What's normal anyway? Residual plots are more telling than significance tests when checking ANOVA assumptions
暂无分享,去创建一个
[1] Karl Moder,et al. The two-sample t test: pre-testing its assumptions does not pay off , 2011 .
[2] H. Piepho. Data Transformation in Statistical Analysis of Field Trials with Changing Treatment Variance , 2009 .
[3] Michael D. Casler,et al. Fundamentals of Experimental Design: Guidelines for Designing Successful Experiments , 2015 .
[4] Hans-Peter Piepho,et al. Checking Normality and Homoscedasticity in the General Linear Model Using Diagnostic Plots , 2012, Commun. Stat. Simul. Comput..
[5] M. McIntosh,et al. Can Analysis of Variance Be More Significant , 2015 .
[6] W. R. Schucany,et al. Preliminary Goodness-of-Fit Tests for Normality do not Validate the One-Sample Student t , 2006 .
[7] Claus Thorn Ekstrøm. Teaching "Instant Experience" with Graphical Model Validation Techniques. , 2014 .
[8] Marcin Kozak,et al. The effects of data input errors on subsequent statistical inference , 2015 .
[9] Marla S. McIntosh,et al. “ Is , or is not , the two great ends of Fate”: Errors in Agronomic Research , 2015 .
[10] M. Kozak. Analyzing one-way experiments: a piece of cake of a pain in the neck? , 2009 .
[11] Hans-Peter Piepho,et al. Why Randomize Agricultural Experiments , 2013 .
[12] D G Altman,et al. Statistics notes: Absence of evidence is not evidence of absence , 1995 .
[13] B. Moser,et al. Homogeneity of Variance in the Two-Sample Means Test , 1992 .
[14] Achim Zeileis,et al. Diagnostic Checking in Regression Relationships , 2015 .