Answer:
2X-1
Step-by-step explanation:
as far as i got
Answer:
I think 13 is C
Step-by-step explanation:
71+52= 123
180-123= 57
Answer:
We know that n = 50 and p =0.78.
We need to check the conditions in order to use the normal approximation.
Since both conditions are satisfied we can use the normal approximation and the distribution for the proportion is given by:

With the following parameters:


Step-by-step explanation:
Previous concepts
The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".
Solution to the problem
We know that n = 50 and p =0.78.
We need to check the conditions in order to use the normal approximation.
Since both conditions are satisfied we can use the normal approximation and the distribution for the proportion is given by:

With the following parameters:


Given:
CDEF is dilated by a scale factor of
to form the quadrilateral C'D'E'F'.
To find:
The measure of side E'F'.
Solution:
If a figure is dilated by a scale factor of k, then the side of dilated figure is k times of corresponding side of original figure.
CDEF is dilated by a scale factor of
to form the quadrilateral C'D'E'F'. So,

From the given figure it is clear that
. So,


Therefore, the measure of side E'F' is 9 units.
Answer:
Residual = 11.462
Since the residual is positive, it means it is above the regression line.
Step-by-step explanation:
The residual is simply the difference between the observed y-value which is gotten from the scatter plot and the predicted y-value which is gotten from regression equation line.
The predicted y-value is given as 20.7°
The regression equation for temperature change is given as;
y^ = 9.1 + 0.6h
h is the observed amount of humidity and it's given to be 23 percent or 0.23.
Thus;
y^ = 9.1 + 0.6(0.23)
y^ = 9.238
Thus:
Residual = 20.7 - 9.238
Residual = 11.462
Since the residual is positive, it means it is above the regression line.