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:
x= 2.28
or, x= 0.219
Step-by-step explanation:
2x²-5x+1 =0
a= 2, b= -5 and c= 1
x= (-b <u>+</u> √(b²-4ac))/2a
= (5 <u>+</u> √(25-8))/4
= (5 <u>+</u> √17)/4
Answer:
Step-by-step explanation:
a true
b false
pi·(7.2/2)^2·x = 2·pi·(7.2/2)^2 + 2·pi·(7.2/2)·x
x = 4.5 = 4 1/2
O = V = 1458/25·pi = 58.32·pi
This is a system of equations. You can solve it by substitution or elimination. I'm going to use substitution x - 2y = -4.5; add 2y to each side x - 2y + 2y = -4.5 + 2y; simplify x = 2y - 4.5 2(2y - 4.5) + 3y = 12 4y - 9 + 3y = 12 7y - 9 = 12 7y - 9 + 9 = 12 + 9 7y = 21 y = 3 <--------first answer x - 2y = -4.5 x - 2(3) = -4.5 x - 6 = -4.5 x -6 + 6 = -4.5 + 6 x = 1.5 <--------second answer Check: 2(1.5) + 3(3) = 12? 3 + 9 = 12? 12 = 12; It checks 1.5 - 2(3) = -4.5? 1.5 - 6 = -4.5? -4.5 = -4.5; It checks x = 1.5 and y = 3 <-----------Final Answer
