Appreciated the visual representation of what the definition means and breaking down the problems to illustrate the concepts. It made the material much easier to understand.
So the row reduction that takes place in example 2 leads to a row of zeros and a free variable, hence the nontrivial solutions. In the last example, we didn't use the A^T*A process since the columns were orthogonal, but if we did, there would not be a row of zeros; we would get the unique solution found using the formula. Thanks for your question.
in the least-squares error problem you said any other point would be further than 9.2, but our definition says the least-squares solution is less than or equal to any other point. How does that work?
Appreciated the visual representation of what the definition means and breaking down the problems to illustrate the concepts. It made the material much easier to understand.
I’m slightly confused on why the last example has a unique solution, while example 2 had a nontrivial solution
So the row reduction that takes place in example 2 leads to a row of zeros and a free variable, hence the nontrivial solutions. In the last example, we didn't use the A^T*A process since the columns were orthogonal, but if we did, there would not be a row of zeros; we would get the unique solution found using the formula. Thanks for your question.
@ i appreciate that. Ended up getting a B-! Couldn’t have done it without your help. Thank you!
This makes more sense than the way I learned to do this stuff in statistics. Figures.
Video 3 completed for rewatching all the videos for this unit
in the least-squares error problem you said any other point would be further than 9.2, but our definition says the least-squares solution is less than or equal to any other point. How does that work?
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