Answer:
Step-by-step explanation:
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.
The answer would be 54 students liked dogs the best.
1 pound = 16 ounces.
Divide total ounces by 16:
84 / 16 = 5.25 pounds = 5 pounds and 4 ounces.
Answer: 390,000
Step-by-step explanation:
