Statistical Quality Control Questions and Answers – Modeling Process Quality – Discrete Distributions – 1

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This set of Statistical Quality Control Multiple Choice Questions & Answers (MCQs) focuses on “Modeling Process Quality – Discrete Distributions – 1”.

1. What will be the variance of the sample following hyper geometric distribution and having 5 items defective in 100 lot items, if 10 samples are taken without replacement?
a) 0.431818
b) 0.410023
c) 0.483838
d) 0.568898
View Answer

Answer: a
Explanation: variance of hyper geometric distribution is given by,
\(\sigma^2 = \frac{nD}{N} (1-\frac{D}{n})(\frac{N-n}{N-1})\) Putting, N=100, n=10, D=5; we get variance=0.431818.
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2. For a Poisson distribution, parameter λ is having a value of 7. What is the variance for the distribution?
a) 2.64
b) 2.78
c) 7
d) 14
View Answer

Answer: c
Explanation: For the Poisson probability distribution, the variance is equal to the parameter λ of the probability distribution. So for the above stated distribution, the variance will be equal to 7.

3. For 9 Bernoulli trials, what will be the mean of trials when the probability of failure is 0.56?
a) 5.04
b) 1.74
c) 3.96
d) 2.82
View Answer

Answer: c
Explanation: For n=9 and probability of failure=0.56, we get probability of success;
p = 1 – 0.56 = 0.44.
Using this, we get;
mean= np=9*0.44=3.96.

4. In sample fraction defective, the ̂ symbol represents that ______
a) p ̂ is an estimate of the true value of binomial parameter p
b) It is the “raised to power” operator
c) It is having no significance
d) It is the true value of binomial parameter p
View Answer

Answer: a
Explanation: The ̂ symbol represents the estimate of the true, unknown value of the binomial parameter p.p is the value of sample fraction defective or sample fraction nonconforming.

5. For a Pascal distribution, the probability of occurrence of a event x is given by _____
a) \(p(x)=\left(x-1 \atop r-1\right) p^x (1-p)^{x-r}\)
b) \(p(x)=\left(r-1 \atop x-1\right) p^x (1-p)^{x-r}\)
c) \(p(x)=\left(x-1 \atop r-1\right) p^x (1-p)^{1-r}\)
d) \(p(x)=\left(x-1 \atop r-1\right) p^x (1-p)^{x-1}\)
View Answer

Answer: a
Explanation: The Pascal probability distribution is given by the following equation,
\(p(x)=\left(x-1 \atop r-1\right) p^x (1-p)^{x-r}\)
Where x=r, r+1, r+2… and r≥1 is an integer.
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6. When is Pascal distribution, called negative binomial distribution?
a) When r>0 but not necessarily an integer
b) When r<0 but an integer
c) When r>3 and an integer
d) When r=0 and an integer
View Answer

Answer: a
Explanation: When in a Pascal probability distribution, r is greater than 0 but not necessarily an integer, the Pascal distribution is called, negative binomial distribution. This is a special case of Pascal distribution.

7. When r=1 in a Pascal distribution, what is this case called?
a) Negative binomial distribution
b) Positive binomial distribution
c) Hyper geometric distribution
d) Geometric distribution
View Answer

Answer: d
Explanation: The special case of Pascal distribution when r=1, we get geometric distribution. This represents the number of Bernoulli trials until the first success occurs.

8. Lognormal distributions are a part of discrete distributions.
a) True
b) False
View Answer

Answer: b
Explanation: Lognormal distributions are an important part of continuous distributions, as they represent probability distribution of variables having continuous values. They are not counted in discrete distributions.

9. Which of the expression represents the mean of Pascal distribution?
a) r/p
b) np
c) nD/N
d) np(1-p)
View Answer

Answer: a
Explanation: The mean of the Pascal distribution is given by the following equation,
μ=r/p
Where r > 1 and an integer and p is probability of success.
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10. The equation of variance, i.e. σ2=\(\frac{r(1-p)}{p^2}\) is true for _____
a) Hyper geometric distribution
b) Pascal Distribution
c) Normal distribution
d) Binomial distribution
View Answer

Answer: b
Explanation: The expression \(\frac{r(1-p)}{p^2}\) is useful for finding out variance of the Pascal probability distribution. We can identify it as it has “r” in the expression of variance.

11. Bernoulli trials are a part of _____ probability distribution.
a) Poisson
b) Binomial
c) Hyper geometric
d) Normal
View Answer

Answer: b
Explanation: Bernoulli trials are the independent trials having a result either a “success” or a “failure”. They are an important part of binomial probability distribution. The probability distribution is based upon the Bernoulli trials.

12. \( \left(a \atop b\right)\) is expanded as __________
a) \(\frac{a!}{b!}\)
b) \(\frac{a!}{b!(a-b)!}\)
c) \(\frac{a!}{b!(b-a)!}\)
d) \(\frac{b!}{a!}\)
View Answer

Answer: b
Explanation: \( \left(a \atop b\right)\) is generally acronym for the combination \(a \atop b \)C or \(\frac{a!}{b!(a-b)!}\)

13. For a Pascal distribution; r=3 and the probability of success is 0.7895. What will be the value of “σ”?
a) 1.0131
b) 3
c) 1.0065
d) 1.7320
View Answer

Answer: c
Explanation: We know, for a Pascal distribution,
σ2=r(1-p)/p2
Evaluation of the above equation using the values r=3 and p=0.7895, we get, σ=1.0063.
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14. For evaluation probability of measurement of length of a part being a certain value, discrete probability distributions can be used.
a) True
b) False
View Answer

Answer: b
Explanation: The length of a part is always measured as a continuous value. Taking this fact into account, we cannot use discrete probability distributions for evaluation of probability. The continuous probability distributions are used for the above mentioned purpose.

Sanfoundry Global Education & Learning Series – Statistical Quality Control.

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Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn