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2020 | 140 | 12-25
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Multiple Linear Regression Using Cholesky Decomposition

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Various real-world problem areas, such as engineering, physics, chemistry, biology, economics, social, and other problems can be modeled with mathematics to be more easily studied and done calculations. One mathematical model that is very well known and is often used to solve various problem areas in the real world is multiple linear regression. One of the stages of working on multiple linear regression models is the preparation of normal equations which is a system of linear equations using the least-squares method. If more independent variables are used, the more linear equations are obtained. So that other mathematical tools that can be used to simplify and help to solve the system of linear equations are matrices. Based on the properties and operations of the matrix, the linear equation system produces a symmetric covariance matrix. If the covariance matrix is also positive definite, then the Cholesky decomposition method can be used to solve the system of linear equations obtained through the least-squares method in multiple linear regression. Based on the background of the problem outlined, such that this paper aims to construct a multiple linear regression model using Cholesky decomposition. Then, the application is used in the numerical simulation and real case.
Physical description
  • Faculty Mathematics and Natural Science, Universitas Padjadjaran, Jalan Raya Bandung-Sumedang Km. 21 Jatinangor Sumedang 45363, Indonesia
  • Faculty Mathematics and Natural Science, Universitas Padjadjaran, Jalan Raya Bandung-Sumedang Km. 21 Jatinangor Sumedang 45363, Indonesia
  • Faculty Mathematics and Natural Science, Universitas Padjadjaran, Jalan Raya Bandung-Sumedang Km. 21 Jatinangor Sumedang 45363, Indonesia
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