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5:21 AM
@Qmechanic did you delete my comment? If so, thanks :-) I should read the question properly before commenting!
 
5:40 AM
@JohnRennie : Below which post?
 
1
Q: Magnetic field lines are closed lines?

Kani PeniHow is it proven that the magnetic lines are closed lines? Because the divergence of $\mathbf{B}$ is zero does not sound convincing, since fields like $\mathbf{F} = a\mathbf i +b\mathbf j+c\mathbf k$ (with $a$, $b$, and $c$ being constant) do have zero divergence; and yet the field lines in these...

@Qmechanic That one
 
@JohnRennie : Nah, that was another mod :)
 
Ah, it might have been Buzz.
Anyway, I am (once again) grateful to the moderating team! :-)
 
 
2 hours later…
7:41 AM
Hi @JohnRennie can you come to our room?
 
 
3 hours later…
10:38 AM
@Slereah The first page of Weibel is great, the rest...
 
 
4 hours later…
2:14 PM
0
Q: Can I ask this question of physics SE?

Harshit RajputI wanted to know if this question can be asked on Physics SE and if not, can it be discussed here? In almost all mechanics textbooks the quantity $$\int F.\,ds$$ is defined as the work done by the force F. However, since thermodynamics tells us that work is actually a means/method of transferrin...

 
 
9 hours later…
11:42 PM
Dumb terminology question...the linear in linear regression is about the model itself while the linear in linear least squares is about the parameters, correct?
So linear regression is specifically about a model $a_0 + a_1 x$ while linear least squares can handle the more general $a_n x^n$?
I always grouped linear regression into the latter case, but it sounds like that may be incorrect (or at least against the common terminology)
 
11:53 PM
@DanielUnderwood I think terminology in that area is just a mess :P
you can see this confusion in the answers and comments here where people are just really confused about whether "linear regression" means fitting a linear function or a model linear in its parameters
The Wiki article is similarily confused, at one point claiming that fitting a polynomial to the data is linear regression because it's linear in the coefficients, but then talking about a "linear regression line" which only makes sense if you restrict linear regression to fitting a linear function to the data
I think most people will think of fitting a linear function when you say "linear regression", but if you want to be unambiguous you should probably just avoid the term :P
see also this waffling answer about how no one's really sure what "regression" means
 

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