Run Charts, Control Charts, Statistical Sampling, and Percent within Limits

2021 ◽  
pp. 267-303
Author(s):  
J. J. Hren ◽  
S. D. Walck

The field ion microscope (FIM) has had the ability to routinely image the surface atoms of metals since Mueller perfected it in 1956. Since 1967, the TOF Atom Probe has had single atom sensitivity in conjunction with the FIM. “Why then hasn't the FIM enjoyed the success of the electron microscope?” The answer is closely related to the evolution of FIM/Atom Probe techniques and the available technology. This paper will review this evolution from Mueller's early discoveries, to the development of a viable commercial instrument. It will touch upon some important contributions of individuals and groups, but will not attempt to be all inclusive. Variations in instrumentation that define the class of problems for which the FIM/AP is uniquely suited and those for which it is not will be described. The influence of high electric fields inherent to the technique on the specimens studied will also be discussed. The specimen geometry as it relates to preparation, statistical sampling and compatibility with the TEM will be examined.


Pflege ◽  
2013 ◽  
Vol 26 (2) ◽  
pp. 119-127 ◽  
Author(s):  
Jan Kottner ◽  
Armin Hauss
Keyword(s):  

Vergleichende Qualitätsmessungen und Beurteilungen spielen in der Pflege eine zunehmend wichtige Rolle. Qualitätskennzahlen sind von systematischen und zufälligen Fehlern beeinflusst. Eine Möglichkeit, mit zufälliger Variation in Kennzahlenvergleichen adäquat umzugehen, bietet die Theorie der Statistischen Prozesskontrolle (SPC). Im vorliegenden Beitrag werden Regelkarten (control charts) als Werkzeuge der SPC vorgestellt. Es handelt sich dabei um grafische Darstellungen von Qualitätskennzahlen im zeitlichen Verlauf. Attributive Merkmale können mithilfe von p-, u- und c-Regelkarten dargestellt werden. Es gibt eine Reihe von Regeln, mit denen spezielle Variationen (special cause variation) innerhalb des betrachteten Prozesses identifiziert werden können. Finden sich im Diagramm keine Hinweise auf nichtzufällige Variationen, geht man davon aus, dass sich der Prozess innerhalb «statistischer Kontrolle» befindet (common cause variation). Eine Abweichung eines Datenpunktes um mehr als drei Standardabweichungen vom Mittelwert aller vorliegenden Datenpunkte gilt als stärkstes Signal nicht zufallsbedingter Variation. Im Qualitätsmanagementkontext sind Regelkarten für die dynamische Messung von Prozessen und Ergebnissen und deren Beurteilungen traditionellen Mittelwerts- und Streuungsvergleichen überlegen.


2010 ◽  
Author(s):  
Thomas H. Stone ◽  
I. M. Jawahar ◽  
Ken Eastman ◽  
Gabi Eissa

2002 ◽  
Vol 231 ◽  
pp. 309-314 ◽  
Author(s):  
CH Peterson ◽  
LL McDonald ◽  
RH Green ◽  
WP Erickson

2018 ◽  
Vol 9 (12) ◽  
pp. 1890-1897
Author(s):  
K. Rosaiah ◽  
B. Srinivasa Rao ◽  
J. Pratapa Reddy ◽  
C. Chinnamamba

Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.


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