Deviance residual‐based control charts for monitoring the beta‐distributed processes

2021 ◽  
Author(s):  
Wook‐Yeon Hwang
Keyword(s):  
Control Charts ◽  
Distributed Processes ◽  
Deviance Residual

CUSUM control charts to monitor series of Negative Binomial count data

2015 ◽  
Vol 26 (4) ◽  
pp. 1925-1935 ◽  
Author(s):  
Airlane Pereira Alencar ◽  
Linda Lee Ho ◽  
Orlando Yesid Esparza Albarracin
Keyword(s):  
Generalized Linear Model ◽  
Control Charts ◽  
Negative Binomial ◽  
Hospital Admissions ◽  
Simulation Studies ◽  
Hospitalization Rates ◽  
The City ◽  
Cusum Control Charts ◽  
Deviance Residual ◽  
Current Proposal

To detect outbreaks of diseases in public health, several control charts have been proposed in the literature. In this context, the usual generalized linear model may be fitted for counts under a Negative Binomial distribution with a logarithm link function and the population size included as offset to model hospitalization rates. Different statistics are used to build CUSUM control charts to monitor daily hospitalizations and their performances are compared in simulation studies. The main contribution of the current paper is to consider different statistics based on transformations and the deviance residual to build control charts to monitor counts with seasonality effects and evaluate all the assumptions of the monitored statistics. The monitoring of daily number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current proposal.

Vergleichende Qualitätsmessungen Teil 2: Regelkarten

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

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.

Control charts: Minimizing fundamental attribution error in appraisal decisions

2010 ◽  
Author(s):  
Thomas H. Stone ◽  
I. M. Jawahar ◽  
Ken Eastman ◽  
Gabi Eissa
Keyword(s):  
Control Charts ◽  
Fundamental Attribution Error ◽  
Attribution Error

The Sample Mean and Control Charts

1989 ◽  
Author(s):  
Teresa M. Amabile
Keyword(s):  
Control Charts ◽  
Sample Mean ◽  
And Control

Variable Control Charts for Gumbel Distribution Based on Percentiles

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

APPLICATION OF SPC AND GROWTH MODELS ON THE ECONOMIC TRAJECTORY OF THE LEATHER AND FOOTWEAR INDUSTRY

2020 ◽  
Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic
Keyword(s):  
Human Capital ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Growth Models ◽  
Pearson Correlation ◽  
Small Companies ◽  
Statistical Process ◽  
Production Staff ◽  
Footwear Industry

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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