Answer:
d) Squared differences between actual and predicted Y values.
Step-by-step explanation:
Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable.Such formula could be used to generate values of given value X.
For example,
suppose a = 10 and b = 7. If X is 10, then predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.
It minimize the squared difference between actual and predicted value.
Step-by-step explanation:
(x+2y)²=x²+4xy+4y²
...........
Answer:
120 inches
Step-by-step explanation:
hope this helps
I will go about solving this using the elimination method.
First, convert the equations.
10x + y = -20
4x + y = -12
Second, find the easiest variable to get rid of and get rid of it! (In this case, y) We will subtract to get rid of y.
6x = -8
Third, you want to solve the equation.
6x = -8 (divide by 6)
x =

Fourth, solve for y by inserting the answer for x into one of the equations.
10(

) + y = -20

+ y = -20 (subtract

)
y =

The solution for this system of equations is (

,

).