Use counting to determine the whole number that corresponds to the cardinality of these sets:. (a) A= (xl x € Nand 20.< x<27) (b) B=(xixeNandx+1=x)
(c) C={xl xe Nand(x- I)(x - 9) = 0)
(d) D={xlx E N, H X 5 100, and xis divisible by both 5 and 8)

Step-by-step explanation:

Cardinality of a set is defined as number of element in a set. It is represented as n(X) where X is any set.

a) Given the set A =(x l x € N and 20.< x<27). According to the set, the set contains the values of natural numbers between 20 and 27. The values are 21, 22, 23, 24, 25 and 26

A = (21, 22, 23, 24, 25, 26)

According to the set A, it can be seen that there are 6 elements in the set, this means n(A) = 6.

b) Given B=(x | xeN and x+1=x)

Since natural numbers starts from 1, the first element in the set is 2 i.e 1+1

The elements of the set B = (2, 3, 4, 4...)

The number of whole number in the set is therefore infinite.

c) For the set C={x l xe N and (x- 1)(x - 9) = 0)

We need to get the root of the equation  (x- 1)(x - 9) = 0

x-1 = 0 and x-9 = 0

x = 1 and x = 9

Hence the element C = (1,9)

The number of whole number in the set is n(C) = 2

d)  D={xlx E N, H X 5 100, and x is divisible by both 5 and 8)

Given the value of x between 5 and 100, the values of x divisible by 5 = (10, 15, 20, 25, 30, 35, 40, 45, 50 , 55, 60 , 65, 70, 75, 80 85, 90, 95)

values of x divisible by 8 = (16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96)

The total number of elements in both set = 29 i.e n(D) = 29

i think it is x = 1.2

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! )