Answer: C) (1,0.80) represents the unit rate. E) (25,20) represents the price of a necklace with 25 beads
Step-by-step explanation:
(1, 0.80) represents the unit rate.
(25, 20) represents the price of a necklace with 25 beads.
= unit rate →
= 0.80;
= 0.80;
= 0.80
Point (1, r) → (1, 0.80) represents the unit rate.
25(0.80) = 20; thus, (25, 20) represents the price of 25 beads.
Hope this helps
Answer:
The probability mass of X is 0.03
Step-by-step explanation:
If we set the winning requirement of your heads and my tails then the occurring possibility of both is 1/2 or 0.5.
Hence let us make a graph and use the figures to calculate the all the probabilities of you getting a heads.
Where X represents the number of dollars won during the flip of the coin, probability of heads represent the chances of occurrence of the value and of winning the dollars.
The probability of winning start to drop as the winning amount increases.
X 0 1 2 3 4 5
Probability of Heads 0 0.50 0.25 0.13 0.06 0.03
You're looking for the extreme values of
subject to the constraint
.
The target function has partial derivatives (set equal to 0)


so there is only one critical point at
. But this point does not fall in the region
. There are no extreme values in the region of interest, so we check the boundary.
Parameterize the boundary of
by


with
. Then
can be considered a function of
alone:



has critical points where
:



but
for all
, so this case yields nothing important.
At these critical points, we have temperatures of


so the plate is hottest at (1, 0) with a temperature of 14 (degrees?) and coldest at (-1, 0) with a temp of -12.
Answer:
Step-by-step explanation:
to get the mean you need to add up all the numbers
46+52+51+54+49+51+61=364
then divide that sum by the amount of numbers you have
364 divided by 7=52
so the mean you have is 52 degrees
Answer:
Step-by-step explanation:
Outline are values which are entirely different from those remaining values in a data set. These extreme values can skew an approximately normal distribution by skewing the distribution in the direction of the outliers and this makes it difficult for the data set to be analyzed.
Its effect is such that the mean becomes extremely sensitive to extreme outliers making it possible that the mean is this not a representative of the population and this theoretically affects the standard deviation.