In the world of linear approximations of multiple parameters and multiple outputs, the Jacobian is a matrix that tells you: if I twist this knob, how does that part of the output change?
Pretend that a through z are parameters, or knobs you can twist. Let’s not say whether you have control over them (endogenous variables) or whether the environment / your customers / your competitors / nature / external factors have control over them (exogenous parameters).
And pretend that F¹ through Fⁿ are the separate kinds of output. You can think in terms of a real number or something else, but as far as I know the outputs cannot be linked in a lattice or anything other than a matrix rectangle.
In other words this matrix is just an organised list of “how parameter c affects output F⁹”.
Notan bene — the Jacobian is just a linear approximation. It doesn’t carry any of the info about mutual influence, connections between variables, curvature, wiggle, womp, kurtosis, cyclicity, or even interaction effects.
A Jacobian tensor would tell you how twisting knob a knocks on through parameters h, l, and p. Still linear but you could work out the outcome better in a difficult system — or figure out what happens if you twist two knobs at once.
In math jargon: the Jacobian is a matrix filled with partial derivatives.
