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The type of polynomial that would best model the data is a <em>cubic</em> polynomial. (Correct choice: D)
<h3>What kind of polynomial does fit best to a set of points?</h3>
In this question we must find a kind of polynomial whose form offers the <em>best</em> approximation to the <em>point</em> set, that is, the least polynomial whose mean square error is reasonable.
In a graphing tool we notice that the <em>least</em> polynomial must be a <em>cubic</em> polynomial, as there is no enough symmetry between (10, 9.37) and (14, 8.79), and the points (6, 3.88), (8, 6.48) and (10, 9.37) exhibits a <em>pseudo-linear</em> behavior.
The type of polynomial that would best model the data is a <em>cubic</em> polynomial. (Correct choice: D)
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Applying the angle addition postulate, the measure of angle RST is: 66°.
<h3>What is the Angle Addition Postulate?</h3>If two angles share a common vertex and a common side, they are adjacent angles that form a larger angle. According to the angle addition postulate, the sum of these two adjacent angles will give a sum that is equal to the measure of the larger angle they both form.
We know the following:
Measure of angle RSU = 43º
Measure of angle UST = 23º
In the diagram given, angle RSU and angle UST are adjacent angles that form a larger angle, angle RST.
Therefore, based on the angle addition postulate, the measure of angle RST = sum of the measures of angles RSU and UST.
Therefore, we would have:
m∠RST = m∠RSU + m∠UST
Substitute
m∠RST = 43 + 23
m∠RST = 66°
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