Attribute data

Are the acceptance criteria for attribute data sampling set at zero defects ... 

The inherent limitations of attribute data prevent their use for preliminary statistical studies since specification values are not measured. Attribute data have only two values (conforming/nonconforming, pass/fail, go/no-go, present/absent) but they can be counted, analyzed, and the results plotted to show variation. Measurement can be based on the fraction defective, such as parts per million (PPM). While variables data follows a distribution curve, attribute data varies in steps since you can t count a fraction. There will either be zero errors or a finite number of errors. 

With attribute data the product either has or has not the ascribed attribute - it can therefore either pass or fail the test. There are no gray areas. Attributes are measured on a go or no-go basis. With variables, the product can be evaluated on a scale of measurement. However, with inspection by attributes we sometimes use an acceptable quality level (AQL) that allows us to ship a certain percent defective in a large batch of product -... 

Where you have required your subcontractors to send a certificate of conformity (CofC) testifying the consignment s conformity with the order, you cannot omit all receiving checks. Once supplier capability has been verified, the C of C allows you to reduce the frequency of incoming checks but not to eliminate them. The C of C should be supported with test results. Therefore you need to impose this requirement in your purchasing documents. However, take care to specify exactly what test results you require and in what format you require them presented, as you could be provided with attribute data when you really want variables data. 

Don t apply AQLs to attribute data - set the standard as zero defects. 

Characteristic—A distinguishing feature of a process or its output on which variable or attribute data can be collected. 

There are numerous approaches to the problem of capturing all the information in a set of multi endpoint data. When the data are continuous in nature, approaches such as the analog plot can be used (Chemoff, 1973 Chambers et al., 1983 Schmid, 1983). A form of control chart also can be derived for such uses when detecting effect rather than exploring relationships between variables is the goal. When the data are discontinuous, other forms of analysis must be used. Just as the control chart can be adapted to analyzing attribute data, an analog plot can be adapted. Other methods are also available. 

Acceptable quality characteristics, or specifications, are generally described in terms of discrete or attribute data (e.g., pass/fail or no unit outside 75% to 125%) and are inappropriately referred to as zero defect or tolerance (since these are for the sample tested). 

Control charts based on attribute data include the p chart, np chart, c chart, and chart. The former two are applied when fraction nonconforming or number of non-conforming is a concern, and the latter two are used to deal with the nonconformities. Most pharmaceutical manufacturing industries employ one or more of these charts. 

Examination of archaeological textile evidence includes analysis of two classes of evidence to yield what is referred to as attribute data. These data are derived from (1) technical fabrication examinations of the fiber-yam-fabric evidence and (2) the physical and chemical analyses of the fiber. Both classes of information are necessary to characterize fully the fabric evidence, and both require photomicrography as an initial step in the analytical procedure. The nature of the information sought leads to different avenues of testing. Because both are essential to the complete understanding of the textile, the sets of results obtained in the testing complement each other. 

Sampling. A sample was drawn at random from a list of randomly combined x and y coordinates that indicated location points for pseudo-morphic evidence. The location point is defined as the field of vision at 10 X magnification that allowed attribute data occurring in the field to be observed. The spearpoint was placed upon a special microscope stage that had a centimeter grid system. The x axis was identified by letters A-Y, and the y axis was identified by numbers 1-10. These combinations of coordinates led to the spatial location points or subunits of the site. Photomicrography was used to record the mineralized fabric attributes at 101 location points. 

Attribute data were identified from the photomicrographs of each location point in the sample, and each potential categorical variable (attribute) was recorded as present or absent. Because it was desirable to determine whether the evidence at the location points was related, the data were subjected to hierarchical clustering. The measure of dissimilarity used in the project was the number of matches among attribute measurements that two location points shared. For example, two points had a dissimilarity of 0 if they matched on all attribute measurements, and at the other extreme, the two location points had a dissimilarity of 13 (the total number of measured attributes) if they did not match on any of the measurements. Each match was weighed as equally important. In addition to this intuitive measure of... 

Process data is either quantitative or qualitative in nature. Quantitative data, or variable data, is measured along a continuous scale (i.e., 1 to 60 seconds). Qualitative data, or attribute data, is measured in categories, like pass/fail, yes/no, blue/green, and so on. Both types of data have value, but usually variable data is preferred over attribute data because it tells you more about the process. 

