使用判别分析对苏丹辛纳尔州家庭收入进行分类(2021)

Abdalrahim Ahmed Gissmalla, A. A. Ahmed
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引用次数: 0

摘要

这项研究的目的是区分收入充足和不足,并确定影响收入的最具歧视性的因素。这些数据是通过向户主发放的结构化问卷从辛纳尔的家庭中获得的,其中(800)户(417户)收入充足,(383户)收入不足。利用SPSS软件进行判别分析和决策树分析。结果表明,所应用的判别模型与从样本中获得的数据拟合良好,判别中使用的24个变量中有7个具有统计学意义。最重要的判别变量是对生活水平的评估和为支付家庭生活费用而借款。研究表明,与可能误差不超过14.5%的决策树相比,判别函数模型特异性的可能误差不大于14.2%。该研究建议使用统计判别模型来区分收入充足和收入不足,并使用决策树来对Sinnar根据收入。
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Using the Discriminant Analysis to Classify the Income of Households in Sinnar State, Sudan (2021)
The purpose of this study was to distinguish between sufficient and insufficient income and to identify the most discriminating factors that influence income. The data was obtained from households in Sinnar through a structured questionnaire addressed to the heads of families, a sample of (800) households (417) had sufficient incomes, and (383) had insufficient incomes. Discriminate analysis and decision trees were applied with the help of the (SPSS) program. The results suggested that the discrimination model applied had a good fit with the data obtained from the sample and that 7 of the 24 variables used in discrimination were statistically significant. The most important discriminating variables were the evaluation of the standard of living and borrowing to cover the family's living expenses. The research showed that the possible error in discriminate function model specificity does not exceed 14.2% compared to decision trees where the possible error does not exceed 14.5%. The research study recommended the use of a statistical discrimination model to discriminate between a sufficient income and insufficient income and the use of decision trees to classify the administrative unit of Sinnar according to income.
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