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Collinear

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Collinear

Not to be confused with colinear map.

In geometry, collinearity is a property of a set of points, specifically, the property of lying on a single line.[1] A set of points with this property is said to be collinear (often misspelled as colinear). In greater generality, the term has been used for aligned objects, that is, things being "in a line" or "in a row". In statistics, collinearity refers to an exact or approximate linear relationship between two explanatory variables, multicollinearity extends the concept to more than two explanatory variables, and lateral collinearity expands the concept still further.

Points on a line

In most geometries a line is a primitive (undefined) object type. A model for the geometry supplies an interpretation of what points, lines and other object types are. This abstract point of view requires a richer vocabulary to describe its concepts than does Euclidean geometry. Thus, in spherical geometry, where lines are represented in the standard model by great circles of a sphere, sets of collinear points lie on the same great circle. Such points do not lie on a "straight line" in the Euclidean sense, and are not thought of as being in a row. Of course, if the geometry is Euclidean, then collinear points lie on a straight line.

Linear maps (or linear functions), as geometric maps, preserve the collinearity property (that is, they map collinear point sets to collinear point sets), which is another way to say that they map lines to lines. In projective geometry such mappings are called homographies and are a special type of collineation.

Examples in Euclidean geometry

Triangles

In any triangle the following sets of points are collinear:

  • Any vertex, the tangency of the opposite side with an excircle, and the Nagel point are collinear in a line called a splitter of the triangle.
  • Any vertex, the tangency of the opposite side with the incircle, and the Gergonne point are collinear.
  • From any point on the circumcircle of a triangle, the nearest points on each of the three extended sides of the triangle are collinear in the Simson line of the point on the circumcircle.

Quadrilaterals

  • In a convex quadrilateral ABCD whose opposite sides intersect at E and F, the midpoints of AC, BD, and EF are collinear and the line through them is called the Newton line (sometimes known as the Newton-Gauss line). If the quadrilateral is a tangential quadrilateral, then its incenter also lies on this line.[2]
  • In a cyclic quadrilateral, the area centroid, the vertex centroid, and the intersection of the diagonals are collinear.[5]
  • In a tangential trapezoid, the tangencies of the incircle with the two bases and collinear with the incenter.
  • In a tangential trapezoid, the midpoints of the legs are collinear with the incenter.

Hexagons

  • Pascal's theorem (also known as the Hexagrammum Mysticum Theorem) states that if an arbitrary six points are chosen on a conic section (i.e., ellipse, parabola or hyperbola) and joined by line segments in any order to form a hexagon, then the three pairs of opposite sides of the hexagon (extended if necessary) meet in three points which lie on a straight line, called the Pascal line of the hexagon. The converse is also true: the Braikenridge–Maclaurin theorem states that if the three intersection points of the three pairs of lines through opposite sides of a hexagon lie on a line, then the six vertices of the hexagon lie on a conic, which may be degenerate as in Pappus's hexagon theorem.

Conic sections

  • By Monge's theorem, for any three circles in a plane, none of which is inside one of the others, the three intersection points of the three pairs of lines, each externally tangent to two of the circles, are collinear.
  • In an ellipse, the center, the two foci, and the two vertices with the smallest radius of curvature are collinear, and the center and the two vertices with the greatest radius of curvature are collinear.
  • In a hyperbola, the center, the two foci, and the two vertices are collinear.

Cones

  • The center of mass of a conic solid of uniform density lies one-quarter of the way from the center of the base to the vertex, on the straight line joining the two.

Tetrahedrons

Algebra

Collinearity of points whose coordinates are given

In coordinate geometry, in n-dimensional space a set of three or more distinct points are collinear if and only if, for every subset of three points X = (x1, x2, ... , xn), Y = (y1, y2, ... , yn), and Z = (z1, z2, ... , zn), the matrix

\begin{bmatrix}
1 & x_1 & x_2 & \dots & x_n  \\
1 & y_1 & y_2 & \dots & y_n \\
1 & z_1 & z_2 & \dots & z_n

\end{bmatrix} has rank less than 3. In particular, for three points in the plane (n = 2), the above matrix is square and the points are collinear if and only if its determinant is zero; since that 3 × 3 determinant is plus or minus twice the area of a triangle with those three points as vertices, this is equivalent to the statement that the three points are collinear if and only if the triangle with those points as vertices has zero area.

