- 15.6.49E: An early example of SPC was described in Industrial Quality Control...
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- 15.6.52E: The following represent the number of defects per 1000 feet in rubb...
- 15.6.53E: The following represent the number of solder defects observed on 24...
- 15.6.54E: Consider the data on the number of earthquakes of magnitude 7.0 or ...
- 15.6.55E: In a semiconductor manufacturing company, samples of 200 wafers are...
- 15.6.56E: The following data are the number of spelling errors detected for e...
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Solutions for Chapter 15.6: Applied Statistics and Probability for Engineers 6th Edition
Full solutions for Applied Statistics and Probability for Engineers | 6th Edition
2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).
Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.
Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.
Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.
Coeficient of determination
See R 2 .
Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.
The mean of the conditional probability distribution of a random variable.
The probability of an event given that the random experiment produces an outcome in another event.
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.
A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.
Another name for a probability density function
The response variable in regression or a designed experiment.
A study in which a sample from a population is used to make inference to the population. See Analytic study
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.
A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model
A function used in the probability density function of a gamma random variable that can be considered to extend factorials