Scholarlink

<p>To understand divorce, a quantitative method is used which considers the social, economic, and psychological elements impacting marital changes. We introduce a model based on Ordinary Differential Equations (ODEs), combined with statistical hypothesis testing, to examine divorce trends over two decades using longitudinal, real-world data. Model parameters are estimated through nonlinear least-squares fitting, resulting in a high predictive accuracy <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="42452_2025_7205_Article_IEq1.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="99" /> </InlineMediaObject> <EquationSource Format="TEX">\((R^2 = 0.9878)\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.9878</mn> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation>, indicating the model’s dependability. Robustness is further confirmed through residual analysis, Durbin-Watson (DW), Jarque-Bera (JB) statistics, and normality testing. Consequently, the results provide important understandings of how divorce trends are changing, supplying a data-supported basis for policymakers and researchers to develop helpful intervention strategies to foster marital stability and lower divorce rates.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating divorce dynamics through ODE modeling and statistical hypothesis testing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月3日

查看全文
  • Ausif Padder,
  • Sania Qureshi,
  • Amanullah Soomro,
  • Fozia Shaikh,
  • Evren Hincal,
  • Chih-Wen Chang

摘要

To understand divorce, a quantitative method is used which considers the social, economic, and psychological elements impacting marital changes. We introduce a model based on Ordinary Differential Equations (ODEs), combined with statistical hypothesis testing, to examine divorce trends over two decades using longitudinal, real-world data. Model parameters are estimated through nonlinear least-squares fitting, resulting in a high predictive accuracy \((R^2 = 0.9878)\) ( R 2 = 0.9878 ) , indicating the model’s dependability. Robustness is further confirmed through residual analysis, Durbin-Watson (DW), Jarque-Bera (JB) statistics, and normality testing. Consequently, the results provide important understandings of how divorce trends are changing, supplying a data-supported basis for policymakers and researchers to develop helpful intervention strategies to foster marital stability and lower divorce rates.