It is 18 because the radius is half the diameter and 9 x 2 = 18.
Answer:
y - y1 = m(x - x1)
Step-by-step explanation:
The point slope form equation is [ y - y1 = m(x - x1) ]
(x1, y1) is any given point on the line.
m is the slope.
To solve this you would need these things:
- Slope (y2-y1/x2-x1)
- Y-intercept (y = mx + b)
Best of Luck!
The thickness of a brand new US penny that hasn't been
worn down is 1.52 millimeters.
If you have a million pennies, there are many ways to arrange them.
You can pile them all in one pile, or shovel them into many piles, or
stack them up in any number of stacks up to a half-million stacks
with two pennies in each stack, or try somehow to stack them all up
in one stack that's a million thicknesses high.
Any stack with 'n' pennies in the stack is 1.52n millimeters high.
If you somehow succeed in stacking all million of them in one stack,
then the height of that stack would be . . .
(1,000,000) x (1.52 mm) = 1,520,000 millimeters
152,000 centimeters
1,520 meters
1.52 kilometers
(about 59,842.5 inches
4,986.9 feet
1,662.3 yards
7.56 furlongs
0.944 mile
all rounded)
Answer:

And the z score for 0.4 is

And then the probability desired would be:

Step-by-step explanation:
The normal approximation for this case is satisfied since the value for p is near to 0.5 and the sample size is large enough, and we have:


For this case we can assume that the population proportion have the following distribution
Where:


And we want to find this probability:

And we can use the z score formula given by:

And the z score for 0.4 is

And then the probability desired would be:
