- M1Q1: A ring-spun yarn shows more strength variability that a rotor-spun yarn. Can we say that the ring-spun yarn offers better strength quality than the rotor-spun yarn?
- M1Q2: Is it so that the application of statistical techniques for quality control in textiles has started recently?
- M1Q3: Why quality control is at all required?
- M2Q1: Give two examples each on continuous random variable and discrete random variable.
- M2Q2: Why the statistical characteristics of the primary data do not often exactly equal to those of grouped data?
- M2Q3: Is it so that the probability and relative frequency are same?
- M2Q4: Is normal distribution an example of two-parameter distribution?
- M2Q5: How one can conclude whether a sample can be regarded as taken from a population that follows normal distribution?
- M2Q6: Is Binomial distribution can be taken as a limiting form of Poission distribution?
- M3Q1: What is the difference between the parameter and statistic?
- M3Q2: What is the standard error of mean fiber length?
- M3Q3: Why it is not practically possible to obtain a simple random sample as defined?
- M3Q4: Why it is often said that the larger sample can give more precise estimation of population mean?
- M3Q5: While calculation of variance, sometimes the divisor is found to be n-1, where n is sample size, and sometimes it is found as n? Why is it so?
- M3Q6: In order to know whether the newly developed process is superior to the existing process,which test-one-tailed test or two-tailed test-is recommended?
- M3Q7: In order to know whether the newly developed process is different than the existing process, which test-one-tailed test or two-tailed test-is recommended?
- M3Q8: Is it so that the probability of type II error can be reduced by the choice of a larger sample size?
- M4Q1: Is it not possible that a process turns to be out of control because of presence of random variation?
- M4Q2: Is it so that a process can be found to be out-of control even if there is no point falling out of 3-sigma limits?
- M4Q3: If process mean is in control, but the process variability is not in control, can the process be said to be under control?
- M4Q4: Is Shewhart control chart able to detect a small shift in process mean?
- M4Q5: Name the probability distribution that the process defectives can be regarded to follow?
- M4Q6: Name the probability distribution that the process defects can be regarded to follow?
- M5Q1: Does process capability refer to the uniformity of the process?
- M5Q2: State the two reasons for poor process capability.
- M5Q3: What is the advantage of probability plot over histogram in assessing process capability?
- M5Q4: What are the measures of process capability?
- M5Q5: is it so that the higher is the process capability ratio the lower is the process fall out?
- M5Q6: Can Cp and Cpk be negative?
- M5Q7: What is the merit of Cpm over Cp or Cpmk over Cpk?
- M5Q8: Is it required to check the normality character of a process before finding the process capability?
- M6Q1: Why the non-Schwhart control charts are required to be set up?
- M6Q2: Which one is the least effective in detecting small shift in the process mean: Cusum control chart, MA control chart, and EWMA control chart?
- M6Q3: Which one is the most effective in detecting small shift in the process mean: Cusum control chart, MA control chart, and EWMA control chart?
- M6Q4: What is the equivalence of MA control chart and EWMA control charts?
- M7Q1: How the practical OC curve can be made closer to the ideal OC curve?
- M7Q2: How the discriminatory power of the acceptance sampling plan can be increased?
- M7Q3: What are the specific points of OC curve that a quality engineer looks for?
- M8Q1: How many pieces out of one million garments are found to be defective under six sigma process?
- M8Q2: How many defective garments a six sigma process would produce if the process means shifts to 1.5 times the standard deviation off the target?
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M1Q1: A ring-spun yarn shows more strength variability that a rotor-spun yarn. Can we say that the ring-spun yarn offers better strength quality than the rotor-spun yarn?
ANS: Yes
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M1Q2: Is it so that the application of statistical techniques for quality control in textiles has started recently?
ANS: No, the British textile industry began use of statistical techniques for product/process development in 1932-1933.
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M1Q3: Why quality control is at all required?
ANS: Effective quality improvement can result in increase in productivity and reduction in cost.
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M2Q1: Give two examples each on continuous random variable and discrete random variable.
ANS: Fiber length and fiber strength are the two examples of continuous random variable. Number of fibers in yarn cross-section, number of holes in a knitwear are the two examples of discrete random variable.
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M2Q2: Why the statistical characteristics of the primary data do not often exactly equal to those of grouped data?
ANS: While calculation of statistical characteristics of a grouped data the different values that fall in a certain class are considered to be numerically same as the middle value of that class and this makes the results different.
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M2Q3: Is it so that the probability and relative frequency are same?
ANS: The probability and relative frequency possess the same value. Relative frequency is interpreted as ex post, while probability is interpreted as ex ante.
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M2Q4: Is normal distribution an example of two-parameter distribution?
ANS: Yes, the normal distribution is described by two parameters, namely, mean and standard deviation.
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M2Q5: How one can conclude whether a sample can be regarded as taken from a population that follows normal distribution?
ANS: By using goodness-of-fit tests (objectively) and probability plot (subjectively), one can conclude this.
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M2Q6: Is Binomial distribution can be taken as a limiting form of Poission distribution?
ANS: Yes, a binomial distribution with probability approaching to zero and number of samples approaching to infinity limits to a Poission distribution.
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M3Q1: What is the difference between the parameter and statistic?
