5. If you designed a part with an overall width dimension of 1.250 inches and printed it on the same 3D printer using the same f
ilament, what would you predict the width of the printed part would measure?
6. If the width of your part had to measure between 1.220 inches and 1.255 inches to operate properly, would you adjust the width dimension of your CAD model to improve the chances of the 3D-printed model being within these limits? Explain your reasoning.
6. If the width of your part had to measure between 1.220 inches and 1.255 inches to operate properly, would you adjust the width dimension of your CAD model to improve the chances of the 3D-printed model being within these limits? Explain your reasoning.
2 answers:
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The answer & explanation for this question is given in the attachment below.
Answer:
Answer for the question:
"Show that for a linearly separable dataset, the maximum likelihood solution for the logisitic regression model is obtained by finding a weight vector w whose decision boundary wx. "
is explained in the attachment.
Explanation:
