Answer:
Step-by-step explanation:
Assuming this is in degrees, it is actually 0.
Answer:
4% change
Step-by-step explanation:
6 d x 24<span> hr = 144 hr 144 hr </span>x 60 min<span> = 8640 </span>min<span> 16 hr </span>x 60 min<span> = 960 </span>min<span> 8640 </span>min<span> + 960 </span>min<span> + 34 </span>min<span>= 9634 </span>min<span>. In the first </span>conversion<span>, we used </span>24 hours<span> because there are </span>24 hours<span> in a ... </span>Convert<span> 4 gallon 3 quarts </span>1 pints<span> into </span>pints<span>. ... For example, larger to smaller units still </span>multiply<span> and smaller to larger units still </span>divide<span>.</span>
Answer:
![E(X)= n \int_{0}^1 x^n dx = n [\frac{1}{n+1}- \frac{0}{n+1}]=\frac{n}{n+1}](https://tex.z-dn.net/?f=E%28X%29%3D%20n%20%5Cint_%7B0%7D%5E1%20x%5En%20dx%20%3D%20n%20%5B%5Cfrac%7B1%7D%7Bn%2B1%7D-%20%5Cfrac%7B0%7D%7Bn%2B1%7D%5D%3D%5Cfrac%7Bn%7D%7Bn%2B1%7D)
Step-by-step explanation:
A uniform distribution, "sometimes also known as a rectangular distribution, is a distribution that has constant probability".
We need to take in count that our random variable just take values between 0 and 1 since is uniform distribution (0,1). The maximum of the finite set of elements in (0,1) needs to be present in (0,1).
If we select a value 
we want this:
And we can express this like that:
for each possible i
We assume that the random variable 
are independent and 
from the definition of an uniform random variable between 0 and 1. So we can find the cumulative distribution like this:
And then cumulative distribution would be expressed like this:
For each value 
we can find the dendity function like this:
So then we have the pdf defined, and given by:
and 0 for other case
And now we can find the expected value for the random variable X like this:
Answer:
Use graph paper to create a scatter plot. Draw the x- and y- axes, ensure they intersect and label the origin. Ensure that the x- and y- axes also have correct titles. Next, plot each data point within the graph. Any trends between the plotted data sets should now be evident.
