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![Python code for this linear regression. yihat is in a matrix Yhat, and yi is in a matrix Y. N and D
are dimensions of the matrix Y,. 3.1 Compute the standard deviation of the output
^Y=ND11i=1N(yiy^i)2 [ ]: sigmahaty = [ ]:](https://image.slidesharecdn.com/pythoncodeforthislinearregression-230331103026-b4dca96e/75/Python-code-for-this-linear-regression-yihat-is-in-a-matrix-Yhat-a-pdf-1-2048.jpg)
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Python code for this linear regression. yihat is in a matrix Yhat, a.pdf
The document provides Python code for calculating linear regression. It mentions the use of matrices for predicted values (yhat) and actual values (y), along with their dimensions (n and d). Additionally, it outlines the computation of the standard deviation for the regression output.
![P[Y1] (b) P[Y1] (d) P[Y2] (f) P[Y=3].pdf](https://cdn.slidesharecdn.com/ss_thumbnails/py1bpy1dpy2fpy3-230331103040-9b28968a-thumbnail.jpg?width=640&height=640&fit=bounds)
Python code for this linear regression. yihat is in a matrix Yhat, a.pdf
- 1. Python code forthis linear regression. yihat is in a matrix Yhat, and yi is in a matrix Y. N and D are dimensions of the matrix Y,. 3.1 Compute the standard deviation of the output ^Y=ND11i=1N(yiy^i)2 [ ]: sigmahaty = [ ]: