Answer:
Krutika
Step-by-step explanation:
Lets convert each person's typing rate to words/min.
Krutika:
80 minutes = 6000 words
1 minute = (6000/80) words
= 75 words
Typing Rate: 75 words / min
Mark:
60 minutes = 4200 words
1 minute = (4200/60) words
= 70 words
Typing Rate: 70 words / min
David:
90 minutes = 5850 words
1 minute = (5850/90) words
= 65 words
Typing Rate: 65 words / min
From the above, we can see Krutika has the fastest rate of words per minute.
Answer:
A.
Step-by-step explanation:
So we are given the function:

To find the inverse of the function, we simply need to flip <em>f(x)</em> and <em>x</em> and then solve for <em>f(x)</em>. Thus:

So the answer is A.
22: 3 sticks of butter divided amongst 4 people = ¾ sticks/person
23: There would have been 3 people in the group.
24: 5 divided by 6 because the 5 is the numerator, no she is not correct.
Hope this helps, and have a great Friday!
Answer:
And we can find this probability with the complement rule and using the normal standard distributon table or excel we got:
And if we convert this to a % we got 18.2 % of maximum temperatures higher or equal than 32 C
Step-by-step explanation:
Previous concepts
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".
Solution to the problem
Let X the random variable that represent the maximum monthly temperature of a population, and for this case we know the distribution for X is given by:
Where
and
We are interested on this probability
And the best way to solve this problem is using the normal standard distribution and the z score given by:
If we apply this formula to our probability we got this:
And we can find this probability with the complement rule and using the normal standard distributon table or excel we got:
And if we convert this to a % we got 18.2 % of maximum temperatures higher or equal than 32 C