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Cramer's rule is used in the Ricci calculus in various calculations involving the Christoffel symbols of the first and second kind. [14] In particular, Cramer's rule can be used to prove that the divergence operator on a Riemannian manifold is invariant with respect to change of coordinates. We give a direct proof, suppressing the role of the ...
The number of distinct terms (including those with a zero coefficient) in an n-th degree equation in two variables is (n + 1)(n + 2) / 2.This is because the n-th degree terms are ,, …,, numbering n + 1 in total; the (n − 1) degree terms are ,, …,, numbering n in total; and so on through the first degree terms and , numbering 2 in total, and the single zero degree term (the constant).
Cramér’s decomposition theorem, a statement about the sum of normal distributed random variable Cramér's theorem (large deviations) , a fundamental result in the theory of large deviations Cramer's theorem (algebraic curves) , a result regarding the necessary number of points to determine a curve
Cramer's rule is a closed-form expression, in terms of determinants, of the solution of a system of n linear equations in n unknowns. Cramer's rule is useful for reasoning about the solution, but, except for n = 2 or 3, it is rarely used for computing a solution, since Gaussian elimination is a faster algorithm.
one gets, from Cramer's rule or by substituting out a variable, the coordinates of the intersection point (,) : x s = c 1 b 2 − c 2 b 1 a 1 b 2 − a 2 b 1 , y s = a 1 c 2 − a 2 c 1 a 1 b 2 − a 2 b 1 .
Consistency and independence of the equations in the set is established because the determinant of coefficients is non-zero, so a solution can be found using Cramer's rule. Using the examples from the subsection Elements of signal-flow graphs, we construct the graph In the figure, a signal-flow graph in this case.
The logarithmic moment generating function (which is the cumulant-generating function) of a random variable is defined as: = [ ()].Let ,, … be a sequence of iid real random variables with finite logarithmic moment generating function, i.e. () < for all .
It may be viewed as the association between two variables as a percentage of their maximum possible variation. φ c 2 is the mean square canonical correlation between the variables. [citation needed] In the case of a 2 × 2 contingency table Cramér's V is equal to the absolute value of Phi coefficient.