Say we have a set of linear equations given by a~x =~y. if a is invertible, we know that the solution for ~x is ~x = a −1~y. however, what if a is not a square matrix? in 16a, you saw how this problem could be approached for tall "standing up" matrices a where it really wasn’t possible to find a solution that exactly matches all the measurements, using linear least-squares. the linear least-squares solution gives us a reasonable answer that asks for the "best" match in terms of reducing the norm of the error vector.

the answer is 9.8975

explanation:

when you multiply the "39.59" with the 25% it reduces it and shows its final discounted price. hope this !

step-by-step explanation:

hc ghfrfgb

step-by-step explanation:

gedfgb nbgfrezrdfgbn bgfderdfgbvn bvfdsvcb bvfdfgb gfdgb gfr

statement #3: proof- ang is congruent to ang ecd : angles are congruent.  

step-by-step explanation:

1: the missing angles in statement #3  

a.) acb  

reason- the reason why the answer is acb is because angle ecd and angle acb are both the same size *that's what the word congruent means this is really simple.  

2: the missing word in reason #3  

c.) vertical  

reason- the reason why the answer is vertical is because the angles in that diagram on figure 2 is slanted, which is vertical, and the e and a both lay on vertical lines. along with the other's, but e and a are the 2 main points you want to focus on. and c is kind of like your starting point, because c is for both angles, not just one.  

i hope that was and understanding.

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5,-10,20,-40,30,-60

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