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.
Answer:
40.25 if my math is right
Step-by-step explanation:
2x+5y=21, x+y=3
y=3-x
2x+5y=21
2x+5(3-x) =21
2x+15-5x=21
2x-5x+15=21
-3x+15=21
-3x=21-15
-x=6/3
-x=2
x=-2
2x+5y=619
x=10y-3
________
2(10y-3) +5y=619
20y-6+5y=619
25y=619+6
25y=625
y=625/25
y=25
x=10*25-3
x=250-3
x=247