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  2. Dot product - Wikipedia

    en.wikipedia.org/wiki/Dot_product

    In modern geometry, Euclidean spaces are often defined by using vector spaces. In this case, the dot product is used for defining lengths (the length of a vector is the square root of the dot product of the vector by itself) and angles (the cosine of the angle between two vectors is the quotient of their dot product by the product of their ...

  3. Scalar multiplication - Wikipedia

    en.wikipedia.org/wiki/Scalar_multiplication

    Scalar multiplication of a vector by a factor of 3 stretches the vector out. The scalar multiplications −a and 2a of a vector a. In mathematics, scalar multiplication is one of the basic operations defining a vector space in linear algebra [1] [2] [3] (or more generally, a module in abstract algebra [4] [5]).

  4. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

  5. Triple product - Wikipedia

    en.wikipedia.org/wiki/Triple_product

    The vector triple product is defined as the cross product of one vector with the cross product of the other two. The following relationship holds: The following relationship holds: a × ( b × c ) = ( a ⋅ c ) b − ( a ⋅ b ) c {\displaystyle \mathbf {a} \times (\mathbf {b} \times \mathbf {c} )=(\mathbf {a} \cdot \mathbf {c} )\mathbf {b ...

  6. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    The most noteworthy property of cosine similarity is that it reflects a relative, rather than absolute, comparison of the individual vector dimensions. For any positive constant and vector , the vectors and are maximally similar. The measure is thus most appropriate for data where frequency is more important than absolute values; notably, term ...

  7. Lorentz scalar - Wikipedia

    en.wikipedia.org/wiki/Lorentz_scalar

    In a relativistic theory of physics, a Lorentz scalar is a scalar expression whose value is invariant under any Lorentz transformation.A Lorentz scalar may be generated from, e.g., the scalar product of vectors, or by contracting tensors.

  8. Subsidy Scorecards: University of North Carolina at Asheville

    projects.huffingtonpost.com/projects/ncaa/...

    SOURCE: Integrated Postsecondary Education Data System, University of North Carolina at Asheville (2014, 2013, 2012, 2011, 2010).Read our methodology here.. HuffPost and The Chronicle examined 201 public D-I schools from 2010-2014.

  9. Vector calculus identities - Wikipedia

    en.wikipedia.org/wiki/Vector_calculus_identities

    The dotted vector, in this case B, is differentiated, while the (undotted) A is held constant. The utility of the Feynman subscript notation lies in its use in the derivation of vector and tensor derivative identities, as in the following example which uses the algebraic identity C⋅(A×B) = (C×A)⋅B: