保存先:
| 第一著者: | |
|---|---|
| フォーマット: | Recurso digital |
| 言語: | |
| 出版事項: |
Zenodo
2025
|
| オンライン･アクセス: | https://doi.org/10.5281/zenodo.15749372 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
目次:
- <p><span>This study explores the role of descriptive statistical methods in analyzing and interpreting complex government budgetary systems. The research aims to evaluate the effectiveness of these methods in summarizing financial data, identifying challenges in their application, and proposing best practices to enhance budget transparency and accountability. A mixed-methods approach was employed, using secondary data from government budget reports and fiscal policy reviews (2020-2024). Statistical analysis, including measures of central tendency, dispersion, correlation, and trend analysis, was conducted using Microsoft Excel and SPSS. The findings reveal that government expenditures in key sectors, such as healthcare, education, and infrastructure, grew at average annual rates of 4.4%, 3.2%, and 5.2%, respectively, while tax revenue increased by 16% over five years. A strong positive correlation (r = 0.98) between revenue and expenditure indicates a systematic fiscal strategy aligning budget allocations with revenue growth. The study concludes that descriptive statistics significantly enhance financial transparency and policy evaluation, though their standalone use limits inferential depth. To address these gaps, governments should integrate predictive analytics, standardize financial reporting, and leverage AI-driven data visualization tools. Strengthening fiscal transparency initiatives and capacity-building programs for policymakers will further enhance data-driven decision-making.</span></p>