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If you see an error in the article, please comment or drop me an email. How to fit functions using linear models \[Y_i = \beta_0 + \beta_1 X_i + \sum_{k=1}^d (x_i – \xi_k)_+ \gamma_k + \epsilon_{i}\] Simulated example Source: https://github.com/DataScienceSpecialization/courses Separate the n values into k+1 spans. (k standing for knots) Create a basis: a

If you see an error in the article, please comment or drop me an email. The basics of linear regression Linear regression is one form of regression among others. It is probably the most intuitive and easiest one. The reason for regression is to 1) predict values for which there are no observed values and

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