Kolmogorov-Smirnov test

Fatigue lifetime of HDPE structures such as pipes is recognized to show a large scatter. This study aims to compare different statistical methods and distributions, in order to give convenient modeling of tensile and fatigue test results of commercially available polyethylene compression molded sheets. The median rank, the maximum likelihood and the Kolmogorov-Smirnov fitting are compared for the estimation of Weibull parameters. The choice of the best distribution to fit fatigue lifetime is discussed on the basis of the goodness-of-fit results. It is found that whether the three-parameter distributions of Weibull and lognormal types are suitable for lifetime prediction, the two-parameter Weibull is more conservative for probabilistic fatigue design.

Purpose: The objective of the research study is to investigate whether there is a positive impact of the use of social media on undergraduate students' academic performances and their social interpersonal skills in the Prince Sattam bin Abdulaziz University. Design/methodology/approach: 64 samples were collected from full-time undergraduate students studying in Prince Sattam bin Abdulaziz University from different colleges including Community college, College of Business Administration, College of Education, College of Engineering and College of Pharmacy. The descriptive statistics analysis was used to analyze the demographic data while inferential statistics were used in testing the research hypotheses. The results obtained from the analyses were used to interpret the outcomes.

Findings: The empirical results reveal that the students enjoy meeting new friends online using social media rather than meeting in person and for this reason they spend a lot of time-addicted. It is also confirmed that the bad comments are passed easily through social media affecting other's sentiments and most of them strongly believe that all the information in social media is true and reliable and the rumors were spread easily in social media. Further, it is also confirmed that social media does not support the Learning of the students.
Practical Implications: The study confirms that the students exchange learning materials through social media and it helps them to update the developments in their college/university. The students believe that through Social media they have improved their communication skills and they can communicate with anyone at any time. It is also found that some of their teachers communicate with them and encourage them to use social media but for studying only.

Originality/value: The research work is of its first kind as it focuses on the impact of social media on the academic performances of the students studying in Prince Sattam bin Abdulaziz University, Saudi Arabia, which has suggested effective means for effective implementation of social media strategy.

El uso de pruebas no paramétricas resulta recomendable cuando los datos a analizar no cumplen los supuestos de normalidad y homocedasticidad. Sin embargo, la suposición de la normalidad de los datos o el empleo de pruebas de bondad de ajuste que no son adecuadas para el tamaño muestral empleado son aspectos habituales. Este hecho implica, en muchas ocasiones, el uso de pruebas estadísticas no ajustadas al tipo de distribución real y, consecuentemente, el establecimiento de conclusiones erróneas. Por ello, en el presente estudio se ha analizado el poder de detección de cinco pruebas de bondad de ajuste (Kolmogorov-Smirnov, Kolmogorov-Smirnov-Lilliefors,
Shapiro-Wilk, Anderson-Darling y Jarque-Bera) en distribuciones simétricas con seis tamaños muestrales entre 30 y 1000 participantes generados mediante una simulación Monte Carlo. Los resultados muestran una tendencia conservadora generalizada a medida que se incrementa el tamaño muestral. En cuanto a los tamaños muestrales, las pruebas con un mejor poder de detección de la no normalidad son Kolmogorov-Smirnov-Lilliefors y Anderson-Darling para muestra pequeñas, la prueba de Kolmogorov-Smirnov si se emplean tamaños muestrales medios (200 participantes) y la prueba de Shapiro-Wilk cuando se analizan muestras superiores a 500 participantes. Además, la prueba clásica de Kolmogorov-Smirnov se considera absolutamente ineficaz independientemente del tamaño muestral.

The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal-Wallis test and Kol-mogorov-Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km 2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate measures to avoid potential damages. Electronic supplementary material The online version of this article (https://doi.