The data for resort A shows more consistency because a larger interquartile range such as the one for resort B, shows more variation. This means that the snowfall for resort A is more likely to be close to the median.
Just did this on edg. :)
Answer:
l=0.1401P\\
w =0.2801P
where P = perimeter
Step-by-step explanation:
Given that a window is in the form of a rectangle surmounted by a semicircle.
Perimeter of window =2l+\pid/2+w

Or 
To allow maximum light we must have maximum area
Area = area of rectangle + area of semi circle where rectangle width = diameter of semi circle


Hence we get maximum area when i derivative is 0
i.e. 

Dimensions can be

The measures of the angles don't change when you translate a figure, because the entire figure is moving as a whole. Imagine having a paper parallelogram, moving it around and flipping it over. Not even dilations would change these angles (for reasons that can be pretty easily visualed but not really proven until geometry)
Forty-thousand, five-hundred eighty-three.
Answer:
a) P(x=3)=0.089
b) P(x≥3)=0.938
c) 1.5 arrivals
Step-by-step explanation:
Let t be the time (in hours), then random variable X is the number of people arriving for treatment at an emergency room.
The variable X is modeled by a Poisson process with a rate parameter of λ=6.
The probability of exactly k arrivals in a particular hour can be written as:

a) The probability that exactly 3 arrivals occur during a particular hour is:

b) The probability that <em>at least</em> 3 people arrive during a particular hour is:
![P(x\geq3)=1-[P(x=0)+P(x=1)+P(x=2)]\\\\\\P(0)=6^{0} \cdot e^{-6}/0!=1*0.0025/1=0.002\\\\P(1)=6^{1} \cdot e^{-6}/1!=6*0.0025/1=0.015\\\\P(2)=6^{2} \cdot e^{-6}/2!=36*0.0025/2=0.045\\\\\\P(x\geq3)=1-[0.002+0.015+0.045]=1-0.062=0.938](https://tex.z-dn.net/?f=P%28x%5Cgeq3%29%3D1-%5BP%28x%3D0%29%2BP%28x%3D1%29%2BP%28x%3D2%29%5D%5C%5C%5C%5C%5C%5CP%280%29%3D6%5E%7B0%7D%20%5Ccdot%20e%5E%7B-6%7D%2F0%21%3D1%2A0.0025%2F1%3D0.002%5C%5C%5C%5CP%281%29%3D6%5E%7B1%7D%20%5Ccdot%20e%5E%7B-6%7D%2F1%21%3D6%2A0.0025%2F1%3D0.015%5C%5C%5C%5CP%282%29%3D6%5E%7B2%7D%20%5Ccdot%20e%5E%7B-6%7D%2F2%21%3D36%2A0.0025%2F2%3D0.045%5C%5C%5C%5C%5C%5CP%28x%5Cgeq3%29%3D1-%5B0.002%2B0.015%2B0.045%5D%3D1-0.062%3D0.938)
c) In this case, t=0.25, so we recalculate the parameter as:

The expected value for a Poisson distribution is equal to its parameter λ, so in this case we expect 1.5 arrivals in a period of 15 minutes.
