No it is not. It's possible to have to have a set of vectors that are linearly dependent but still Span R^3. Same holds true for reverse. Linear Independence does not guarantee Span R^3. IF both conditions are met then that set of vectors is called the Basis for R^3. So, for a set of vectors, S, to be a Basis it must be:
(1) Linearly Independent
(2) Span S = R^3.
This means that both conditions are independent.
(i) They are linearly dependent since the 2nd vector is twice the 1st vector. All 3 vectors lie in the x-z plane, so they don't span 3D space. (ii) They are linearly independent. Note that the cross-product of the first two is (-1,1,1). If the third vector is not perpendicular to the above cross-product, then the third vector does not lie in the plane defined by the first two vectors. (-1,1,1) "dot" (1,1,-1) = -1+1-1 = -1, not zero, so 3rd vector is not perpendicular to the cross product of the other two.
the set of possible values of the independent variable or variables of a function.
The data set must be unbiased, the outcomes of the trials leading to the data set must be independent. The data set must be large enough to allow the Law of Large Numbers to be effective.
Assume that the table shows a set of values for two variables that are linearly related. If these assumptions are false, you cannot do answer the question.Suppose the first set of values is for a variable, x, and the second set is for y.Select any two [ordered] pairs data: (x1, y1) and (x2, y2). Then the slope between these two points is (y2 - y1)/(x2 - x1) provided x2 ≠ x1. If x2 = x1 then the slope is undefined.
A set of variables or equations are said to be independent if no one of them can be expressed in terms of the others. In statistics, a variable, X, is said to be independent of another variable, Y, if changes in Y do not cause changes in X. The reverse need not be true.
To find a basis for a vector space, you need to find a set of linearly independent vectors that span the entire space. One approach is to start with the given vectors and use techniques like Gaussian elimination or solving systems of linear equations to determine which vectors are linearly independent. Repeating this process until you have enough linearly independent vectors will give you a basis for the vector space.
An independent system of linear equations is a set of vectors in Rm, where any other vector in Rm can be written as a linear combination of all of the vectors in the set. The vector equation and the matrix equation can only have the trivial solution (x=0).
(i) They are linearly dependent since the 2nd vector is twice the 1st vector. All 3 vectors lie in the x-z plane, so they don't span 3D space. (ii) They are linearly independent. Note that the cross-product of the first two is (-1,1,1). If the third vector is not perpendicular to the above cross-product, then the third vector does not lie in the plane defined by the first two vectors. (-1,1,1) "dot" (1,1,-1) = -1+1-1 = -1, not zero, so 3rd vector is not perpendicular to the cross product of the other two.
When an eigenvalue of a matrix is equal to 0, it signifies that the matrix is singular, meaning it does not have a full set of linearly independent eigenvectors.
No, weight and displacement is not a set of vectors. A vector in the area of mathematics is defined as a direction as well as a magnitude of a specific item. Vectors can be labeled in a variety of ways.
Given one vector a, any vector that satisfies a.b=0 is orthogonal to it. That is a set of vectors defining a plane orthogonal to the original vector.The set of vectors defines a plane to which the original vector a is the 'normal'.
The single vector which would have the same effect as all of them together
When you resolve a vector, you replace it with two component vectors, usually at right angles to each other. The resultant is a single vector which has the same effect as a set of vectors. In a sense, resolution and resultant are like opposites.
A vector plane is a two-dimensional space defined by a set of two non-parallel vectors. It represents all linear combinations of these vectors. In linear algebra, vector planes are used to visualize and understand relationships between vectors in space.
yes it is
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The direction of a vector is defined in terms of its components along a set of orthogonal vectors (the coordinate axes).