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$begingroup$ In some literature, I've browse that a regression with several explanatory variables, if in various units, required to be standardized.
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Third, I don't use SPSS so I can't help there, but I might be astonished if it didn't offer some forms of nonlinear regression. Some choices are quantile regression, regression trees and sturdy regression.
$begingroup$ Just in case you use gradient descent to fit your product, standardizing covariates could increase convergence (due to the fact brake pad factory when you have unscaled covariates, the corresponding parameters may well inappropriately dominate the gradient). As an instance this, some R code:
MånsTMånsT 12.1k11 gold badge5151 silver badges6666 bronze badges $endgroup$ two 1 $begingroup$ Is it a smart idea to standarize variables that are quite skewed or is it improved only to standardize symmetrically distributed variables? Really should we stardadize only the enter variables or also the outcomes? $endgroup$
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Fourth, I'm a tad concerned about your statement: I actually need/must accomplish a regression Evaluation to discover which products to the questionnaire predict the reaction to an overall merchandise (gratification)
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