The Economic and Economic-Statistical Designs of the Synthetic Chart for the Coefficient of Variation

2017 ◽  
Vol 46 (3) ◽  
pp. 20160500
Author(s):  
C. Y. Wai ◽  
L. L. Sok ◽  
M. B. C. Khoo ◽  
H. C. Ming ◽  
A. L. J. Xiong
Keyword(s):  
Coefficient Of Variation ◽  
Statistical Designs ◽  
Synthetic Chart

scholarly journals Optimal designs of the side sensitive synthetic chart for the coefficient of variation based on the median run length and expected median run length

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255366
Author(s):  
Waie Chung Yeong ◽  
Ping Yin Lee ◽  
Sok Li Lim ◽  
Peh Sang Ng ◽  
Khai Wah Khaw
Keyword(s):  
Optimal Design ◽  
Coefficient Of Variation ◽  
Average Run Length ◽  
Length Distribution ◽  
Median Number ◽  
False Alarms ◽  
Actual Performance ◽  
Run Length ◽  
Run Length Distribution ◽  
Synthetic Chart

The side sensitive synthetic chart was proposed to improve the performance of the synthetic chart to monitor shifts in the coefficient of variation (γ), by incorporating the side sensitivity feature where successive non-conforming samples must fall on the same side of the control limits. The existing side sensitive synthetic- γ chart is only evaluated in terms of the average run length (ARL) and expected average run length (EARL). However, the run length distribution is skewed to the right, hence the actual performance of the chart may be frequently different from what is shown by the ARL and EARL. This paper evaluates the entire run length distribution by studying the percentiles of the run length distribution. It is shown that false alarms frequently happen much earlier than the in-control ARL (ARL0), and small shifts are often detected earlier compared to the ARL1. Subsequently, this paper proposes an alternative design based on the median run length (MRL) and expected median run length (EMRL). The optimal design based on the MRL shows smaller out-of-control MRL (MRL1), which shows a quicker detection of the out-of-control condition, compared to the existing design, while the results from the optimal design based on the EMRL is similar to that of the existing designs. Comparisons with the synthetic-γ chart without side sensitivity shows that side sensitivity reduces the median number of samples required to detect a shift and reduces the variability in the run length. Finally, the proposed designs are implemented on an actual industrial example.

An Optimal Design of the Synthetic Coefficient of Variation Chart Based on the Median Run Length

2020 ◽  
pp. 2150018
Author(s):  
Wai Chung Yeong ◽  
Sok Li Lim ◽  
Michael Boon Chong Khoo ◽  
Khai Wah Khaw ◽  
Peh Sang Ng
Keyword(s):  
Coefficient Of Variation ◽  
Average Run Length ◽  
Moving Average ◽  
Small Sample ◽  
False Alarms ◽  
Small Shift ◽  
Run Length ◽  
Ewma Chart ◽  
Synthetic Chart ◽  
Run Lengths

The synthetic coefficient of variation (CV) chart is currently evaluated based only on the average run length (ARL), but this paper evaluates the chart based on different percentiles of the run length, which shows that false alarms frequently happen earlier than that shown by the in-control ARL (ARL[Formula: see text], and for small sample sizes and shift sizes, the out-of-control condition is frequently detected before what is shown by the out-of-control ARL (ARL[Formula: see text]. Furthermore, the run lengths show large variations. Hence, the chart’s performance could not be interpreted only in terms of the ARL. This paper proposes the median run length (MRL)-based design for the synthetic CV chart, which is not available in the literature. The MRL-based design shows larger MRL0 and ARL0, smaller MRL1 and ARL1, and less variation in the out-of-control run lengths compared to existing ARL-based designs. However, the in-control run lengths show more variation. Comparisons show that the synthetic chart outperforms the VSS and Shewhart charts, while comparison with the Exponentially Weighted Moving Average (EWMA) chart shows that although it outperforms the synthetic chart based on the ARL for small shift sizes, the synthetic chart shows better performance in terms of the MRL. The MRL-based synthetic chart is then implemented on an industrial example.

