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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It could be 12x4=48
It should be idk if I’m right
You idiot you answered your own question!
If both pieces of metal are made of the same material the ratio of the mass and volume will be the same. here the symbol M stands for the mass of the object, and V the volume. Density has the units of mass divided by volume such as grams per centimeters cube (g/cm3) or kilograms per liter (kg/l).
