Do not power law fit your process data for predictive models. No. Stop. Put the keyboard down. Your model will almost certainly fail to extrapolate beyond the training range. Instead, think for at least two seconds about the chemistry and the process, maybe review your kinetics textbook, and only then may you fit to a physics-based model for which you will determine proper statistical significance. Poor fit? Too bad, revise your assumptions or reconsider whether your “data” are really just noise.
Always run qNMR with an internal standard if you are using it to determine purity. And, as a corollary, do not ignore unidentified peaks. Yes, even if it “has always been that way”.
DOE models almost certainly tell you less than you think they do, especially when cross-terms are involved, or when the effects are categorical, or when running a fractional factorial design…
Do not power law fit your process data for predictive models. No. Stop. Put the keyboard down. Your model will almost certainly fail to extrapolate beyond the training range. Instead, think for at least two seconds about the chemistry and the process, maybe review your kinetics textbook, and only then may you fit to a physics-based model for which you will determine proper statistical significance. Poor fit? Too bad, revise your assumptions or reconsider whether your “data” are really just noise.
Always run qNMR with an internal standard if you are using it to determine purity. And, as a corollary, do not ignore unidentified peaks. Yes, even if it “has always been that way”.
DOE models almost certainly tell you less than you think they do, especially when cross-terms are involved, or when the effects are categorical, or when running a fractional factorial design…