Normal or random variations that are considered part of operating the system at its current capability are <u> c. common cause variations.</u>
Explanation:
Common cause variation is fluctuation caused by unknown factors resulting in a steady but random distribution of output around the average of the data.
Common-cause variation is the natural or expected variation in a process.
Common-cause variation is characterised by:
- Phenomena constantly active within the system
- Variation predictable probabilistically
- Irregular variation within a historical experience base
It is a measure of the process potential, or how well the process can perform when special cause variation removed.
Common cause variation arises from external sources that are not inherent in the process and is where statistical quality control methods are most useful.
Statistical process control charts are used when trying to monitor and control 5- and 6-sigma quality levels.
Answer:
The answer is that it is a speaker note.
Explanation:
It leaves a note for people that use presentation files. I use it all the time on my google slides.
Answer:
here is what I think!
Explanation:
G-mail is:
- secure
- easy to use
- fast
- can be used to sign in anywhere!<u>(including brainly)</u>
- you don't need to pay when creating one
- can help you in billing and buying apps and their paid product
- <em><u>you </u></em> can use it because <em>why no!</em>
- some websites need G-mail to be used
thats why you should use G-mail
tell me if you have an idea!
Implement the simulation of a biased 6-sided die which takes the values 1,2,3,4,5,6 with probabilities 1/8,1/12,1/8,1/12,1/12,1/
hjlf
Answer:
see explaination
Explanation:
import numpy as np
import matplotlib.pyplot as plt
a = [1, 2, 3, 4, 5, 6]
prob = [1.0/8.0, 1.0/12.0, 1.0/8.0, 1.0/12.0, 1.0/12.0, 1.0/2.0]
smls = 1000000
rolls = list(np.random.choice(a, smls, p=prob))
counts = [rolls.count(i) for i in a]
prob_exper = [float(counts[i])/1000000.0 for i in range(6)]
print("\nProbabilities from experiment : \n\n", prob_exper, end = "\n\n")
plt.hist(rolls)
plt.title("Histogram with counts")
plt.show()
check attachment output and histogram
Answer:
1. Date 2. It will appear to the right of the selected column.
