Thanks for the link. I just tried how is work regression analysis in lazarus or compare gnuplot. I usually use Rstat for my work (agrochemistry).

Yes I know what is the nonlinear (but linearizable) regression functions:logarithmic,power, exp, logistic,s,quadratic,growth etc... to normalise the variables transform by ln (x+0.001) to ensure that the absolute values of skewness and kurtosis. The normality of residuals i checked again by the absolute values of their skewness and kurtosis.

For a logarithm function, the variable y is linear with the logarithm of x context, i.e. y reacts additively to the product of x. In the case of an exponential relation, in contrast to the logarithm function, y its logarithm is linearly related to x. The exponential in the context of y the rate of increase of proportional to v has already been reached value. For a power function, the logarithm of y is linear with the logarithm of x context. The parabolic function and the quadratic function are polynomial belongs to a family of functions, this family of functions is practically any suitable for describing the relationship, but the lawfulness of the relationship most of the time they are not characterized. In the case of a logistic function, the values of the dependent variable are slow first and then they grow faster and faster and then slow down again toward an upper limit approaching.

In my work I usually use Shapiro-Wilk normality test or KS.(before ANOVA, et). Test of normality...

For example ANOVA is robust for mild impairment of the normality condition, in which case it is sufficient to check a weaker condition. Peak skewness test is used when normality is not acceptable with conventional tests used for normality testing. The essence of the method is that the quotient of the shortness (kurtosis) and the standard error, as well as the ratio of the skewness and the standard error should not exceed 3.3.

All the best.