Sydney’s teacher says she may have her report card percentage based on either the mean or the median and she can drop one test score if she chooses.

94%, 84%, 100%, 68%

which measure of center should sydney choose so she can have the highest percentage possible?

answer choice
a. sydney should choose the mean after dropping one test score.
b. sydney should choose the mean without dropping one test score.
c. sydney should choose the median after dropping one test score.
d. sydney should choose the median without dropping one test score.

Step-by-step explanation:

Given are 4 scores as

94, 84,100,68

Minimum: 68

Quartile Q1: 76

Median: 89

Quartile Q3: 97

IQR =97-76 =21

Maximum: 100

Outlier = median ±1.5 IQR

=(89-31.5,89+31.5)

=(67.5,120.5)

/since all entries lie within this there is no outlier

No need to drop one test score

Mean =86.5

But to get high score 94,84,100 can be considered

Mean = 92.67%

Median =94%

Hence C.Sydney should choose the median after dropping one test score.

3. parallelogram

step-by-step explanation:

we turn each of our sides into direction vectors

ab=b-a=(3 - 7, 1 - 0) = (-4, 1)

bc=c-b=(1,6)

cd=d-c=(4,-1)

da=a-d=(-1,-6)

we see how the opposite sides have negated direction vectors; this is characteristic of a parallelogram.   we have congruent sides ab=cd and bc=da with unequal lengths because 1^2+6^2 is different from (-4)^2+1^2.   so it's not a square or a rhombus.  

we check the dot product of direction vectors of adjacent sides.   we have

ab dot bc = -4(1) + 1(6) = 2

that's not zero so the sides are not perpendicular.   we've ruled out rectangle.

the direction vectors indicate two pairs of congruent parallel sides, so we choose parallelogram over trapezoid.

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! )