Answer:
A. He will have to work 40 hours to buy the headphones
B. 1200 ≤ 10.25x ≤ 2000 where x is # of hrs worked
He can work anywhere between 118 and 196 hours.
Step-by-step explanation:
A. Divide 399.95 by 10.25 to get 39.01 hours. But since you cant work 0.01 of an hour, you have to round up to the next hour
B. You want to make more than or equal to 1200, so put that in the inequality. You want to make less or equal to 2000, so you put that in the inequality. In the middle, you put 10.25 an hour multiplied by the number of hours, which is a variable.
To solve, I made 10.25x = 1200, and x equaled 117.07, which rounds up to 118 hours because you cant work a 0.07 of an hour. 118 hours is the low number of the spectrum.
To solve for the highest number on the spectrum, you do 10.25x = 2000, and x equals 195.12, but since you cant work 0.12 of an hour, it rounds up to 196 hours.
Answer:
(fg)(x) = 6x³ - 2x² + 12x - 4
Step-by-step explanation:
( f g ) ( x ) = f ( x ) * g ( x )
f(x) = 2x² + 4
g(x) = 3x – 1
( f g ) ( x ) = (2x² + 4) (3x – 1)
= 6x³ - 2x² + 12x - 4
x=-26 is the final anwser :)
X = √7²+4² by using Pythagoras Theory as the triangle is a right angled triangle
Answer:
a)0.6192
b)0.7422
c)0.8904
d)at least 151 sample is needed for 95% probability that sample mean falls within 8$ of the population mean.
Step-by-step explanation:
Let z(p) be the z-statistic of the probability that the mean price for a sample is within the margin of error. Then
z(p)=
where
- Me is the margin of error from the mean
- s is the standard deviation of the population
a.
z(p)=
≈ 0.8764
by looking z-table corresponding p value is 1-0.3808=0.6192
b.
z(p)=
≈ 1.1314
by looking z-table corresponding p value is 1-0.2578=0.7422
c.
z(p)=
≈ 1.6
by looking z-table corresponding p value is 1-0.1096=0.8904
d.
Minimum required sample size for 0.95 probability is
N≥
where
- z is the corresponding z-score in 95% probability (1.96)
- s is the standard deviation (50)
- ME is the margin of error (8)
then N≥
≈150.6
Thus at least 151 sample is needed for 95% probability that sample mean falls within 8$ of the population mean.
