Arcsine

square-root transformation was used to normalize percentage parasitism data.

For these data, the square-root transformation considerably reduced the distribution asymmetry, as illustrated in Fig.

The PBI distribution was heavily skewed towards low values and corrected by applying a square-root transformation of the PBI values.

We present a variety of log RR and linear RR models, as described below, including a categorical exposure model (deciles), a linear (untransformed) exposure model (e.g., RR = 1 + cumexp), a model using the log transformation of exposure [(e.g., RR = 1 + log(cumexp)], a model using a

square-root transformation (e.g., RR = 1 + square root (cumexp), a two-piece spline model, a linear-exponential model (used only in the linear RR case), and a Michaelis-Menten model.

Square-root transformation was used in this study because the

square-root transformation performed better than logarithm or inverse transformation in terms &statistical criteria

Along with logarithmic transformation, the spreadsheet has

square-root transformation for counts of injuries or events, arcsine-root transformation for proportions, and percentile-rank transformation (equivalent to non-parametric analysis) when an appropriate formula for a transformation function is unclear or unspecifiable (Hopkins, 2003c and other pages).

Arcsine

square-root transformation was applied to the proportion of branching nodes and

square-root transformation to mean first-order rhizome length.

A

square-root transformation of the TC, OC, IC and TN content data before derivation of PLSR models reduced non-linearity and improved the homogeneity of residuals.

Angular transformation was performed on the percent of mortality by

square-root transformation before the analysis.

Because only a log transformation was used in the original analysis, the independent analysis investigated the use of untransformed data or a

square-root transformation procedure.

A

square-root transformation of the CEC/clay ratio was tested to try to correct for the non-normality of this variable (Fig.

[...] The constant-variance condition has led to the introduction of the inverse sine, inverse, sinh and

square-root transformations which are used nowadays in many fields of applications" In linear regression models, the unequal variance of the error terms produces estimates that are unbiased but are no longer best in the sense of having the smallest variance [9].