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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I did (450*3/4=337.5) to get how much each person would eat, then I did (337.5*14=4725g) to see how big the turkey should be. I then converted grams to kilos (4725*0.001=4.725 kg), and I got how big the turkey would be. It turns out the appropriate size of the turkey would be medium, so then what I did was that I looked for the medium price of the turkey per kg, and found that it was £5.99 per kg. I did (4.725*5.99) and I got about £28.30.
It would cost £28.30 for 14 people.
Answer: The last choice is correct. Edna can score 5 times as many points in the next level as in the level she has reached
Step-by-step explanation: chart of values for 
x is the level y, points possible)
x y
1 5
2 25
3 125
4 625
5 3125
You can see the exponential pattern
For what it's worth, two views of the graph of the equation are attached
The point values are astronomical!
Hi!
Let's take a look at the equation:
F = C + 32
What do we notice?
C and F are both variables.
Variables are unknown numbers represented by a letter.
In this case, C and F are the variables.
Hope this helps!
- Melanie
Answer:3.25
Step-by-step explanation:
30-17=13 13 divied by 4 = 3.25