The general sequence for constructing Process Behavior Charts is the same for all types of data, but there is some variation depending upon whether attribute data (data you can count) or variable data (data on a scale) are involved. We ll show you the steps and some details for each type of Process Behavior Chart. 

The reliable, robust classification of diseases or disease states via biomedical spectroscopy requires special methodology that can handle complex data. In most cases, such data defy simple analyses that assume the presence of easily identified features ( markers ) in the data set. In particular, the methodology must be able to handle data sets that contain relatively few spectra (in the 50s) but many attributes (data points) per spectrum (in the 1000s) ideally, it should also provide some measure of the degree of confidence in a given diagnosis. 

Often times, the amount of attribute data needed to calculate a statistically significant proportion (e.g., fraction defective) is rather large. In this case, the sample size will be determined using MINITAB sofl ware. Assuming we are interested in detecting a difference between a sample that is 4% defective and a sample that is 1% defective (note this is a decision that the experimenter and/or company management must make), the MINITAB Power and Sample Size calculator for two proportions can be used. This results in a sample size of 568 parts per run that will have to be inspected for cracks. Fortunately, for this particular case, the visual inspection of each part takes only a few seconds to complete. So the inspection of 568 parts actually takes less than one hour. 

The different kinds of control charts are based on two groupings of types of data attribute data and variable data. Attribute data includes classification, count, and rank data Variable data refers primarily to continuous data, but rank data are often analyzed using a variable-control chart (realizing that the arithmetic functions are not theoretically valid). Otherwise the ranks can be converted to classification data and analyzed using attribute charts. Figure 8 contains examples of each of these categories of data. 

Continuous data can be converted to attribute data by applying an operational definition for the count or classification. A recorded dimension can be classified as meeting or not meeting the specification however, this conversion does not work in reverse. The measured dimensions tue unknown for a part... 

Eigure 14 Two Types of Attribute Data. (Copyright 1980-1998 Associates in Process Improvement)... 

The two basic types of attribute data were discussed in Section 4.3 ... 

To develop an attribute control chart, a subgrouping strategy must first be determined. The subgroup size (n) is the number of units tested for classification data, or the area of opportunity for the incidence to occur for count data. There are four commonly used control charts for attribute data, depending on the type of attribute data and the constancy of the subgroup size. Table 1 summarizes these charts. 

Chart Name Type of Attribute Data Statistic Charted Subgroup Size... 

CHARTS 3.1. Data Patterns on Control 1861 5.1. Attribute Data 5.2. Control Chart for Percent 1871... 

To monitor the variation of a process or service, data are collected and analyzed for product critical performance metrics. Two types of data are common. Data may be measured on a continuous scale, for example, length, weight, and so on. Such measurements are called variable data. Alternatively, if process observations are of the classification type, they are called attribute data. Examples... 

In Section 4, two control charts for variable data are presented. Both the X control chart for monitoring the process mean and the R control chart for monitoring the process variation are presented. In Section 5, two control charts for attribute data are discussed the p control chart for monitoring percent nonconforming and the c chart for monitoring the number of defects in a sample. Furthermore, brief discussions of data patterns on control charts and recommended supplemental rules for judging nonrandom trends on a control chart are presented. 

CONTROL CHARTS FOR ATTRIBUTE DATA 5.1. Attribute Data... 

At this point, we have reviewed X and R control charts. Both of these charts are used for variable data. Many more control charts exist for varying conditions for variable data, as described above. In this section, two control charts are presented that are most useful in monitoring attribute data. In particular, a control chart known as the p chart is presented to provide a tool for monitoring the... 

Many statistical tools have been developed to control critical process parameters. The most commonly used is the control chart, which is an effective way to monitor and control processes and can be defined for both vtuiables and attributes data. The selection of variables data will typically make basic statistical tools more efficient (i.e., lower sample size requirements to achieve necessary confidence levels). 

Asynchronous transfer mode (ATM), 250 ATC, see Apparent tardiness cost ATM (Asynchronous transfer mode), 250 ATO, see Assemble to order ATP (available to promise), 2046 Attention, limited-resource model of, 1016 AT T Laboratories, 268, 913 Attribute control charts, 1844-1851 Attribute data, 1856-1857 Attribute modeling, 2279—2280 AT T runs rules, 1863—1868 Attributes ... 

---