In order to calculate the parameters β0, β1, β2,…, β k, the logistic regression transforms the dependent into a logit variable and then uses maximum likelihood estimation.
After validation, and making a comparison between different available techniques for sand volume, effective porosity and water saturation estimation, multiple regression transform and neural network were used for the first two and latter, respectively.
For comparisons of genotypes, a homogeneity of slopes test was performed following a linear regression transform.
For the log-linear regressions, without transforming the income from annual to "per day" values, many of the model parameters were not statistically significant.
Effects on binary outcomes were assessed with logistic regression, and effects on continuous outcomes by ordinary least squares linear regression, logarithmically transformed to obtain a near normal distribution.
First, the input variables in the previous reactors are substituted by "virtual" quality variables via least squares support vector regression (LSSVR) transform models.
{mathrm{OS}}_{mathrm{D}}=9.988-0.817{mathrm{FS}}_{mathrm{D}}, where OSD is our score for day D and FSD is Farber's score for day D. Using this regression, we transform Farber's scores to our scale.
Both regularized regression with transformed variables and random forests produced results that are useful in bioprocess data mining.
However, regularized linear regression with transformed variables was found successful in modeling the nonlinearity of the data to some extent.
In all computations (ANOVA and logistic regression), the transformed variables for HOMA-IR and CRP were used.
