Answer:
a. the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.
Step-by-step explanation:
If we have N points
and we want to adjust a model 
We can define the error associated to this like that:
![E(a,b) = \sum_{n=1}^N [y_n -(ax_n +b)]^2](https://tex.z-dn.net/?f=%20E%28a%2Cb%29%20%3D%20%5Csum_%7Bn%3D1%7D%5EN%20%5By_n%20-%28ax_n%20%2Bb%29%5D%5E2)
So as we can see here we are adding the square distances between the real and the adjusted values in order to minimize the error for this reason the correct answer is:
a. the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.
For this case we need to calculate the slope with the following formula:
Where:
And we can find the intercept using this:
- 2x + 3 > x + 24
First we must get all variables on the same side and constants on the opposite side. To do so we subtract x from the right and the left. Then subtract 3 from both sides as well.
- 2x - x > 24 - 3
Now we combine like terms:
- 3x > 21
We must isolate the variable x. In order to do so, we divide both sides by - 3. When you divide by a negative number, you must also flip the inequality so the greater than becomes a less than.
x < 21 / - 3
x < - 7 is your answer.
Answer:
x ≤ 7/2
Step-by-step explanation:
Expand brackets: 3x - 15 ≤ x - 8
Subtract x from both sides: 2x - 15 ≤ -8
Add 15 to both sides: 2x ≤ 7
Divide both sides by 2: x ≤ 7/2
I’m not one hundred percent but I think it’s C