00:01
So a bottling plant fills 12 ounce cans of soda, automatic filling process that could be adjusted to any mean fill volume that will fill the cans according to a normal distribution.
00:10
However, not all cans contain the same volume and they vary.
00:14
Historically, we vary by 0 .025 ounces.
00:18
Operation manager at the plant wants to know if they put too much soda in the can, the company loses money.
00:22
If too little is put in the can, the customer are shortchanged.
00:25
The state department is going to come in and the manager wants to make sure that they don't get fined.
00:30
She knows the process is to select one can at random, and if that contains less than 11 .97 ounces, the company will be reprimanded and potentially find, assuming that the manager wants at most 5 % chance of this happening, at what level should she set the mean fill level? okay, so we're talking about the z scores here, and we're looking to find the new mean.
00:54
We know that this is 11 .97 minus whatever our mean is over 0 .0 .0 .2.
01:00
And that's going to be the z score at 0 .05%.
01:06
So we're going to have to use inverse normal.
01:08
And what you can do is 0 .05, 0 .1.
01:13
I'm just going to grab a calculator.
01:15
And just to show you, under distributions, we're going to go inverse normal...