For this problem, you kind of have to work backwards. The first step is figuring out what percentage of 900 is 225--this way we can find out what percentage we need to take from the sample of 48. In order to find the percentage:
225 / 900 = 0.25 or 25% < this is the percentage of students the administrator predicts will be in favor of the dress code. Now we can assume that this means 25% of the original 48 asked were in favor.
So now, we need to find 25% of 48:
48 x 0.25 = 12 < this is the number of students out of the original 48 who were in favor of the new dress code.
Answer: 12 students
Answer:
36 cm.
Step-by-step explanation:
Let, the side of the square = x cm.
As, side of the square is 3 cm less than and 2 cm more than the sides of the rectangle.
Thus, the sides of the rectangle will be (x+3) and (x-2)
<em>Also, it is given that, 'The area of the square is 30 cm² less than the area of the rectangle'.</em>
As, Area of the square =
Area of the rectangle =
Thus, we have
i.e.
i.e.
i.e.
Thus, the side of the square is 36 cm.
When distributing the negative three into the parentheses, that canceled out the -5 and made it a positive 15
Answer: <span>This is an example of correlation but not causation.
Explanation:
The statement "when more apples grew in the backyard, the pet cat stayed indoors for a longer time" is an excellent example to explain the difference between causation and correlation.
Is the very fact that the apples grew in the backyard what makes the pet cat stay indoors longer?
Sure, you know it isn't. Sure there is another cause that influence both the growing of apples and the time the pet cats stay indoor. So, there is not a causality relationship.
Given that some fact is influencing both phenomena, you find that they behave in a way that one permits predict the other, which is what correlation indicates, but not that one is the cause of the other.
When you know the cause you might change the final behavior, but when you know that the variables are correlated you just can use one to predict the other.
In this example, if you see that more apples grow in the backyard you can predict that the cat pets will stay indoors for a longer time, but you can do nothing to modify the time the pet cats stay indoors because you do not the reason why they are doing that.
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