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[An overview of multiple linear regression model and its application].

Y H Hu1, S C Yu, X Qi

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|June 11, 2019
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Summary

This article explains Multiple Linear Regression (MLR), a key multivariate statistical analysis tool. It covers MLR principles, applications, conditions, and alternatives to enhance data utilization and statistical analysis efficiency.

Keywords:
Linear modelsModels, statisticalRegression analysis

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Area of Science:

  • Statistics
  • Data Analysis
  • Multivariate Analysis

Background:

  • Multiple Linear Regression (MLR) is a fundamental statistical technique.
  • It extends simple linear regression for analyzing multiple independent variables.
  • MLR is widely used in various fields for its predictive capabilities.

Purpose of the Study:

  • To provide a practical introduction to the Multiple Linear Regression (MLR) model.
  • To elucidate the core principles, applications, and diagnostic criteria of MLR.
  • To explore extensions and alternative methods for situations where MLR assumptions are not met.

Main Methods:

  • The article systematically explains the basic principles of MLR.
  • It includes practical application examples to illustrate its use.
  • It details the conditions for MLR application and methods for diagnosing model fit.

Main Results:

  • A comprehensive understanding of MLR's foundational concepts and practical implementation.
  • Guidance on assessing the suitability and diagnosing issues within MLR models.
  • Knowledge of alternative statistical methods for scenarios violating MLR assumptions.

Conclusions:

  • Effective application of MLR improves data utilization and statistical analysis efficiency.
  • Understanding MLR's conditions and diagnosis is crucial for reliable research outcomes.
  • Awareness of alternative methods ensures robust analysis even when MLR assumptions are violated.