bluuebunny/crossref_metadata_2025_split huggingface dataset:
Database Systems for Acceptance Sampling
Authors: Lenz Hans‐J. 
Published: February 2008

Abstract

Data collected in on‐line or off‐line quality control are no longer read into files owned by single departments but are stored in a database system. The data can be accessed according to the user's view (profile) and his read/write rights. The use of such operative data is called
on‐line transaction processing
(OLTP). It is of interest to low ranked officers responsible for administrative tasks and concerned with workflows like incoming batch inspection. Middle managers need data for planning and decisions, i.e., for “on‐line analytical processing” (OLAP), where grouped and aggregated data are mandatory. Such databases are called
data warehouses
and the managers' views are known as
data marts
. The main design ideas of such systems are presented.
Acceptance Sampling in Modern Industrial Environments
Authors: Von Collani Elart , Göb Rainer 
Published: February 2008

Abstract
In the industrial mass production environments of the first half of the twentieth century, statistical product inspection (SPI) was a central tool of statistical quality control (SQC). Contemporary SQC emphasizes methods of provision and prevention such as experimental design or process control rather than inspection. To remain a valid tool of modern SQC, SPI has to be redefined in its purpose and in its procedures. This article analyzes important challenges for SPI in modern industrial environments: the adjunct role of SPI in product control, adaptiveness and flexibility of SPI, the role of prior information on product quality, the economic concern of SPI, and quality measurement as a purpose of SPI.
Sampling in Pharmaceutical and Chemical Industries
Authors: Melgaard Henrik 
Published: February 2008

Abstract
This article discusses the specific issues related to sampling in the pharmaceutical and chemical industry. The effect of measurement uncertainty in case of acceptance sampling by variables is one issue. The largest deterioration on the operating characteristics is seen from the low‐frequency error type. Sampling in bulk materials and retesting is also discussed. The trend in this industry is in the direction of faster measurement methods replacing off‐line by on‐line measuring and release.
Sampling in Software Development
Authors: Runeson Per 
Published: February 2008

AbstractIn software development, sampling inspection is applied during the development process, as the production process for software is trivial. Insoftware inspection, which is a manual scrutiny of software code or specifications, sampling is applied to estimate the number of remaining faults and to guide which parts of the artifact to inspect. Capture–recapture techniques are used in the fault content estimation. Insoftware testing, which is the dynamic execution of code, sampling is implicitly or explicitly applied to select test cases. Since the number of states is extremely high in software products, a complete coverage of all cases is practically impossible. In addition to scoping the tests to a reasonable magnitude of size, sampling is used to estimate the number of remaining faults and to predict the operational failure behavior of the software, e.g. through software reliability growth models. The theory is quite well established in the field, while the industrial practice is underdeveloped.
Sampling in Implementation of Statistical Process Control
Authors: Nishina Ken 
Published: February 2008

Abstract
In this article, we explain how to obtain data by sampling and implement statistical process control (SPC) over its life cycle. The SPC life cycle consists of three steps: mass production preparation, early‐stage mass production, and routine mass production. In mass production preparation, machine performance is ensured by sampling according to an experimental design. In mass production stage, by sampling in control charts, process variation is reduced and process capability is ensured. Sampling to prevent the escape of a nonconforming product in routine mass production is also explained. In addition, considering semiconductor wafer process, the inherent process variation related to sampling is introduced.
Sampling from Virtual Populations
Authors: Polansky Alan M. 
Published: February 2008

Abstract
The increasing complexity of the problems addressed in modern statistical inference has created many situations where a closed‐form analytical solution is not available. For example, when constructing a rejection region for a hypothesis‐testing procedure, the sampling distribution of the test statistic under the null hypothesis may not have a closed form, even when the exact form of the population is known. In nonparametric situations where the population may not be known, estimates of the sampling distribution of a test statistic or parameter estimate computed using the bootstrap often do not have a closed form. The use of empirical approximations based on simulated realizations of the random process that produces the observed data have been implemented for many years. Almost universal access to computers has made such simulations easy to perform. The purpose of this article is to review the most common methodologies for performing these simulations.
Adaptive and Spatial Sampling Designs
Authors: Siegfried Gabler 
Published: February 2008

Abstract
On the one hand, spatial and adaptive sampling designs are informative since they depend on the characteristic of interest—in contrast to the classical sampling designs such as simple random sampling. On the other hand, better results can be obtained using adaptive designs for populations with special patterns. Dealing with spatial inferences, model‐based approaches are often used. The fixed‐population approach is replaced by a stochastic one. The characteristic of interest is defined for a continuous region of space and time that can be described by a spatial stochastic process.
Selection and Validation of Response Scales
Authors: Ograjenšek Irena 
Published: February 2008

