Mathematics, 15.02.2021 17:30 studybug6170

Mary wants to buy a small fence place around us semi circular garden demeanor of a semi circle is 18 feet Mary calculus that she needs of about 28.3 feet of fence use a drop down menu to explain whether or not Mary's reasoning is correct use 3.4 for pie

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Answer from: zoseta

Diameter of a semi circular garden = 18 feet

Radius of the garden :

$= \tt\frac{diameter}{2}$

$=\tt \frac{18}{2}$

$=\tt 9 \: feet$

Thus, the radius of the semi circular fence = 9 feet

We know that :

$\color{hotpink}\tt \: Circumference \: of \:a \: semi \: circle \color{plum}=\pi r$

Which means, the circumference of Mary's semi circular garden :

$=\tt \pi r$

(pi value = 3.14)

$= \tt3.14 \times 9$

$=\tt 28.26 \: \: feet$

Thus, the circumference of fence needed for the semi circular garden = 28.26 feet

28.26 can be rounded off to 28.30 and 28.30 is the same as 28.3

Thus, Mary's reasoning is correct.

Therefore, Mary's reasoning is correct which means she needs 28.3 feet of fence to surround her garden.

Answer from: Quest

i leave the calculator work to you.

7. diameter 4 miles, circumference 4π miles

8. radius 0.2 miles, circumference 0.4π miles

[this is a bad problem because the actual radius of a car on the track is less than 0.2 miles so the answer is less than 0.4π miles]

9. radius 6 feet, circumference 12π feet

10. diameter 14 cm, circumference 14π cm

11. diameter 4.8 feet, circumference 4.8π feet

Ineed on questions 7-11 on this page i’m trying to get this done before i leave to go to class at 1

Answer from: Quest

the answer is 200

step-by-step explanation:

this is a problem about poisson probabilities. i don't know if you've

already studied poisson distributions, or if you have a place to look

to read more about the subject.

here's how you can see a poisson distribution coming. when there are

a lot of events that may happen at any time (or any place), and when

you're counting how many of them happen at a particular time (or a

particular place), and each event is independent of the others, then

the result is a poisson distribution. this is roughly true for the

chocolate chips; one chip being in a cookie has only a small effect on

the probability that another chip will be in the cookie.

once you know you have a poisson distribution, you know a lot. there

is only one parameter to a poisson distribution, so if you know the

mean, you know everything. the form of the distribution is

probability of n chips = {[e^(-x)]*(x^n)} / (n! )

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