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ipn [44]
3 years ago
5

Find symmetric equations for the line of intersection of the planes. z = 4x − y − 13, z = 6x + 5y − 13

Mathematics
1 answer:
MArishka [77]3 years ago
3 0
The intersection line of two planes is the cross product of the normal vectors of the two planes.

p1: z=4x-y-13 => 4x-y-z=13
p2: z=6x+5y-13 => 6x+5y-z=13
The corresponding normal vectors are:
n1=<4,-1,-1>
n2=<6,5,-1>

The direction vector of the intersection line is the cross product of the two normals, 
vl=
 i   j   k
4 -1 -1
6  5 -1
=<1+5, -6+4, 20+6>
=<6,-2,26>
We simplify the vector by reducing its length by half, i.e.
vl=<3,-1,13>

To find the equation of the line, we need to find a point on the intersection line.
Equate z:  4x-y-13=6x+5y-13 => 2x+6y=0 => x+3y=0.
If x=0, then y=0, z=-13 => line passes through (0,0,-13)

Proceed to find the equation of the line:
L: (0,0,-13)+t(3,-1,13)
Convert to symmetric form:
\frac{x-0}{3}=\frac{y-0}{-1}=\frac{z-(-13)}{13}
=>
\frac{x}{3}=\frac{-y}{1}=\frac{z+13}{13}

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Loosely, "varies inversely" means that as one quantity increases, the other decreases. Specifically, if x and y vary inversely, then y = k/x where k is some constant (a particular number).

Another way to write that relationship is xy = k.  To find the value of k for this problem, you need a numbers for x and y that go together.

Suppose x = number of children and y = number of candies (it doesn't matter what order these go in as long as you keep track!).

"When 12 children are present, 140 pieces of candy are consumed."  So when x = 12, the value of y = 140.

xy = k
(12)(140) = k
1680 = k

Now, when x = 8 children,

8y = 1680
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Let P = 0.50.30.50.7 be the transition matrix for a Markov chain with two states. Let x0 = 0.50.5 be the initial state vector fo
pav-90 [236]

Answer:

Probability distribution vector = \left(\begin{array}{c}0.375\\ 0.625 \end{array} \right)

Step-By-Step Explanation

If P=\left(\begin{array}{cc}0.5&0.3\\ 0.5&0.7 \end{array} \right)  is the transition matrix for a Markov chain with two states.  

x_{0}=\left(\begin{array}{c}0.5\\ 0.5 \end{array} \right)  be the initial state vector for the population.

X_{1}=P x_{0}=\left(\begin{array}{cc}0.5&0.3\\ 0.5&0.7 \end{array} \right) \left(\begin{array}{c}0.5\\ 0.5 \end{array} \right) =\left(\begin{array}{c}0.4\\ 0.6 \end{array} \right)  

X_{2}=P^{2} x_{0}=\left(\begin{array}{c}0.38\\ 0.62 \end{array} \right)  

X_{3}=P^{3} x_{0}=\left(\begin{array}{c}0.38\\ 0.62 \end{array} \right)  

X_{30}=P^{30} x_{0}=\left(\begin{array}{c}0.37499\\ 0.625 \end{array} \right)  

In the long run, the probability distribution vector Xm approaches the probability distribution vector \left(\begin{array}{c}0.375\\ 0.625 \end{array} \right) .

This is called the steady-state (or limiting,) distribution vector.

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