Abstract
Measurement is defined as a set of rules for assigning either numbers or numerals to empirical properties of a concept under consideration. Concepts can be operationalized as variables with different properties of measurement scale: nominal, ordinal, interval, and ratio. The level of measurement determines which mathematical operations are possible on a variable, which, in turn, define the tools of statistical analysis at researchers' disposal.
Specific measurement tools applicable in the survey setting include list questions, category questions, ranking questions, and rating or scale questions. We want these measures to be reliable and valid, capturing what they are supposed to capture.
In the process of measurement, data can be obtained either in formal or informal settings and can represent verbal (oral and written) as well as nonverbal acts and/or responses. In order to minimize the possible effect of the chosen data collection method on the validity and/or scope of the research findings, researchers can use two or more methods of data collection to test hypotheses and measure variables; this is the essence of the so‐called triangulation.
Processing of Survey Data
Authors: Biemer Paul P. 
Published: February 2008

Abstract
Upon completion of data collection, the survey data must be converted from its raw and unedited state to a verified and corrected state that is ready for analysis and/or dissemination to the users. These steps may include questionnaire inspection, data entry, editing, coding, data preparation, documentation, and data analysis. This chapter discusses these steps as well as their potential effect on data quality.
Survey Quality and Survey Ethics
Authors: Djerf Kari 
Published: February 2008

Abstract

Producers of statistics have devoted considerable efforts to improve the quality of their products. Traditionally, surveys are annotated with quality descriptions providing the clients with support to interpret the results. Furthermore, there can be studies on the quality of the entire sample survey, called
quality reports
. The clients often need more profound information on the organization, applied procedures and processes as well as the products. Thus, the approach is changing from a mere focus on product quality to total quality management (TQM).

This article describes various perspectives of survey quality, starting from traditional reporting and ending up with recent more comprehensive quality approaches.
Descriptive Statistics
Authors: Shore Haim 
Published: February 2008

Abstract
Descriptive statistics, calculated from the data, provide summary information about properties of a data set. In this article some of the most widely used descriptive statistics are detailed with the allied formulae. These include the sample average, median and other sample quantiles, mode, standard deviation, coefficient of variation, range, interquartile range, skewness, and kurtosis.
Normality Tests
Authors: Koning Alex J. 
Published: February 2008

Abstract

For testing normality with unknown parameters μ and σ
2
, extensive simulation studies have shown the superiority of the Shapiro–Wilk test and variants. The Shapiro–Francia test is a simplified version of the Shapiro–Wilk test, which uses the squared normal probability plot correlation as a test statistic. The Cramér–von Mises and Anderson–Darling tests with plug‐in estimators for μ and σ
2
perform slightly worse than the Shapiro–Wilk test.
Statistical Methods for Counts, Rates, and Proportions
Authors: Thyregod Poul 
Published: February 2008

Abstract
Poisson distribution and multinomial distribution are natural distribution models for count and frequency data for categorical variables. When there are only two categories, the multinomial reduces to the binomial distribution.
The distributions are skewed with a variance that changes with the mean value, and therefore standard methods for normally distributed variables such as ANOVA (analysis of variance) and the general linear model cannot reasonably be applied.
When events are counted and classified by two or more categorical variables or when sample units are classified, the counts or frequencies are often presented in tabular form in so‐called contingency tables.
A general framework for analyzing association in multidimensional contingency tables is provided by the log‐linear models. When a set of nested models is formulated, higher‐order interaction terms may be removed successively in analogy with ANOVA models for normally distributed variables. A subset of these models, the logit models, may be used for assessing the effect of explanatory variables on a categorical response variable.
Another important subset of the log‐linear models is constituted by the graphical models. These models allow for a factorization of the joint probability into a product of conditional distributions.
The log‐linear and logit models are special cases of a wider class of models, the generalized linear models. In this framework, the effect of continuous explanatory variables may be modeled in regression‐like models. An example is the logistic regression where the effect of explanatory variables on a response probability is modeled.
When the assumption of independence is violated, overdispersion might result. A method for modeling overdispersion is also presented.
Sample‐Size Determination
Authors: Shore Haim 
Published: February 2008

Abstract
Sample data may be collected with different objectives in different scenarios. In some cases, one may wish to collect enough observations that would guarantee minimal prespecified reliability for the conclusions derived from the data. On other occasions, in addition to specified constraints regarding reliability, an optimal value is desired of some objective function that would guarantee that the sampling procedure is optimal in some sense. In all these cases, a major decision variable is the required sample size. In this article, sample‐size determination is addressed, relating, in particular, to four often‐encountered scenarios: hypothesis testing, parameter estimation, relational modeling, and optimal sampling.
Measures of Association
Authors: Hershberger Scott L. , Fisher Dennis G. 
Published: February 2008

