Please answer this correctly
1 answer:
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<h3>Answer: Choice A) 252 in^2</h3>
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Explanation:
Refer to the diagram below.
The red rectangle is 6 inches by 12 inches, so its area is 6*12 = 72 square inches. There are two of these rectangles (one on top, one on the bottom). That accounts for 2*72 = 144 square inches so far.
The purple rectangles are 3 inches by 12 inches, yielding an area of 3*12 = 36 square inches each. That's another 2*36 = 72 square inches when we account for the front and back purple rectangles.
The green rectangles are 6 inches by 3 inches. Each green rectangle is 6*3 = 18 square inches. Having two of them means we'll add on 2*18 = 36 square inches.
Overall, the entire surface area is the sum of all the areas we calculated: 144+72+36 = 252 square inches, which is choice A
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Alternative Method:
- L = 12 = length
- W = 6 = width
- H = 3 = height
SA = surface area of the rectangular prism
SA = 2*(L*W + L*H + W*H)
SA = 2*(12*6 + 12*3 + 6*3)
SA = 2*(72 + 36 + 18)
SA = 2*(126)
SA = 252 in^2
So we get the same answer either way
Answer:
The upper 20% of the weighs are weights of at least X, which is , in which
is the standard deviation of all weights and
is the mean.
Step-by-step explanation:
Normal Probability Distribution:
Problems of normal distributions can be solved using the z-score formula.
In a set with mean and standard deviation
, the z-score of a measure X is given by:
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.
Upper 20% of weights:
The upper 20% of the weighs are weighs of at least X, which is found when Z has a p-value of 0.8. So X when Z = 0.84. Then
The upper 20% of the weighs are weights of at least X, which is , in which
is the standard deviation of all weights and
is the mean.