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Multiple Linear Regression

In: Fundamentals of Statistical Inference

Author

Listed:
  • Konstantin M. Zuev

    (California Institute of Technology, Department of Computing and Mathematical Sciences)

Abstract

The behavior of complex systems often depends on many different factors. For example, the level of blood cholesterol depends on age, diet, physical exercise, and various genetic factors. A stock price tomorrow may depend on its price over the last several days, the company’s performance measures, and overall economic and financial factors. For most real systems, it is impossible to account for all factors that affect its behavior. That is why we try to identify the most important factors, or simply factors that are available for measurements, and build a stochastic model of the system with the hope that it will mimic the behavior of the real system adequately, at least to some extent. In this final chapter, we will discuss the multiple linear regression model, which is an extension of the simple linear regression model for the case, where system’s response Y depends not just on a single input X, but on a vector X = (X(1), . . . ,X(p)) of p different inputs.

Suggested Citation

  • Konstantin M. Zuev, 2026. "Multiple Linear Regression," International Series in Operations Research & Management Science, in: Fundamentals of Statistical Inference, chapter 12, pages 307-363, Springer.
    • Handle: RePEc:spr:isochp:978-3-032-03848-7_12
    DOI: 10.1007/978-3-032-03848-7_12
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