Abstract
Measures of association are numerical estimates of the magnitude of the statistical dependence between two variables, especially if these variables are categorical. A multitude of such measures have been proposed; we review some of the most commonly used ones, dividing them into two types. The first is suitable for nominal scale data, and includes measures of predictive association. The second type is suitable for ordinal data, and includes measures of concordance‐discordance.
Confidence Intervals
Authors: Dracup Chris 
Published: February 2008

AbstractIn estimating a population parameter from sample data, it is more informative to provide a range of “plausible” parameter values rather than a one‐point estimate. A 95% confidence interval contains those values of the population parameter that would not be rejected at the 0.05 significance level by a statistical test applied to the sample data. In the long run, such intervals should provide 95% “coverage”, with 95% of them including their true population value (assuming the data meet any distribution assumptions). Topics covered include the construction of confidence intervals for means, proportions, and mean differences; the effects of discrete and nonsymmetrical sampling distributions; central and noncentral intervals; bootstrapped intervals; and the Bayesian approach.
Least‐Squares Estimation
Authors: Van De Geer Sara A. 
Published: February 2008

Abstract

The method of least‐squares is about estimating parameters by minimizing the squared discrepancies between observed data, on the one hand, and their expected values on the other. It is commonly used in the context of a regression model, where one observes pairs of variables (
X
,
Y
), with
X
, a covariable and
Y
, a response variable. The expectation of the response variable
Y
given the covariable
X
is modeled as a function
f
β
(
X
) depending on parameters β.
Time Series Analysis
Authors: Brockwell P. J. 
Published: February 2008

AbstractThe time‐domain approach to the modeling and forecasting of time series is outlined. Weakly stationary processes are introduced, and an introduction to autoregressive moving average processes and their properties is given. Estimation, model selection, and prediction with these models are considered, and extensions to multivariate time series, state‐space models, and nonlinear models are indicated.
Exponentially Weighted Moving Average (
EWMA
) Control Chart
Authors: Crowder Stephen V. , Vander Wiel Scott A. 
Published: February 2008

Abstract
An exponentially weighted moving average (EWMA) is a filter of sequential process data, giving the most recent observation the most weight and older observations exponentially less weight. EWMA control charts are especially useful in detecting small shifts in the mean or variance of a process. For processes that tend to wander over time, EWMAs are also a basic technique for predicting future observations, for making automatic adjustments to a process and for monitoring a sequence of forecast or regulation errors.
Control Charts for Attributes
Authors: Jones‐Farmer L. Allison 
Published: February 2008

Abstract

Control charts can be used to monitor characteristics of a process that are measured using categorical or attribute data. The most common attribute control charts are the
p
‐chart and
c
‐chart. This section discusses the scenarios in which the
p
‐ and
c
‐charts are useful. A detailed example of the use of a
p
‐chart is given. Strengths and weaknesses of attribute charts are discussed.
Multivariate Exponentially Weighted Moving Average (
MEWMA
) Control Chart
Authors: Conerly Michael D. 
Published: February 2008

Abstract
The multivariate exponentially weighted moving average (MEWMA) control chart is used to simultaneously monitor several correlated process variables. The primary focus of this article is monitoring the process mean by constructing an MEWMA control chart. An overview of the application and use of MEWMA control charts is discussed. The plotting statistic is clearly defined and an illustrative example is presented.
Multivariate Control Charts, Interpretation of
Authors: Mason Robert L. , Young John C. 
Published: February 2008

Abstract

Signal interpretation is straightforward when only one variable is being monitored in a process. This is because separate charts can be constructed to monitor changes in the location and/or changes in the variation of the variable of interest. For a multivariate process consisting of
p
variables, signal interpretation is more complex because the signaling statistic can potentially be a function of all
p
variables. Not only do we have to detect location and variation changes in the
p
variables, but we also have to detect changes in the relationships (i.e., correlations) among the many variables. Further, procedures that identify the particular variable or group of variables that cause or contribute to the signal are limited. In this presentation, we discuss two procedures that are useful in signal interpretation when using Hotelling's
T
2
statistic in multivariate process control and briefly describe several alternative methods.
Regression Control Charts
Authors: Shu Lianjie , Tsui Kwok‐Leung , Tsung Fugee 
Published: February 2008

Abstract
The regression control chart, which integrates regression analysis and control chart theory, has proved useful in various fields of application in the last few years. The basic idea of the regression control chart is to monitor the process output after it has been adjusted for the effect of the external covariate(s). This paper provides an overall review of regression control techniques. The formulation of the regression control chart is presented, and its computation is illustrated. Some remarks on future issues are also made.
Control Charts, Nonparametric
Authors: Chakraborti Subhabrata , Graham Marien A. 
Published: February 2008

