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Determining Appropriate Transformations
Transformation vs Weighted Regression
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&BLOCK Analysis of Variance ANOVA arc sine square-root assumption of normality bark DIB Bartlett's test CLASS predictor variables computed constant variance departures from normality diameter inside bark diameter outside bark example exp(a F Value F-test Figure fitted value squared frequency histogram Friedman's Rank Sum histogram homogeneity of variance inverse Kolmogorov-Smirnov test LAMDA Levene's test linear regression analysis linearizing transformation log transformation LOGDOB GT logit MATERIAL 1 TIME'MATERIAL Mean Square MODEL response normal distribution Normal Probability Plot null hypothesis option outliers OUTPUT p-value Parameter Estimates PHOS Pr>F Predicted Value PROC GLM PROC MEANS NOPRINT PROC UNIVARIATE pure error R-square rank transformation relationship residual plot residual sum response variable Root MSE sample sizes SAS statements significant sine square-root transformation skewed Source DF Model Stem-and-Leaf diagram sum of squares UNIVARIATE PROCEDURE variance is proportional variance-stabilizing transformation weighted least squares Weighted Regression zero