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Logistic Regression A Self Learning Text

This chapter surveys the role of the IEA as a key component of the international energy security system. First, it gives an overview of the nature of the energy security challenges and the key governance regimes that have been set up historically to manage these challenges, including the IEA. Then, it elaborates on the specific roles taken up by the IEA in global energy governance, particularly in light of the dramatic changes that characterize the global energy security system. Finally, it gives a comparative overview of the three oil market interventions that the IEA has so far coordinated, before presenting the conclusions.

The international energy agency

Smoothing Forecasting And Prediction Of Discrete Time Series

Background: the COVID-19 has been successfully contained in China but is spreading all over the world. We use phenomenological models to dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We use the experience from China to analyze the calibration results on Japan, South Korea, Iran, Italy and Europe, and make future scenario projections.

Methods: we calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries and Europe as a whole. The different models provide upper and lower bounds of our scenario predictions.

Results: We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogenous situations across provinces. Based on Chinese experience, we identify a high risk in Japan with estimated total confirmed cases as of March 25 being 1574 (95% CI: [880, 2372]), and 5669 (95% CI: [988, 11340]) by June. For South Korea, we expect the number of infected cases to approach the ceiling, 7928 (95% CI: [6341, 9754]), in 20 days. We estimate 0.15% (95% CI: [0.03%, 0.30%]) of Italian population to be infected in a positive scenario. We would expect 114867 people infected in Europe in 10 days, in a negative but probable scenario, corresponding to 0.015% European population.

Conclusions: The extreme containment measures implemented by China were very effective with some instructive variations across provinces. For other countries, it is almost inevitable to see the continuation of the outbreak in the coming months. Japan and Italy are in serious situations with no short-term end to the outbreak to be expected. There is a significant risk concerning the upcoming July 2020 Summer Olympics in Tokyo. Iran9s situation is highly uncertain with unclear and negative future scenarios, while South Korea is approaching the end of the outbreak. Both Europe and the USA are at early stages of the outbreak, posing significant health and economic risks to the world in absence of serious measures.

Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review

http://www.ein.org.pl/sites/default/files/2019-01-01.pdf

Wear of brake system components in various operating conditions of vehicle inthe transport company

Abstract Transport companies operate in a dynamically developing and competitive market. Maintaining the already held position and further expansion requires adjusting the level of services provided to the needs and requirements of customers, as well as continuous surveying, monitoring and adjusting the implemented strategy. There are different methods for such an analysis. This article proposes logistic regression. The research was conducted on the basis of a distribution and trade company dealing with the supply of automotive spare parts. As the most profitable group of customers are local car repair shops, it was this group that was subject to analysis. The quality of service assessment was considered from the point of view of delivery time. The dichotomous form of the predictor taking two values - late and on-time delivery - was determined. From among the possible ones, regressors whose influence was statistically significant and whose modification was possible were selected. The research showed which of them (and how strongly) affect the dependent variable, which allowed for modification of strategy and implementation of new solutions increasing the number of satisfied customers.

/pdf/logistic-regression-in-modeling-and-assessment-of-transport-1hsxj7hgng.pdf

Logistic regression in modeling and assessment of transport services

Laboratory measurements of vehicle exhaust emissions in conditions reproducing real traffic

Abstract The steady increase in the number of road users and their growing mobility mean that the issue of road safety is still a topical one. Analyses of factors influencing the number of road traffic accidents contribute to the improvement of road safety. Because changes in traffic volume follow a daily rhythm, hour of the day is an important factor affecting the number of crashes. The present article identifies selected mathematical models which can be used to describe the number of road traffic accidents as a function of the time of their occurrence during the day. The study of the seasonality of the number of accidents in particular hours was assessed. The distributions of the number of accidents in each hour were compared using the Kruskal-Wallis and Kolmogorov-Smirnov tests. Multidimensional scaling was used to present the found similarities and differences. Similar hours were grouped into clusters, which were used in further analysis to construct the ARMAXmodel and the Holt-Winters model. Finally, the predictive capabilities of each model were assessed.

/pdf/predictive-analysis-of-the-impact-of-the-time-of-day-on-road-3eorvdm75m.pdf

Predictive analysis of the impact of the time of day on road accidents in Poland

http://www.ein.org.pl/sites/default/files/2020-02-02.pdf

Application of the logistic regression for determining transition probability matrix of operating states in the transport systems

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