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Minimizing the sum of the squared deviations around the line is called Least square estimation.
It is given that the sum of squares is around the line.
Least squares estimations minimize the sum of squared deviations around the estimated regression function. It is between observed data, on the one hand, and their expected values on the other. This is called least squares estimation because it gives the least value for the sum of squared errors. Finding the best estimates of the coefficients is often called “fitting” the model to the data, or sometimes “learning” or “training” the model.
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Is there any option to choose from ?
If so show them and I’ll probably be able to answer .
Seven has the greatest value as a digit, but 1, 000 has the greatest standard form value.
Shampoo: 2, 4, 6, 8, 10, 12, 14 16, 18 , 20, 22, 24
nail polish: 4, 8, 12, 16, 20,. 24
combs: 8, 16, 24
hair dryers, 12, 24
find the least common multiple of the weeks given, 2, 4, 8, 12
the number is 24 so every 24 weeks