Collinearity of points whose pairwise distances are given

A set of at least three distinct points is called straight, meaning all the points are collinear, if and only if, for every three points A, B, and C, the following determinant of a Cayley–Menger determinant is zero (with d(AB) meaning the distance between A and B, etc.):

\det \begin{bmatrix}
      0 & d(AB)^2 & d(AC)^2 & 1 \\
d(AB)^2 &    0    & d(BC)^2 & 1 \\
d(AC)^2 & d(BC)^2 &       0 & 1 \\
      1 &       1 &       1 & 0

\end{bmatrix} = 0.

This determinant is, by Heron's formula, equal to −16 times the square of the area of a triangle with side lengths d(AB), d(BC), and d(AC); so checking if this determinant equals zero is equivalent to checking whether the triangle with vertices A, B, and C has zero area (so the vertices are collinear).

Equivalently, a set of at least three distinct points are collinear if and only if, for every three points A, B, and C with d(AC) greater than or equal to each of d(AB) and d(BC), the triangle inequality d(AC) ≤ d(AB) + d(BC) holds with equality.

Number theory

Two numbers m and n are not coprime—that is, they share a common factor other than 1—if and only if for a rectangle plotted on a square lattice with vertices at (0, 0), (m, 0), (m, n), and (0, n), at least one interior point is collinear with (0, 0) and (m, n).

Concurrency (plane dual)

In various plane geometries the notion of interchanging the roles of "points" and "lines" while preserving the relationship between them is called plane duality. Given a set of collinear points, by plane duality we obtain a set of lines all of which meet at a common point. The property that this set of lines has (meeting at a common point) is called concurrency, and the lines are said to be concurrent lines. Thus, concurrency is the plane dual notion to collinearity.

Collinearity graph

Given a partial geometry P, where two points determine at most one line, a collinearity graph of P is a graph whose vertices are the points of P, where two vertices are adjacent if and only if they determine a line in P.

Usage in statistics and econometrics

Main article: Multicollinearity

In statistics, collinearity refers to a linear relationship between two explanatory variables. Two variables are perfectly collinear if there is an exact linear relationship between the two, so the correlation between them is equal to 1 or −1. That is, X_{1} and X_{2} are perfectly collinear if there exist parameters \lambda_0 and \lambda_1 such that, for all observations i, we have

X_{2i} = \lambda_0 + \lambda_1 X_{1i}.

This means that if the various observations (X1i, X2i ) are plotted in the (X1, X2) plane, these points are collinear in the sense defined earlier in this article.

Perfect multicollinearity refers to a situation in which k (k ≥ 2) explanatory variables in a multiple regression model are perfectly linearly related, according to

X_{ki} = \lambda_0 + \lambda_1 X_{1i} + \lambda_2 X_{2i} + \dots + \lambda_{k-1} X_{(k-1),i}

for all observations i. In practice, we rarely face perfect multicollinearity in a data set. More commonly, the issue of multicollinearity arises when there is a "strong linear relationship" among two or more independent variables, meaning that

X_{ki} = \lambda_0 + \lambda_1 X_{1i} + \lambda_2 X_{2i} + \dots + \lambda_{k-1} X_{(k-1),i} + \varepsilon_i

where the variance of \varepsilon_i is relatively small.

The concept of lateral collinearity expands on this traditional view, and refers to collinearity between explanatory and criteria (i.e., explained) variables.[6]

Usage in other areas

Antenna arrays


In telecommunications, a collinear (or co-linear) antenna array is an array of dipole antennas mounted in such a manner that the corresponding elements of each antenna are parallel and aligned, that is they are located along a common line or axis.

Photography

The collinearity equations are a set of two equations, used in photogrammetry and remote sensing to relate coordinates in an image (sensor) plane (in two dimensions) to object coordinates (in three dimensions). In the photography setting, the equations are derived by considering the central projection of a point of the object through the optical centre of the camera to the image in the image (sensor) plane. The three points, object point, image point and optical centre, are always collinear. Another way to say this is that the line segments joining the object points with their image points are all concurrent at the optical centre.[7]

See also

Notes

References

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