[AntonioLao]

To be fair there must be a square. Linear regression uses the method of least squares. Statistics insists the central tendency standard deviation as the difference of second moment expectation minus the square of the mean. Estimation relies on the biased comparison errors of root mean squares. Fermat’s Last Theorem was proven true only for sums of squares. Pythagoras harmonized squares to play music of the spheres. In analytic geometry with such concepts of directrix, latus rectum, focus, vertex, and axis of symmetry could never fully describe conic sections of circle, ellipse, parabola, and hyperbola until the realization that their general equation is expressed by two variables when completing the squares: Ax+Bxy+Cy+Dx+Ey+F=0. Whoever can deny never attempted solving the biggest magic squares. Now people are doing the restricted Sudoku squares. Some doing the more literate no numbers but incomplete crossword puzzles’ single letter per square. Then there is the timeless and popular limited knights’ moves bounded by sixty four alternately colored chessboard squares which easily switch to the quicker, swifter checkers’ dare.

To measure it is not enough (1) to use length, extension, and perimeter. To enclose it is enough to use units of cubes since each can be covered completely by six squares. The 1st is abstracted by Stoke’s Theorem, the 2nd by Gauss’ Theorem into a vanishing directional divergence differential square. Both are possible only if connected to a square theorem where the volume triple integrals is V=∫∫∫div(curlA)dV, the surface double integral is S=∫∫curlA∙NdS, and the single line integral is L=∫A∙dr such that V=S=L is always exactly fair.

Notations: A is an arbitrary vector; div and curl are respectively the inner Del and the outer Del operators, N is the unit normal vector to the surface S, and dr is the infinitesimal radius vector. Reference: textbooks on vector analysis.

[Lloyd Gillespie]

Antonio, have you checked all of J.Gibbs work, since he's the original inventor of vector math?

Lloyd

Originally Posted by AntonioLao To be fair there must be a square. Linear regression uses the method of least squares. Statistics insists the central tendency standard deviation as the difference of second moment expectation minus the square of the mean. Estimation relies on the biased comparison errors of root mean squares. Fermat’s Last Theorem was proven true only for sums of squares. Pythagoras harmonized squares to play music of the spheres. In analytic geometry with such concepts of directrix, latus rectum, focus, vertex, and axis of symmetry could never fully describe conic sections of circle, ellipse, parabola, and hyperbola until the realization that their general equation is expressed by two variables when completing the squares: Ax+Bxy+Cy+Dx+Ey+F=0. Whoever can deny never attempted solving the biggest magic squares. Now people are doing the restricted Sudoku squares. Some doing the more literate no numbers but incomplete crossword puzzles’ single letter per square. Then there is the timeless and popular limited knights’ moves bounded by sixty four alternately colored chessboard squares which easily switch to the quicker, swifter checkers’ dare. To measure it is not enough (1) to use length, extension, and perimeter. To enclose it is enough to use units of cubes since each can be covered completely by six squares. The 1st is abstracted by Stoke’s Theorem, the 2nd by Gauss’ Theorem into a vanishing directional divergence differential square. Both are possible only if connected to a square theorem where the volume triple integrals is V=∫∫∫div(curlA)dV, the surface double integral is S=∫∫curlA∙NdS, and the single line integral is L=∫A∙dr such that V=S=L is always exactly fair. Notations: A is an arbitrary vector; div and curl are respectively the inner Del and the outer Del operators, N is the unit normal vector to the surface S, and dr is the infinitesimal radius vector. Reference: textbooks on vector analysis.

[AntonioLao]

Originally Posted by Lloyd Gillespie have you checked all of J.Gibbs work, since he's the original inventor of vector math?

I tried to find some of his original books or paper but no luck. The following was extracted from the web but not verbatimly:
From 1882 to 1889, Gibbs refined his vector analysis, wrote on optics, and developed a new electrical theory of light. He deliberately avoided theorizing about a TOE. Was this a wise decision?

[Lloyd Gillespie]

Originally Posted by AntonioLao I tried to find some of his original books or paper but no luck. The following was extracted from the web but not verbatimly: From 1882 to 1889, Gibbs refined his vector analysis, wrote on optics, and developed a new electrical theory of light. He deliberately avoided theorizing about a TOE. Was this a wise decision?

You know Antonio, he did come very close to theorizing about a TOE in a few of the laws and principles he wrote. I've come across these in the past, and I think it is to do with his thermodynamics, but I can't put my finger on these laws and principles, at this moment, although I am certain he did write them... As I remember, they are quite profound, in a general sense...

Lloyd

[AntonioLao]

Originally Posted by Lloyd Gillespie I think it is to do with his thermodynamics

Isn't thermodynamics concerns about the second law on entropy and direction of time? These are still unanswered questions. Could he have answered then?