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Bu çalışmada doğrusal regresyon analizinin varsayımlarının sağlanmadığı durumlarda kullanılabilecek olan kantil regresyon analizi ile Türkiyede 2000-2020 yılları arasındaki CO2 emisyonu ve birincil enerji tüketimi arasındaki ilişkinin modellenmesi amaçlanmıştır. Çalımanın ilk böümünde uygun regresyon modeli elde edildikten sonra, veri setine aykırı değerler eklenerek bu gözlemlerin varlığında sonuçlar yeniden elde edilmiş ve kantil regresyonun performansı değerlendirilmiştir. Uygun modelin seçiminde hata kareler ortalamasının karekökü (RMSE) ve ortalama mutlak hata (MAE) kriterleri kullanılmıştır. Elde edilen sonuçlara göre; Türkiye’de 2000-2020 yılları arasında ortalama CO2 emisyonu 289.62±68.12 milyon ton ve ortalama birincil enerji tüketimi 4.66±1.21 exajoule olarak belirlenmiştir. CO2 emisyonundaki değişimin %98.74’nün birincil enerji tüketimi ile açıklandığı ve değişen varyans probleminin varlığında kantil regresyon analizinin, doğrusal regresyon analizinden daha güvenilir sonuçlar verdiği elde edilmiştir. Kantil regresyon modeline göre; birincil enerji tüketimindeki bir exajoule’luk artışın CO2 emisyonunun τ=0.50. kantilinde yaklaşık olarak 54.3 milyon ton artışa sebep olacağı belirlenmiştir. Aykırı değerlerin varlığında τ=0.25 ve τ=0.50 için elde edilen kantil regresyon modelleri en uygun modeller olup birbirine oldukça yakın sonuçlar vermiştir. Bu modellerin, CO2 emisyonu ve birincil enerji tüketimi arasındaki ilişkinin modellenmesinde çıkarsama amaçlı kullanılması önerilmektedir.

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out- of- control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent.

This paper aims to compare the performances of modified confidence intervals based on robust scale estimators with classical confidence interval for process capability index (Cp) when the process has a non-normal distribution. The estimated coverage probability and the average width of the confidence intervals were obtained by a Monte-Carlo simulation under different scenarios. Simulation results showed that the modified confidence intervals performed well in terms of coverage probability and average width for all cases. Two real-life numerical examples from industry are analyzed to illustrate the performance and the implementation of the classical and modified confidence intervals for the process capability index (Cp) which also supported the results of the simulation study to some extent. [ABSTRACT FROM AUTHOR]

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out- of- control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent. [ABSTRACT FROM AUTHOR]