400-P: High Levels of Glycemic Coefficient of Variation Are Associated with Higher Hypoglycemia Episodes in T1D Adults in a Brazilian Tertiary Hospital

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 400-P
Author(s):  
THAIS B. BRASIL ◽  
ANDREI C. SPOSITO ◽  
BEATRIZ ADACHI ◽  
WALKYRIA M. VOLPINI ◽  
ELIZABETH J. PAVIN
Keyword(s):  
Coefficient Of Variation ◽  
Tertiary Hospital

KELAYAKAN DAN RISIKO USAHATANI JERUK KEPROK MADURA DI KABUPATEN SUMENEP

2013 ◽  
Vol 3 (2) ◽  
Author(s):  
Isdiantoni Isdiantoni
Keyword(s):  
Discount Rate ◽  
Coefficient Of Variation

Menurut Direktorat Budidaya Tanaman Buah Deptan (2009), potensi pengembangan tanaman jeruk keprok Madura di Kabupaten Sumenep, cukup besar yaitu seluas 400 hektar yang tersebar di 3 (tiga) kecamatan, yaitu Kecamatan Dasuk, Kecamatan Ambunten dan Kecamatan Pasongsongan. Salah satu faktor yang dapat menenunjang keberhasilan pengembangan komoditas jeruk ini, adalah kelayakan ekonomis (menguntungkan secara finansial).Dipihak lain, petani sebagai pelaku utama kegiatan pengembangan jeruk keprok Madura dan sebagai produsen, harus mengetahui kemungkinan resiko yang akan diterimanya dan besarnya keuntungan dari usaha ini. Pengetahuan terhadap hubungan antara resiko dan keuntungan ini, akan memberikan dasar pertimbangan yang rasional bagi petani dalam mengembangkan komoditas jeruk keprok Madura. Informasi/data pada penelitian ini, diperoleh dari petani jeruk keprok Madura yang bibitnya berasal dari cangkokan dan mulai dibuahkan pada umur 3 (tiga) tahun.Pengukuran kelayakan finansial usahatani jeruk keprok Madura dilakukan dengan melihat kriteria investasi, dan pengukuran terhadap hubungan antara tingkat resiko dengan keuntungan, diukur secara statistik dengan melihat koefisien variasi (coefficient of variation) dan batas bawah keuntungan. Kriteria investasi pada usahatani jeruk keprok Madura menunjukkan nilai NPV sebesar Rp. 118,342,271 (> 0), Net B/C sebesar 1.38 (> 1) dan IRR sebsar 23,7% (> discount rate), sehingga proyek usahatani jeruk keprok Madura dapat dikatakan go! (layak dilaksanakan).Periode yang diperlukan untuk menutup biaya investasi, yaitu 9 tahun 10 bulan (di bawah dari umur ekonomis proyek), sehingga proyek ini layak diusahakan. Selama periode proyek (15 tahun) nilai koefisien variasi (CV) didapatkan 0.588 (CV > 0.5) dan nilai batas bawah keuntungan (L) didapatkan sebesar Rp. (31,204,042) yang menunjukkan L < 0.  Dengan demikian, pengusahatani jeruk keprok Madura harus berani menanggung resiko (kerugian) sebesar  Rp. 31,204,042,- pada setiap proses produksi. Kata kunci: Usahatani Jeruk Keprok Madura, Kelayakan, dan Resiko Finansial

Research of the activity and viability of spermatozoa at different concentrations and proportions of diluents after incubation

2020 ◽  
pp. 88-95
Author(s):  
Svitlana Lobchenko ◽  
Tetiana Husar ◽  
Viktor Lobchenko
Keyword(s):  
Plasma Concentration ◽  
Incubation Time ◽  
Coefficient Of Variation ◽  
Incubation Medium ◽  
Wide Range ◽  
Series Of Experiments ◽  
The Subject ◽  
Boar Spermatozoa ◽  
Native Plasma ◽  
Better Than

