Detection – or inspection is a part-oriented strategy that attempts to identify unacceptable output after it has been produced and separate it from the good output. Distribution – the population from which observations are drawn, categorized into cells, and form identifiable patterns. It is based on the concept of variation that states that anything measured repeatedly will arrive at different results. These results will fall into statistically predictable patterns. A bell- shaped curve (or normal distribution) is an example in which the greatest number of observations fall in the center with fewer and fewer observations falling evenly on either side of the average. Force Field Analysis – a technique developed by Kurt Lewin that displays the driving (positive) and restraining (negative) forces surrounding any change. This is displayed in a “balanced sheet” format. Frequency Distribution – a statistical table that presents a large volume of data in such a way that the central tendency (average/mean/median) and distribution are clearly displayed. Population – the universe of data under investigation from which a sample will be taken. Prevention – a future-oriented strategy that improves quality by directing analysis and action toward correcting the production process. Prevention is consistent with a philosophy of continuous or never-ending improvement. Process – the combination of people, machine and equipment, raw materials, methods and environment that produces a given product or service. Process Capability – the measured, built-in reproducibility or consistency of the product turned out by the process. Such a determination is made using statistical methods. The statistically determined pattern or distribution can only then be compared to specification limits to decide if a process can consistently deliver product within those parameters. Sample – one or more individual events or measurements selected from the output of a process for the purpose of identifying characteristics and performance of the whole. Sigma – the greek letter used to designate the estimated standard deviation. Special cause – a source of variation that is intermittent, unpredictable, unstable, sometimes called an assignable cause. Usually evident by a point beyond the control limits. Standard deviation – a measure of the spread of the process output or the spread of a sampling statistic from the process, denoted by the greek letter sigma. Statistical control – the condition describing a process from which all special causes have been removed, evidenced on a control chart by the absence of points beyond the control limits and by the absence of non-random patterns or trends within the control limits. Statistical process control – the use of statistical techniques such as control charts to analyze a process or its output so as to take appropriate action to achieve and maintain a state of statistical control and to improve the capability of the process. Stratification – the process of classifying data into subgroups based on characteristics or categories.  

Attribute – qualitative data that can be counted for

recording and analysis. These include characteristics of

occurrence, or number of times/parts installed per cycle,

etc.

Average – or the mean is the most common expression of

the centering of a distribution. It is signified by X bar and is

calculated by totaling the observed values and dividing by

the number of observations.

Bimodal Distribution – one which has two identifiable curves within it, indicating a mixture of two populations such as different shifts, machines, workers, etc. Common Cause – a source of variation that is always present; part of the random variation inherent in the process itself. Its origin can usually be traced to an element of the system which only management can correct. Control chart – a graphic representation of a characteristic of a process, showing plotted values of some statistic gathered from that characteristic, and one or two control limits. It has two basic uses: as a judgment to determine if a process was in control, and as an aid in achieving and maintaining statistical control. Control Limit – a line (or lines) on a control chart used as a basis for judging the significance of the variation from subgroup to subgroup. Variation beyond a control limit is evidence that special causes are affecting the process. Control limits are calculated from process data and are not to be confused with engineering specifications. Range – a measure of variation in a set of data. It is calculated by subtracting the lowest value in the data set from the highest value in the same data set. Runs – the patterns in a run chart or control chart within which a number of points line up on only one side of the central line. Beyond a certain number of consecutive points, the pattern becomes unnatural and requires attention. Trends – the patterns in a run chart of control chart that feature the continued rise of fall of a series of points. Like runs, attention should be paid to such patterns when they exceed a predetermined number. Variables – characteristics of a part which can be measured such as length, id/od, width, etc. Variation – the inevitable difference among individual outputs of a process. The sources of variation are grouped into two major classes: common causes and special causes.