4. Determine which of the following are equivalence relations and/or partial CHAPTER 6. RELATIONS 121 ordering relations for the given sets: (a) A = { lines in the plane}, and r defined by xry if and only if x is parallel to y. Assume every line is parallel to itself. (b) A = R and r defined by xry if and only if |x − y| ≤ 7.

Determine which of the following are equivalence relations and/or partial CHAPTER 6. RELATIONS 121 ordering relations for the given sets: (a) A = { lines in the plane}, and r defined by xry if and only if x is parallel to y. Assume every line is parallel to itself. (b) A = R and r defined by xry if and only if |x − y| ≤ 7.

A is an Equivalence Relation.

Step-by-step explanation:

For a reflexive case : x is parallel to itself => x R x

For a symmetric : x is parallel to y => y is parallel to x.

Thus, x R y = y R x

For a transitive:  x is parallel to y, and y is parallel to z then x, y, z are parallel to each other.

=> x R y and y R z = x R z

Therefore, A is an Equivalence Relation.

0.5 meters per second

step-by-step explanation:

the average speed between 2 seconds and 8 seconds is 0.5 meters per second.

hope this ! mark brainliest!

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! )