AbstractStandard control charts are often based on the assumption that the observations follow a specific parametric distribution, such as the normal. In many applications we do not have enough information to make this assumption and in such situations, development and application of control charts that do not depend on a particular distributional assumption is desirable. Nonparametric or distribution‐free control charts can serve this broader purpose. We discuss some ideas and recent developments in the area of univariate nonparametric control charts. Most of these charts are for monitoring the location; few charts are available for scale. A key advantage of nonparametric charts is that the in‐control run length distribution is the same for all continuous process distributions. Nonparametric charts are more robust and efficient than their parametric counterparts under heavy‐tailed and/or asymmetric distributions.
Variable Sampling Rate Control Charts
Authors: Reynolds Marion R. , Stoumbos Zachary G. 
Published: February 2008

Abstract
When control charts are used to monitor a process, the traditional practice is to take samples from the process using a fixed sampling rate (FSR). Variable sampling rate (VSR) control charts vary the sampling rate as a function of the data obtained from the process. With a VSR control chart the sampling rate is increased when there is some indication of a problem with the process, and decreased when there is no indication of a problem with the process. A VSR control chart signals in the same way as a standard FSR control chart when the indication of a problem with process is strong enough. There are several approaches to implementing the VSR concept. One approach is to allow the sampling interval to vary as a function of the data. Another approach is to allow the sample size for the current sample to vary as a function of the data in previous samples. A third approach is to allow the sample size for the current sample to depend on the data in this sample. Compared to traditional FSR control charts, all of the approaches to implementing the VSR concept will produce much faster detection of most process changes.
Average Run Lengths and Operating Characteristic Curves
Authors: Human Schalk W. , Graham Marien A. 
Published: February 2008

Abstract

Control charts are typically used in establishing whether a process is in a state of statistical control or not. The performance of any control chart is an important consideration and characteristics of the run‐length distribution are generally used to assess its performance. Two popular measures of a control chart's performance are the expected value of the run‐length distribution, called the
average run length
(
ARL
), and the operating characteristic (
OC
). Both are measures of how quickly a chart will detect or react to a change in the process and can be used to compare control charting plans. Both these measures provide useful information about the operational performance of a control chart. We discuss the
ARL
and the
OC
of the well‐known Shewhart

X

chart when both the process mean and variance are known. The application of
OC
curves in acceptance sampling is also considered.
Autoregressive Integrated Moving Average (ARIMA) Modeling
Authors: Luceño Alberto , Peña Daniel 
Published: February 2008

AbstractARIMA (autoregressive integrated moving average) modeling is a method to approach the dynamic behavior of a process that generates a time series of successive valuesY′ = (y1, …,yn) of a magnitudeYobserved at equispaced periods {S, …,nS}, whereSis a sampling interval. ARIMA is composed of three parts, namely, AR, I, and MA, that can stand alone or be combined at will. Hence there are white noise models (without any part) as well as AR, I (e.g., random walk), MA, ARI, ARMA, IMA, and ARIMA models. When the I part is included, the process is nonstationary. Differentiation of a nonstationary ARIMA process leads to a stationary ARMA process. ARIMA models belong to the class of linear time series models and often assume that vectorY′ is jointly normally distributed, possibly after transformation. ARIMA modeling is usually implemented by identifying the order of the AR, I, and MA parts, estimating the AR and MA parameters, and diagnosing the adequacy of the hypotheses. Short‐term forecasting with ARIMA models is often difficult to beat in the quality context by using nonlinear and/or nonnormal models.
Process Capability, Error in Estimation of
Authors: Pearn Wen Lea , Wu Chien‐Wei 
Published: February 2008

Abstract
Process capability indices (PCIs) are widely used to provide numerical measures of process performance, which have received considerable research attention and increased usage in process assessments and purchasing decisions during the last decade. A substantial majority of capability research works that appeared in the literature do not take into account gauge measurement errors. However, the gauge measurement errors have a significant effect on estimating and assessing process capability since measurement systems are not 100% precise. An inaccurate measurement system can thwart all the benefits of improvement endeavors resulting in poor quality. Analyzing process capability without considering gauge capability may often lead to unreliable decisions. It could result in a serious loss to producers if gauge capability is not being considered in process capability estimation and testing. Since measurement errors unfortunately cannot be avoided, using appropriate confidence coefficients and power becomes an essential task. In this article, we summarize the issue on estimating and testing the process capability is the presence of measurement errors, provide a numerical example discussing the influence of measurement errors, and cite additional references for more discussions on gauge repeatability and reproducibility (GR&R) studies.