9 days ago, the mold covered an area of
.
Today (after 9 days), the mold covers an area of
.
<em>What is the increase in area?</em> It went from 3 to 9. So the increase is
.
Now, we need to find <em>rate of change</em>. It means to find how much the area increased every day over the past 9 days. We simply divide <em>the increase (6)</em> by the <em>total number of days (9)</em> to find the rate per day. So we have,

ANSWER: Change of Rate =
per day
Answer:
2.30 years
Step-by-step explanation:
The number of fish tripled in the first year, making a total of 240 * 3 = 720 fishes.
(a) The formula for logistic equation is as the following

where P0 = 240 is the number of fishes initially, we can plug in P = 720 and t = 1 to calculate the constant k



b) Using the following formula

with P = 3000, P0 = 240, k = 1.1, we can calculate the number of years it takes to get to 3000 fishes




Answer:
The overview of the given problem is outlined in the following segment on the explanation.
Step-by-step explanation:
The proportion of slots or positions that have been missed due to numerous concurrent transmission incidents can be estimated as follows:
Checking a probability of transmitting becomes "p".
After considering two or even more attempts, we get
Slot fraction wasted,
= ![[1-no \ attempt \ probability-first \ attempt \ probability-second \ attempt \ probability+...]](https://tex.z-dn.net/?f=%5B1-no%20%5C%20attempt%20%5C%20probability-first%20%5C%20attempt%20%5C%20probability-second%20%5C%20attempt%20%5C%20probability%2B...%5D)
On putting the values, we get
= ![1-no \ attempt \ probability-[N\times P\times probability \ of \ attempts]](https://tex.z-dn.net/?f=1-no%20%5C%20attempt%20%5C%20probability-%5BN%5Ctimes%20P%5Ctimes%20probability%20%5C%20of%20%5C%20attempts%5D)
= ![1-(1-P)^{N}-N[P(1-P)^{N}]](https://tex.z-dn.net/?f=1-%281-P%29%5E%7BN%7D-N%5BP%281-P%29%5E%7BN%7D%5D)
So that the above seems to be the right answer.
<span>Randomly generate an integer from 1 to 7 two times, and the probability is 1/7 ^2
This is the </span><span>statement that best describes the use of a simulation to predict the probability that two randomly chosen people will both have their birthdays on a Monday.
There are 7 days in a week, so there are 7 choices but only 1 Monday. So, 1/7 is the probability that a person's birthday falls on a Monday.
1st person asked will have 1/7 probability.
2nd person asked will also have 1/7 probability
So, (1/7)</span>² is the probability that both persons will have their birthdays on a Monday.