The results of studies of the viability of spermatozoa with different incubation time at different concentrations and using different diluents are highlighted in the article. (Un) concentrated spermatozoa were diluented: 1) with their native plasma; 2) medium 199; 3) a mixture of equal volumes of plasma and medium 199. The experiment was designed to generate experimental samples with spermatozoa concentrations prepared according to the method, namely: 0.2; 0.1; 0.05; 0.025 billion / ml. The sperm was evaluated after 2, 4, 6 and 8 hours. The perspective of such a study is significant and makes it possible to research various aspects of the subject in a wide range. In this regard, a series of experiments were conducted in this area. The data obtained are statistically processed and allow us to highlight the results that relate to each stage of the study. In particular, in this article it was found out some regularities between the viability of sperm, the type of diluent and the rate of rarefaction, as evidenced by the data presented in the tables. As a result of sperm incubation, the viability of spermatozoa remains at least the highest trend when sperm are diluted to a concentration of 0.1 billion / ml, regardless of the type of diluent used. To maintain the viability of sperm using this concentration of medium 199 is not better than its native plasma, and its mixture with an equal volume of plasma through any length of time incubation of such sperm. Most often it is at this concentration of sperm that their viability is characterized by the lowest coefficient of variation, regardless of the type of diluent used, which may indicate the greatest stability of the result under these conditions. The viability of spermatozoa with a concentration of 0.1 billion / ml is statistically significantly reduced only after 6 or even 8 hours of incubation. If the sperm are incubated for only 2 hours, regardless of the type of diluent used, the sperm concentrations tested do not affect the viability of the sperm. Key words: boar, spermatozoa, sperm plasma, concentration, incubation, medium 199, activity, viability, rarefaction.

Design and development of manually operated gladiolus planter

2018 ◽  
Vol 5 (01) ◽  
Author(s):  
TAPAN K. KHURA ◽  
H. L. KUSHWAHA ◽  
SATISH D LANDE ◽  
PKSAHOO . ◽  
INDRA L . KUSHWAHA
Keyword(s):  
Coefficient Of Variation ◽  
Field Capacity ◽  
Power Source ◽  
Forward Speed ◽  
Cereal Crop ◽  
Maximum Coefficient ◽  
Coefficient Of Uniformity ◽  
The Mean ◽  
The Cost ◽  
Immense Potential

Floriculture is an age-old farming activity in India having immense potential for generating selfemployment and income to farmers. However, the cost of cultivation of flower is high as compared to cereal crop. Level of mechanization for different field operations is one but foremost reason for the higher cost of cultivation. As most of the Indian farmers are marginal and small, a need for manually operated gladiolus planter was felt. The geometric properties of gladiolus corm were determined for designing the seed metering system and seed hopper of the planter. The planter was evaluated in the field when pulled by two persons as a power source and guided by a person. The coefficient of variation and highest deviation from the mean spacing was observed as 12.93% and 2.65cm respectively. The maximum coefficient of uniformity of 90.59% was observed for a nominal corm spacing of 15cm at 0.56 kmh-1 forward speed. An average MISS percentage was observed as 2.65 and 2.25 for nominal corm spacing of 15 and 20 cm. The multiple index was zero for two levels corm spacing and forward speed of operation. The QFI was found in the range of 97.2 and 97.9 percent. The average field capacity of the planter was observed as 0.02 hah-1.The average draft requirement of the planter was found as 821 ± 50.3 N.

scholarly journals Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

2019 ◽  
Vol 23 (10) ◽  
pp. 4323-4331 ◽  
Author(s):  
Wouter J. M. Knoben ◽  
Jim E. Freer ◽  
Ross A. Woods
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Ad Hoc ◽  
Model Performance ◽  
Technical Note ◽  
Mean Flow ◽  
Model Adequacy ◽  
Robust Model ◽  
The Mean ◽  
Adequacy Assessment

Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

Sign in / Sign up

Export Citation Format

Share Document