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3 years ago

Solve for x in the equation x^2-14+31=63

1 answer:

3 0

To solve for a variable, you need to get it (x) by itself.

x² - 14 + 31 = 63 Add 31 to -14
x² + 17 = 63 Subtract 17 from both sides of the equation
x² = 46 Square root both sides
x = $\pm \sqrt{46}$

Check your work by plugging in $\sqrt{46}$ for x and then $- \sqrt{46}$ .

$\sqrt{46}$ ² - 14 + 31 = 63 Square $\sqrt{46}$
46 - 14 + 31 = 63 Subtract 14 from 46
32 + 31 = 63 Add
63 = 63

$- \sqrt{46}$ ² - 14 + 31 = 63 Square $- \sqrt{46}$
46 - 14 + 31 = 63 Subtract 14 from 46
32 + 31 = 63 Add
63 = 63

So, x is $\sqrt{46}$ and $- \sqrt{46}$ .

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The slope of the graph of the function is equal to 0 for x between x = -3 and x = -2.
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<h3>How to complete the sentences?</h3>

By critically observing the graph shown in the image attached below, we can logically deduce that the slope of the graph of this function is equal to 0 for x, between x = -3 and x = -2.

Similarly, the slope of the graph of this function is also equal to 0 for x, between x = 3 and x = 4.

Based on the graph (see attachment), the greatest value of y is 4 while the smallest value of y is -3.

Read more on slope here: brainly.com/question/3493733

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Assume Y=1+X+u, where X, Y, and u=v+X are random variables, v is independent of X; E(v)=0, Var(v)=1, E(X)=1, and

kobusy [5.1K]

Answer:

a) $E(u|X=1)= E(v|X=1) + E(X|X=1) = E(v) +1 = 0 +1 =1+$

b) $E(Y| X=1)= E(1|X=1) + E(X|X=1) + E(u|X=1) = E(1) + 1 + E(v) + 0 = 1+1+0=2$

c) $E(u|X=2)= E(v|X=2) + E(X|X=2) = E(v) +2 = 0 +2 =2$

d) $E(Y| X=2)= E(1|X=2) + E(X|X=2) + E(u|X=2) = E(2) + 2 + E(v) + 2 = 2+2+2=6$

e) $E(u|X) = E(v+X |X) = E(v|X) +E(X|X) = E(v) +E(X) = 0+1=1$

f) $E(Y|X) = E(1+X+u |X) = E(1|X) +E(X|X) + E(u|X) = 1+1+1=3$

g) $E(u) = E(v) +E(X) = 0+1=1$

h) E(Y) = E(1+X+u) = E(1) + E(X) +E(v+X) = 1+1 + E(v) +E(X) = 1+1+0+1 = 3[/tex]

Step-by-step explanation:

For this case we know this:

$Y = 1+X +u$

$u = v+X$

with both Y and u random variables, we also know that:

$[tex] E(v) = 0, Var(v) =1, E(X) = 1, Var(X)=2$

And we want to calculate this:

Part a

$E(u|X=1)= E(v+X|X=1)$

Using properties for the conditional expected value we have this:

$E(u|X=1)= E(v|X=1) + E(X|X=1) = E(v) +1 = 0 +1 =1$

Because we assume that v and X are independent

Part b

$E(Y| X=1) = E(1+X+u|X=1)$

If we distribute the expected value we got:

$E(Y| X=1)= E(1|X=1) + E(X|X=1) + E(u|X=1) = E(1) + 1 + E(v) + 0 = 1+1+0=2$

Part c

$E(u|X=2)= E(v+X|X=2)$

Using properties for the conditional expected value we have this:

$E(u|X=2)= E(v|X=2) + E(X|X=2) = E(v) +2 = 0 +2 =2$

Because we assume that v and X are independent

Part d

$E(Y| X=2) = E(1+X+u|X=2)$

If we distribute the expected value we got:

$E(Y| X=2)= E(1|X=2) + E(X|X=2) + E(u|X=2) = E(2) + 2 + E(v) + 2 = 2+2+2=6$

Part e

$E(u|X) = E(v+X |X) = E(v|X) +E(X|X) = E(v) +E(X) = 0+1=1$

Part f

$E(Y|X) = E(1+X+u |X) = E(1|X) +E(X|X) + E(u|X) = 1+1+1=3$

Part g

$E(u) = E(v) +E(X) = 0+1=1$

Part h

E(Y) = E(1+X+u) = E(1) + E(X) +E(v+X) = 1+1 + E(v) +E(X) = 1+1+0+1 = 3[/tex]

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3 years ago