Answer:
- 48
Step-by-step explanation:
If you want to learn more about this concept, it's called composition of functions.
First, you plug in g(x) as if it were x into f(x).
f(g(x))= -5 (1/2x + 4) + 2
= -5/2x - 20 + 2
= -5/2x - 18
Then, plug in the value given, as x.
= -5/2 (12) - 18
= -30 - 18
= - 48
I hope this helped!
Y=kx
12=-4k
k = -3
so when y = 15
y = kx
15 = -3x
x = -5
----------------
$50 / 0.04 = $1250 (4% interest)
so
1250 * 0.06 = $75
answer
interest $75 at 6%
-----------------------------
y = kx
1/4 = 4k
k = 1/16
when x = 5
y = kx
y = 1/16(5)
y = 5/16
-----------
y = kx
-5 = 4k
k = -5/4
direct equation
y = -5/4x
Answer:
0.2514 = 25.14% probability that the diameter of a selected bearing is greater than 85 millimeters.
Step-by-step explanation:
Problems of normally distributed samples are solved using the z-score formula.
In a set with mean
and standard deviation
, the zscore of a measure X is given by:

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
In this question, we have that:

Find the probability that the diameter of a selected bearing is greater than 85 millimeters.
This is 1 subtracted by the pvalue of Z when X = 85. Then



has a pvalue of 0.7486.
1 - 0.7486 = 0.2514
0.2514 = 25.14% probability that the diameter of a selected bearing is greater than 85 millimeters.
Answer:
The correct option is;
(B) Yes, because sampling distributions of population proportions are modeled with a normal model.
Step-by-step explanation:
Here we have the condition for normality being that where we have a population with a given mean and standard deviation, while a sufficiently large sample is drawn from the population while being replaced, the distribution of the sample mean p will be distributed normally according to central limit theorem.