ANS: The parameter, representing a statistical characteristic of the population, is a constant for the given population, but, the statistic, representing a statistical characteristic of the sample, is a variable.
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M3Q2: What is the standard error of mean fiber length?
ANS: The standard deviation of distribution of mean fiber length is the standard error of mean fiber length.
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M3Q3: Why it is not practically possible to obtain a simple random sample as defined?
ANS: It is practically impossible to numerically identify each individual of a population either because of the large size of the population or because of the inaccessibility or current non-existence of some of the individuals, therefore, it is not possible to obtain a simple random sample as defined.
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M3Q4: Why it is often said that the larger sample can give more precise estimation of population mean?
ANS: This is said so because as the sample size increases, the standard deviation of sample mean (that is, standard error of sample mean) reduces.
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M3Q5: While calculation of variance, sometimes the divisor is found to be n-1, where n is sample size, and sometimes it is found as n? Why is it so?
ANS: While calculating the sample variance, the divisor should be n, and this is known as a biased estimator of population variance. But, when the divisor is n-1, the resulting expression is known as an unbiased estimator of population variance.
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M3Q6: In order to know whether the newly developed process is superior to the existing process,which test-one-tailed test or two-tailed test-is recommended?
ANS: One-tailed test.
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M3Q7: In order to know whether the newly developed process is different than the existing process, which test-one-tailed test or two-tailed test-is recommended?
Ans: Two-tailed test.
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M3Q8: Is it so that the probability of type II error can be reduced by the choice of a larger sample size?
ANS : Yes
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M4Q1: Is it not possible that a process turns to be out of control because of presence of random variation?
ANS: Yes, it is possible, but the probability of such occurrence is very low, that is, 0.0027.
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M4Q2: Is it so that a process can be found to be out-of control even if there is no point falling out of 3-sigma limits?
ANS: Yes, it is possible, the presence of a run, trend, etc. can do so.
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M4Q3: If process mean is in control, but the process variability is not in control, can the process be said to be under control?
ANS: No.
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M4Q4: Is Shewhart control chart able to detect a small shift in process mean?
ANS: No.
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M4Q5: Name the probability distribution that the process defectives can be regarded to follow?
ANS: Binomial distribution.
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M4Q6: Name the probability distribution that the process defects can be regarded to follow?
ANS: Poission distribution.
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M5Q1: Does process capability refer to the uniformity of the process?
ANS: Yes.
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M5Q2: State the two reasons for poor process capability.
ANS: The two reasons for poor process capability are poor process centering and excess process variability.
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M5Q3: What is the advantage of probability plot over histogram in assessing process capability?
ANS: The probability plot requires relatively small data, while the histogram requires relatively large data to assess process capability.
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M5Q4: What are the measures of process capability?
ANS: The measures of process capability are Cp, Cpu, Cpl, Cpk, Cpm, Cpmk.
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M5Q5: is it so that the higher is the process capability ratio the lower is the process fall out?
ANS: Yes
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M5Q6: Can Cp and Cpk be negative?
ANS: Cp cannot be negative, but Cpk can be negative.
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M5Q7: What is the merit of Cpm over Cp or Cpmk over Cpk?
ANS: Cp and Cpk are not the adequate measures of process centering, whereas Cpm and Cpmk are known to be the adequate measures of process centering.
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M5Q8: Is it required to check the normality character of a process before finding the process capability?
ANS: Yes, otherwise the capability of the process may be misinterpreted.
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M6Q1: Why the non-Schwhart control charts are required to be set up?
ANS: The Shewhart control charts are relative insensitive to small shifts in the process mean, but the non-Shewhart control charts are expected to detect the small shifts in the process mean.
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M6Q2: Which one is the least effective in detecting small shift in the process mean: Cusum control chart, MA control chart, and EWMA control chart?
ANS: The MA control chart is generally found to be the least effective among the three in detecting small shift in the process mean.
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M6Q3: Which one is the most effective in detecting small shift in the process mean: Cusum control chart, MA control chart, and EWMA control chart?
ANS: The EWMA control chart and Cusum control charts can be made comparable depending upon the choice of parameters of the control charts.
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M6Q4: What is the equivalence of MA control chart and EWMA control charts?
ANS: If /\\=2/(w+1) for the EWMA control chart then it is equivalent to a w-period moving average control chart in the sense that the control limits are identical in steady state.
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M7Q1: How the practical OC curve can be made closer to the ideal OC curve?
ANS: By increasing the sample size, the practical OC curve can be made closer to the ideal OC curve.
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M7Q2: How the discriminatory power of the acceptance sampling plan can be increased?
ANS: The discriminatory power of the acceptance sampling plan can be increased by increasing the sample size. Also, plans with smaller value of c provide discrimination at lower levels of lot fraction defective than do plans with larger values of c.
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M7Q3: What are the specific points of OC curve that a quality engineer looks for?
ANS: A quality engineer always looks for the AQL and the LTPD in an OC curve.
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M8Q1: How many pieces out of one million garments are found to be defective under six sigma process?
ANS: 0.002 out of one million garments are found to be defective under six sigma process.
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M8Q2: How many defective garments a six sigma process would produce if the process means shifts to 1.5 times the standard deviation off the target?
ANS: Under this scenario, a six-sigma process would produce about 3.4 ppm defective.
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