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This course consists of the theory of linear regression and practical implementation.
Updated on Sep, 2026
Programming, Data Science, Machine Learning
Duration - 3.5 hours
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This course consists of the theory of linear regression and practical implementation. Assumptions, removing inconsistent variables, etc., will be covered in this course.
R is a powerful language and environment that is used for statistical computing and graphics. Very similar to the S language and environment that was developed at Bell Laboratories by John Chambers and colleagues, while the R program was a part of the GNU project. R can be considered as a different implementation of S, and there are some important differences, while so much of their code is similar for both S and R.
R provides a lot of variety for statistics, including linear and non-linear modeling and classical statistical tests, among others, as well as for graphical techniques which are quite extensible. The S language is, therefore, a great choice for research in statistical methods, and R provides an open source to match the requirements.
One of the biggest strengths of R is that it is easy to integrate it with publication-quality plots, including symbols and formulas related to mathematical equations. So while great care has been taken to ensure default choices in minor design changes, the user is more often than not in full control of the software. In addition, R is available as free software under the terms and conditions of the Free Software Foundation’s GNU General Public License. It is capable of being compiled and run on a number of platforms, including UNIX platforms, macOS, and Windows.
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