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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Hey There!
The answer to your problem is 
or 
both answers are correct!
<u>Step 1: Subtract x²+12 from both sides.</u>
−7x−(x²+12)=x²+12−(x²+12)
−x²−7x−12=0
<u>Step 2: Factor left side of equation.</u>
<u>Step 3: Set factors equal to 0.</u>
or 
<u>Get your answer:</u>
or
It’s b because if Mandy goes 18 days and her friend goes 3 times the days she goes.
Answer:
The points on a line can be assigned real number coordinates (think of numbers on a number line). The distance between any two points is their difference.
Answer:
<h2> 21 km </h2>
Step-by-step explanation:
If the bicyclist travels 7 km for every 5 minutes then it is directly proportional
5 min 7 km
15 min x
