Analisis Kolerasi dan Analisis Regresi

Authors

  • M Andrian Universitas Pembangunan Panca Budi, Indonesia
  • Mila Fadilah G S Universitas Pembangunan Panca Budi, Indonesia
  • Hadi Saputra Panggabean Universitas Pembangunan Panca Budi, Indonesia

DOI:

https://doi.org/10.57235/aurelia.v4i2.6787

Keywords:

correlation coefficient, linear regression, Pearson’s r, least squares, predictive modeling, data assumptions, quantitative analysis

Abstract

This study examines the essential statistical methods of correlation analysisand regression analysis, as highlighted in seminal literature indexed in Scopus. Correlation quantifies the strength and direction of linear relationships between continuous variables, typically expressed via Pearson’s r (ranging from –1 to +1) Regression analysis further extends this relationship into a predictive model through the least squares method, resulting in an equation of the form Y = mX + b, where m is the slope and b is the intercept . We emphasize the importance of verifying data assumptions (e.g., linearity, normality, homoscedasticity) before application . The synergy between correlation and regression offers both relational insight and predictive capability, demonstrating wide utility across fields such as biostatistics, social sciences, economics, and engineering

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References

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Published

2025-07-31

Issue

Vol. 4 No. 2 (2025): July 2025

Section

Articles

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