Answer: 1.1
Step-by-step explanation:
If Sam buys 14 water bottles, he will have spent 18.9, so when you subtract 20 (the limit) from 18.9 (how much money he'll spend on the water bottles) you get 1.1
Answer:
B) The sum of the squared residuals
Step-by-step explanation:
Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).
Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.
Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.
Therefore, option B gives the correct answer.
Answer:
C. 
Step-by-step explanation:
{y = ¼x - 3}
{2x + 4y = 12
2x + 4[¼x - 3] = 12
2x + x - 12 = 12
3x - 12 = 12
+ 12 + 12
_________
3x = 24
__ __
3 3
[Plug this back into both equations above to get the y-coordinate of −1]; 
I am joyous to assist you anytime.
We can find out what LM is based on the fact that the bases of the 2 triangles are the same, this would also mean that the hypotenuse sides of the triangles should also be the same as well. Thus the sides of LM should also equal the sides of MN.
Another way, is to assume that the triangles are right angle ones, and use Pythagorean theorem to solve for the height and use that to solve for the hypotenuse.
