Answer:
a) 14960 bottles
b) 502 bottles
Step-by-step explanation:
Given that:
Mean (μ) = 24 ounces, standard deviation (σ) = 0.14 ounces
a) From empirical rule (68−95−99.7%) , 68% of the population fall within 1 standard deviation of the mean (μ ± 1σ).
Therefore 68% fall within 0.14 ounces of the mean
the number of bottle = 22,000*68% = 14960 bottles
b) To solve this we are going to use the z score equation given as:
where x is the raw score = 23.72
From the normal probability distribution table: P(X < 23.72) = P (Z < -2) = 0.0228
The number of rejected bottles = 22000 × 0.0228 = 502 bottles
Answer:
C
Step-by-step explanation:
200 x 5 = 1,000
100 x 10 = 1,000
C - 5 to 10 days
Will the answer to the expression is -4 but i don't see any arrows <span />
Answer:
The length of each side is 31.5m, 26.5m, 7m
Step-by-step explanation:
Let the length of the first side of the triangle be x meters.
Then, the second side is x-5 meters.
The third side is given as 7 meters.
The perimeter is 65 meters
This gives us the equation:
The length of each side is 31.5m, 26.5m, 7m
To best emphasize the number of defects. Manager should use graph 3 (refer the image shown):
If we talk about graph 1, it can also be used but usually we put the time line on the horizontal axis, for the convenience and the quantity to be measured on the y-axis. In the graph 1, the time is placed on the vertical axis (x-axis) so it would not be a good pick for the manager.
Same is the case with graph 2 again we have time on the vertical axis. So it is not a good idea to with graph 2.
Graph 3 could be the best to emphasize the number of defects because first of all time is placed on the horizontal axis and the quantity to be shown is on the vertical axis. Secondly, the range of the vertical axis is less so it is easy to observe the data set on the graph quite distinctively. Therefore, graph 3 is the best pick.
Graph 4 is placed correctly in terms of vertical and horizontal axes but the range of vertical axis is quite high due to which the dispersion or the display of the data is quite compressed and it gets hard to visualize.
