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Jun 28, 2023 at 17:10 history edited whuber CC BY-SA 4.0
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Apr 27, 2023 at 12:21 comment added whuber (Continued) I quote from that site, emphasizing the problematic assertions: " The SAS Nonlinear Regression implies curved relationships. In this, the dependent or criterion variables are modeled as a non-linear function of model parameters and one or more independent variables. The reason that these models are called nonlinear regression is that the relationships between the dependent and independent parameters are not linear." The remaining "tutorial" has so many usage errors that it smells very fishy.
Apr 27, 2023 at 12:19 comment added whuber @COOLSerdash Thank you -- I made the change. In researching it, the "featured" Google hit looks like an official SAS page but is truly awful: it can't even maintain the distinction between variables and parameters! For the record, the site to avoid is data-flair.training/blogs/sas-nonlinear-regression.
Apr 27, 2023 at 12:18 history edited whuber CC BY-SA 4.0
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Apr 27, 2023 at 6:08 comment added COOLSerdash The link to the UCLA-page for SAS is dead but I suspect it was this page? Because I'm not sure, I didn't update the link myself.
Jan 27, 2019 at 17:08 comment added whuber @Color I'm not familiar with any. Under mild assumptions about the differentiability of possible transformations, this is addressed by the theory of Partial Differential Equations (PDEs).
Jan 27, 2019 at 16:43 comment added ColorStatistics can you recommend a concise, introductory reading that will help me get a better sense of the linearization you mention, which is the heart of the difference between your example and non-example in point 5. Thank you.
Mar 18, 2016 at 15:47 history edited amoeba CC BY-SA 3.0
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Jun 16, 2015 at 14:05 history edited whuber CC BY-SA 3.0
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Apr 29, 2015 at 7:14 vote accept mHelpMe
Apr 28, 2015 at 16:39 history answered whuber CC BY-SA 3.0