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  • How does NumPy solve least squares for underdetermined systems?
    This works: np linalg lstsq(X, y) We would expect this to work only if X was of shape (N,5) where N>=5 But why and how? We do get back 5 weights as expected but how is this problem solved? Isn't it like we have 2 equations and 5 unknowns? How could numpy solve this? It must do something like interpolation to create more artificial equations?
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    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
  • Does this plot satisfy the linear regression model assumptions?
    predictions = X @ np linalg lstsq(X, Y, rcond=None)[0] residuals = Y - predictions And made the plot to check the two assumptions above, but I don't know how to interpret this plot Does this mean that these two assumptions are reasonable? what can I infer about the spread of the residuals? EDIT -
  • ANOVA as Multiple Linear Regression - Cross Validated
    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
  • Negative R2 on Simple Linear Regression (with intercept)
    Comment 1: Yes, X matrix is computed in a very specific way (Exponential Moving averages of the target) It seems that the problem arises particularly well in this case
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    $\begingroup$ @RonSnow closed form Gibbs sure does make life a lot easier; I would try to keep it :) To do a singular prior, you would choose a prior covariance matrix for $\alpha$ which has as range the same range as $\mathbf{X}$
  • Correlated regressors but coefficient estimation is still good, why?
    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
  • Standard error is too small on perfect data - Cross Validated
    $\begingroup$ Better software automatically issues a warning about near-perfect fit The moral here is when you are using a library like scipy stats (or, really, just about any Python stats library) you need to know enough about the internals to protect yourself, because the library won't do it for you $\endgroup$





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