X y matrix is the tool for Six sigma used mostly during the DMAIC measure phase. If you want to learn much about it then you need to ask an expert who is available in six sigma training
If X and Y are independent Gaussian random variables with mean 0 and standard deviation sigma, then sqrt(X^2 + Y^2) has a Rayleigh distribution with parameter sigma.
y^2 X y^3 = y^(2 + 3) = y^5 You can only do this if the coefficient 'y' is the same for both terms. Remember y^2 = y X y y^3 = y X y X y Hence y^2 X y^3 = y X y X y X y X y = y^5 Similarly for division/subtraction y^3 / y^2 = y^(3 - 2 ) = y^1 = y The power of '1' is trivial and not normally shown. NB You CANNOT do z^2 X y^3 by adding the indices. z^2 X y^3 is (z^2)*(y^3)
The joint probability function for two variables is a probability function whose domain is a subset of two dimensional space. The joint probability function for discrete random variables X and Y is given aspr(x, y) = pr(X = x and Y = y). If X and Y are independent random variables then this will equal pr(X =x)*pr(Y = y).For continuous variables, the joint funtion is defined analogously:f(x, y) = pr(X < x and Y < y).
Let X and Y be two random variables.Case (1) - Discrete CaseIf P(X = x) denotes the probability that the random variable X takes the value x, then the joint probability of X and Y is P(X = x and Y = y).Case (2) - Continuous CaseIf P(a < X < b) is the probability of the random variable X taking a value in the real interval (a, b), then the joint probability of X and Y is P(a < X< b and c < Y < d).Basically joint probability is the probability of two events happening (or not).
MX,Y(s,t) = exp{μxs + μYt + ½(σX2s2 + 2ρσXσYst + σY2t2)} Where X ~ N (μx , σX) and Y ~ N (μY , σY). Also Corr(X,Y) = Cov (X,Y)/{Var(X) . Var(Y)} = ρ
It is a matrix with 1 row and two columns: something like (x, y).
Store the first value in the matrix, then compare every value in the matrix with the stored value, replacing the stored value if the current value is smaller. For instance, the following snippet will locate the smallest int value in a 3x4 matrix named A: int smallest=A[0][0]; for(int x=0; x<3; ++x ) { for(int y=0; y<4; ++y ) { if(A[x][y]<smallest ) { smallest=A[x][y]; } } }
No, a 2x2 matrix with (x) on the top row and (y) on the bottom is represented as (\begin{pmatrix} x_1 & x_2 \ y_1 & y_2 \end{pmatrix}), which contains four elements. In contrast, a 2x1 matrix with only (x) and (y) is represented as (\begin{pmatrix} x \ y \end{pmatrix}), containing only two elements. Therefore, they are not equal as they represent different dimensions and structures.
If X and Y are independent Gaussian random variables with mean 0 and standard deviation sigma, then sqrt(X^2 + Y^2) has a Rayleigh distribution with parameter sigma.
Algorithm: transpose Input: a matrix M[x][y] Output: the transpose of M (a matrix of order y * x) allocate N[y][x] for r = 0 to x-1 // iterate over rows for c = 0 to y-1 // iterate over columns N[c][r] = M[r][c] next c next r return N
Consider the linear system of equations AX = YwhereX is a n x 1 matrix of variables,Y is a n x 1 matrix of constants, andA is an n x n matrix of coefficients.Provided A is not a singular matrix, A has an inverse, A-1, an n x n matrix.Premultiplying by A-1 gives A-1AX = A-1Y or X = A-1Y, the solution to the linear system.
The rotation matrix for a 90° rotation is [[0,-1],[1,0]] More simply, if our shape is a function of x and y, we swap x and y and then negate y. For instance: if our function is y=x, then the 90° rotation would be x=-y
An incidence matrix is a mathematical matrix showing a relationship between two different types of objects. The first class can be written as X and the second as Y with one row for each element of X and one column for each element of Y. The entry in row x and column 1 should be 1 if x and y are related to one another. The entry in row x and column 1 should be 0 if they are not related to each other,
It is a singular matrix.
It is a singular matrix.
x + y = 10 x - y = 6 2x = 16 x = 8, y = 2
3x + 2 = 82