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Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A

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The Bayesian approach offers a more reliable method for multiple linear regression analysis compared to frequentist methods. This statistical technique provides a richer understanding of influencing factors for response variables.

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Area of Science:

  • Statistics
  • Biostatistics

Background:

  • Frequentist and Bayesian statistical approaches are commonly used for regression modeling.
  • Identifying influencing factors for a response variable is crucial in statistical analysis.

Purpose of the Study:

  • To demonstrate the superiority of the Bayesian approach over frequentist methods in multiple linear regression.
  • To enhance the identification of influencing factors for a response variable.

Main Methods:

  • A survey of 310 respondents in Puducherry was conducted.
  • Body Mass Index (BMI) was the response variable, with predictors including age, weight, gender, job nature, and marital status.
  • Jeffreys's Amazing Statistics Program (JASP) was used for analysis, comparing conventional multiple linear regression with Bayesian linear regression.

Main Results:

  • Bayesian linear regression provides posterior distributions for regression coefficients, unlike the single values from frequentist methods.
  • The Bayesian approach allows for model selection using posterior probabilities and provides inclusion probabilities for predictors.

Conclusions:

  • The Bayesian framework offers a more comprehensive set of results for regression coefficients.
  • This approach enhances the quality and reliability of statistical investigation outputs for scientific problems.