Explanation:
Let angle y be the unknown angle inside the triangle.
given
y + 87 + 34 = 180 (sum of angles in a triangle)
![y + 87 + 34 = 180 \\ y + 121 = 180 \\ y = 180 - 121 \\ = 59](https://tex.z-dn.net/?f=y%20%2B%2087%20%2B%2034%20%3D%20180%20%5C%5C%20y%20%2B%20121%20%3D%20180%20%5C%5C%20y%20%3D%20180%20-%20121%20%5C%5C%20%20%3D%2059)
given y + z = 180 (angles on same straight line)
![y + z = 180 \\ 59 + z = 180 \\ z = 180 - 59 \\ = 121](https://tex.z-dn.net/?f=y%20%2B%20z%20%3D%20180%20%5C%5C%2059%20%2B%20z%20%3D%20180%20%5C%5C%20z%20%3D%20180%20-%2059%20%5C%5C%20%20%3D%20121)
Because he divided the population into smaller groups and then randomly sampled each group, he would be using a stratified random sampling procedure.
Answer:
Break even point in dollar sales = $1,050,000
Explanation:
Break Even Point in dollar sales = Fixed Cost/ Contribution margin percentage
Contribution margin percentage = (Contribution margin/ Sales) X 100
Here we have for the year 2017
Contribution margin = $194,750
Sales = $779,000
Contribution margin percentage = ($194,750/$779,000) X 100 = 25%
Break even point in dollar sales = Fixed Cost $262,500/25%
= $1,050,000
Answer: mean monthly income = $5000
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Explanation
In any normal distribution, the median and mean are the same value.
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The proof is as follows:
If mean > median was the case, then the distribution would be skewed to the right (ie positively skewed). The right tail is pulled longer than the left tail. But this would contradict the symmetrical nature of the normal distribution. So mean > median must not be the case.
If mean < median, then the distribution would be skewed to the left (negatively skewed). Visually this pulls the left tail longer than the right tail. Like in the previous paragraph, this contradicts the symmetrical nature of the normal distribution. So mean < median must not be the case.
Since mean > median cannot be true, and neither can mean < median, this must indicate mean = median.
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So in short, any symmetrical distribution always has mean = median and they are at the very center of the distribution.