kutner, M.H. 1990. Applied Linear Statistical Models. 3rd ed. Homewood, IL: Irwin.
- Winer, B.J., Brown, D.R., and Michels, K.M. 1991. Statistical Principles in Experimental Design. 3rd ed. New York: McGraw Hill.
- Rosner, Bernard. 1995. Fundamentals of Biostatistics. 4th ed. Belmont, California: Duxbury Press.
- Sokal, Robert R. and Rohlf, F. James. 1995. Biometry. 3rd. ed. New York: W. H. Freeman and Co.
- Zar, Jerrold H. 1996. Biostatistical Analysis. 3rd ed. Upper Saddle River, NJ: Prentice-Hall.