the answer is 13/7 and here is the correct question
the answer is 13/7 and here is the correct questionwhen a customer wants pie for dessert you cut a whole pies into 7 equal slices...at the end of your shift 3/7 of. a cherry pie.2/7. of an apple pie.3/7 of a peach pie.and 5/7 of a blueberry pie remain...How much pie remains as a fraction of. a whole pie...
Answer:
13/7
Step-by-step explanation:
From the question, we have
3/7 of cherry pie
2/7 of apple pie
3/7 of peach pie
5/7 of blueberry pie
Now we have to add up all of these in order to get the total amount of pie
3/7 + 2/7 +3/7 +5/7
= (3+2+3+5)/7
=13/7
If expressed as a mixed fraction
= 1 6/7
In conclusion, 13/7 pie remains as a fraction of a whole.
the adjacent angles in a parallelogram
Answer:
(x -5)² + (y +4)² = 100 Should be the correct answer, hope this helps :)
Step-by-step explanation:
A circle centered at (h, k) with radius r will have equation ...
... (x -h)² + (y-k)² = r²
The point satisfies the equation for the circle. Filling in the given numbers, we have ...
... (x -5)² + (y+4)² = (-3-5)² + (2+4)² . . . . . . (h, k) = (5, -4), (x, y) = (-3, 2)
... (x -5)² + (y +4)² = 64 +36
Let
![X](https://tex.z-dn.net/?f=X)
denote the random variable for the weight of a swan. Then each swan in the sample of 36 selected by the farmer can be assigned a weight denoted by
![X_1,\ldots,X_{36}](https://tex.z-dn.net/?f=X_1%2C%5Cldots%2CX_%7B36%7D)
, each independently and identically distributed with distribution
![X_i\sim\mathcal N(26,7.2)](https://tex.z-dn.net/?f=X_i%5Csim%5Cmathcal%20N%2826%2C7.2%29)
.
You want to find
![\mathbb P(X_1+\cdots+X_{36}>1000)=\mathbb P\left(\displaystyle\sum_{i=1}^{36}X_i>1000\right)](https://tex.z-dn.net/?f=%5Cmathbb%20P%28X_1%2B%5Ccdots%2BX_%7B36%7D%3E1000%29%3D%5Cmathbb%20P%5Cleft%28%5Cdisplaystyle%5Csum_%7Bi%3D1%7D%5E%7B36%7DX_i%3E1000%5Cright%29)
Note that the left side is 36 times the average of the weights of the swans in the sample, i.e. the probability above is equivalent to
![\mathbb P\left(36\displaystyle\sum_{i=1}^{36}\frac{X_i}{36}>1000\right)=\mathbb P\left(\overline X>\dfrac{1000}{36}\right)](https://tex.z-dn.net/?f=%5Cmathbb%20P%5Cleft%2836%5Cdisplaystyle%5Csum_%7Bi%3D1%7D%5E%7B36%7D%5Cfrac%7BX_i%7D%7B36%7D%3E1000%5Cright%29%3D%5Cmathbb%20P%5Cleft%28%5Coverline%20X%3E%5Cdfrac%7B1000%7D%7B36%7D%5Cright%29)
Recall that if
![X\sim\mathcal N(\mu,\sigma)](https://tex.z-dn.net/?f=X%5Csim%5Cmathcal%20N%28%5Cmu%2C%5Csigma%29)
, then the sampling distribution
![\overline X=\displaystyle\sum_{i=1}^n\frac{X_i}n\sim\mathcal N\left(\mu,\dfrac\sigma{\sqrt n}\right)](https://tex.z-dn.net/?f=%5Coverline%20X%3D%5Cdisplaystyle%5Csum_%7Bi%3D1%7D%5En%5Cfrac%7BX_i%7Dn%5Csim%5Cmathcal%20N%5Cleft%28%5Cmu%2C%5Cdfrac%5Csigma%7B%5Csqrt%20n%7D%5Cright%29)
with
![n](https://tex.z-dn.net/?f=n)
being the size of the sample.
Transforming to the standard normal distribution, you have
![Z=\dfrac{\overline X-\mu_{\overline X}}{\sigma_{\overline X}}=\sqrt n\dfrac{\overline X-\mu}{\sigma}](https://tex.z-dn.net/?f=Z%3D%5Cdfrac%7B%5Coverline%20X-%5Cmu_%7B%5Coverline%20X%7D%7D%7B%5Csigma_%7B%5Coverline%20X%7D%7D%3D%5Csqrt%20n%5Cdfrac%7B%5Coverline%20X-%5Cmu%7D%7B%5Csigma%7D)
so that in this case,
![Z=6\dfrac{\overline X-26}{7.2}](https://tex.z-dn.net/?f=Z%3D6%5Cdfrac%7B%5Coverline%20X-26%7D%7B7.2%7D)
and the probability is equivalent to
![\mathbb P\left(\overline X>\dfrac{1000}{36}\right)=\mathbb P\left(6\dfrac{\overline X-26}{7.2}>6\dfrac{\frac{1000}{36}-26}{7.2}\right)](https://tex.z-dn.net/?f=%5Cmathbb%20P%5Cleft%28%5Coverline%20X%3E%5Cdfrac%7B1000%7D%7B36%7D%5Cright%29%3D%5Cmathbb%20P%5Cleft%286%5Cdfrac%7B%5Coverline%20X-26%7D%7B7.2%7D%3E6%5Cdfrac%7B%5Cfrac%7B1000%7D%7B36%7D-26%7D%7B7.2%7D%5Cright%29)
Como solo division?? Porque es 17