Answer:
7√3
Step-by-step explanation:
We can use pythagoreans theorem to solve this
Since, we know one side, and the hypotenuse, we can solve for the other side.
Pythagoreans theorem: a²+b²=c²
Where a and b are two sides, and c is the hypotenuse (the side opposite of the right angle)
In this triangle, 7 is the side, and 14 is the hypotenuse.
I will plug in the values into pythagoreans theorem, and then simplify:

So x = 7√3
Answer:

Step-by-step explanation:
Soh-cah-toa is a great way to remember:

You just remember the first letters : sine= opposite/hypotenuse , for example.
9514 1404 393
Answer:
see attached
Step-by-step explanation:
Polynomial long division is done the way any long division is done. Find a "partial quotient", subtract from the dividend the product of that partial quotient and the divisor. The result is a new dividend. Repeat until the degree of the dividend is less than that of the divisor.
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In the attached, the "Hints" show you how the partial quotient is found, and they show you how the product of the partial quotient and divisor is found.
The partial quotient term is simply the ratio of the highest degree terms of dividend and divisor. (Unlike numerical long division, there is no guessing.)
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The remainder is the dividend of lower degree than the divisor. As in numerical long division, the full quotient expresses the remainder over the divisor.
For example, 5 ÷ 3 = 1 r 2 = 1 + 2/3.
Your full quotient is (n+5) +1/(n-6).
Answer: Choice C
Amy is correct because a nonlinear association could increase along the whole data set, while being steeper in some parts than others. The scatterplot could be linear or nonlinear.
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Explanation:
Just because the data points trend upward (as you go from left to right), it does not mean the data is linearly associated.
Consider a parabola that goes uphill, or an exponential curve that does the same. Both are nonlinear. If we have points close to or on these nonlinear curves, then we consider the scatterplot to have nonlinear association.
Also, you could have points randomly scattered about that don't fit either of those two functions, or any elementary math function your teacher has discussed so far, and yet the points could trend upward. If the points are not close to the same straight line, then we don't have linear association.
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In short, if the points all fall on the same line or close to it, then we have linear association. Otherwise, we have nonlinear association of some kind.
Joseph's claim that an increasing trend is not enough evidence to conclude the scatterplot is linear or not.