The answer you picked is correct.
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:
The squared term is 1/25
Step-by-step explanation:
The parabola equation in the vertex form is
where point (h,k) is the vertex and a is the squared term. In this case h = 2 and k = -4. On the other hand, we know that y = -3 and x = -3 are in the parabola. Replacing these values in the formula gives
Solving for a
Answer:
22
Step-by-step explanation:
If x = 5, then plug 5 in for x .
3(5) + 7
15 + 7 = 22
First, you need to set the equation equal to zero:
n^2 + 7n + 10 = 0
Now we factor. We need to find two numbers that add up to 7 and multiply to 10.
2 + 5 = 7
2 * 5 = 10
Now, we just need to write this as a polynomial:
(n + 2) (n + 5)
is our answer.
Hope this helps!
