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I have 2 data sets of equal length. I've done a LinearModelFit of them which gives me:

d = Transpose[{d1, d2}];
lm = LinearModelFit[d, x, x]

FittedModel[0.000245082 + 0.000514887x]

This reflects a relationship of y axis values that vary proportionately to time. I actually suspect that visually seeing this relationship having the data scaled to it may turn up some interesting patterns.

I have a notion of now scaling a plot of the original data proportionate to the FittedModel. Not certain how to go about this and I may not have even articulated the question clearly. A clue, some fields refer to this as "square" scaling, but I haven't seen the term used or described widely.

Maybe what doesn't seem to work to do this might help clarify things.

I do know that AspectRatio doesn't seem to get me to what I have in mind. It scales the entire plot including the axes labels and titles. I want to scale the plot area (within the plot frame) itself.

It does seem that I might create a couple of plots, say one without labels with the AspectRatio then another with just the axes labels and combine them with Show or Grid, but this seems cumbersome.

I travel over the holiday weekend, so I may not have the connectivity to respond with additional information until late Monday. I will if I can.

In the meantime, any thoughts appreciated.

Late thought: Would ScalingFunctions help to do this?

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Hi :) do you still want an answer? Maybe you've got one? If not, any new claryfications? –  Kuba Mar 18 at 18:10
@Kuba - Gosh, long time, but yes I would like an answer. Reading through the question again, I wonder if I can do something with LogPlot, but I've yet to see think through to a solution. –  Jagra Mar 19 at 11:43
I'm asking because I'm scanning old topics to close. I think community may think about this one more time :) but for me it is unclear what you are asking about :/. Could you provide better explanation? –  Kuba Mar 19 at 16:29
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