Answer:
A
Step-by-step explanation:
We have that
<span>Circle 1: center (8, 5) and radius 6
</span><span>Circle 2: center (−2, 1) and radius 2
we know that
the equation of a circle is
(x-h)</span>²+(y-k)²=r²
for the circle 1---------> (x-8)²+(y-5)²=36
for the circle 2---------> (x+2)²+(y-1)²=4
using a graph tool
see the attached figure
Part A)<span>What transformations can be applied to Circle 1 to prove that the circles are similar?
we know that
r1/r2---------> 6/2------> 3
</span><span>
to prove that the circle 1 and circle 2 are similar, the radius of circle 1 </span>must be divided by 3 and translate the center of the circle 1 (10) units to the left and (4) units down
<span>
the answer part A) is
</span>
the radius of circle 1 must be divided by 3 and translate the center of the circle 1 (10) units to the left and (4) units down
Part B) <span>What scale factor does the dilation from Circle 1 to Circle 2 have?
the answer Part B) is
the scale factor is (3/1)</span>
Im not sure how to explain how to do a graph, but I hope this helps at least a bit!
~Direct Variation When two variables are related in such a way that the ratio of their values always remains the same, the two variables are said to be in direct variation. In simpler terms, that means if A is always twice as much as B, then they directly vary. If a gallon of milk costs $3, and I buy 1 gallon, the total cost is $3.~
Simplifying
-16 + 23 (-4) + -3
Multiply 23 x -4
Add -16 + -92=-108
Add -108-3= -111 <------Answer
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.