a. Note that
is continuous for all
. If
attains a maximum at
, then
. Compute the derivative of
.

Evaluate this at
and solve for
.




To ensure that a maximum is reached for this value of
, we need to check the sign of the second derivative at this critical point.

The second derivative at
is negative, which indicate the function is concave downward, which in turn means that
is indeed a (local) maximum.
b. When
, we have derivatives

Inflection points can occur where the second derivative vanishes.




Then we have three possible inflection points when
,
, or
.
To decide which are actually inflection points, check the sign of
in each of the intervals
,
,
, and
. It's enough to check the sign of any test value of
from each interval.




The sign of
changes to either side of
and
, but not
. This means only
and
are inflection points.
Answer:
Step-by-step explanation:
s = n(a + 1)
n(a + 1) = s
a + 1 = s/n
a = s/n - 1
To find the area<span> of a </span>triangle<span>, multiply the base by the height, and then divide by 2. The division by 2 comes from the fact that a parallelogram can be divided into 2 triangles. For example, in the diagram to the left, the </span>area<span> of each </span>triangle<span> is equal to one-half the </span>area<span> of the parallelogram. their is your answer </span>
Answer:
Step-by-step explanation:
A)12
B)16
C) I think 32 but it could also be 20 due to the terrible wording of the problem. The problem sounds like they are saying females are different from children, so idk
Answer:
E(w) = 1600000
v(w) = 240000
Step-by-step explanation:
given data
sequence = 1 million iid (+1 and +2)
probability of transmitting a +1 = 0.4
solution
sequence will be here as
P{Xi = k } = 0.4 for k = +1
0.6 for k = +2
and define is
x1 + x2 + ................ + X1000000
so for expected value for W
E(w) = E( x1 + x2 + ................ + X1000000 ) ......................1
as per the linear probability of expectation
E(w) = 1000000 ( 0.4 × 1 + 0.6 × 2)
E(w) = 1600000
and
for variance of W
v(w) = V ( x1 + x2 + ................ + X1000000 ) ..........................2
v(w) = V x1 + V x2 + ................ + V X1000000
here also same as that xi are i.e d so cov(xi, xj ) = 0 and i ≠ j
so
v(w) = 1000000 ( v(x) )
v(w) = 1000000 ( 0.24)
v(w) = 240000