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.
Equation is x= 558 t2/496
x=4.5
I got the answer from another brainly user asking the same question. Someone else answered it. I just typed your question into the search bar and got it. Good luck <3
Answer:
-0.5 goes between -2 and 1
0.5 goes between -2 and 1
-4.5 goes below -2
-2.5 goes below -2
1.5 goes above 1
4 goes above 1
Step-by-step explanation:
Equation of line:
where m is the slope
and c is the y-intercept (the value of y when x = 0)
In this case, given the slope = 1/4
and y-intercept is 8 (from the point (0,8))
The equation of the following line is:
Answer:
y = 5
Step-by-step explanation:
A line parallel to the x- axis has equation
y = c
Where c is the value of the y- coordinates the line passes through
The line passes through (1, 5) with y- coordinate of 5, thus
y = 5 ← equation of line
