Analysis of the Influence of Factors on Flight Delays in the United States Using the Construction of a Mathematical Model and Regression Analysis

T. Kireev, V. Kukartsev, Alesya Pilipenko, A. Rukosueva, V. Suetin
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引用次数: 9

Abstract

The paper complied and structured data to construct a correlation model. The reasons for flight delays and cancellations between 2009 from 2019 are examined, based on the statistical electronic source "Bureau of Transportation Statistics". The normalized data are calculated from the primary data. Based on the data collected, a primary histogram was constructed (outliers were not checked). In addition, significant and weak influencing factors have been identified for the constructing of a correlation table, this table helps to identify an additional factor influencing a flight delay or cancellation. Based on the data from the table, a new histogram was built and a correlation table was constructed, allowing regression analysis to be started. At the beginning of the regression analysis, regression statistics are used and an analysis of variance is performed, then the regression analysis is performed directly and the results of the regression are summed up. Based on the summed-up results, a histogram was built, which takes into account all the statistics and calculations, showing the predicted number of delays or cancellations of flights and their actual number.
基于数学模型构建与回归分析的美国航班延误影响因素分析
本文对数据进行整理和结构化,构建相关模型。根据统计电子来源“交通统计局”,对2009年至2019年期间航班延误和取消的原因进行了审查。归一化数据由原始数据计算得出。根据收集到的数据,构建初级直方图(不检查异常值)。此外,我们还确定了显著影响因素和弱影响因素,构建了相关表,该表有助于识别影响航班延误或取消的额外因素。根据表中的数据,构建新的直方图和相关表,开始进行回归分析。在回归分析开始时,使用回归统计并进行方差分析,然后直接进行回归分析并总结回归结果。根据总结的结果,建立了一个直方图,它考虑了所有的统计和计算,显示了航班延误或取消的预测数量和它们的实际数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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