Answer: b
Explanation:
The line of best fit to a scattergram is obtained in linear regression analysis by minimizing the sum of the squared errors.
For example, in the diagram shown below, there are n data points in the scattergram.
The error for the i-th data point is
.
The coefficients (a and b) for the line of best fit are determined using calculus, to minimize
.
Your answer to the question is B) 7
The LinReg line of best fit for this data set is ŷ = -1.24X + 0.66
<h3>What is regression line?</h3>
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable.
Given:
(−5, 6.3),
(−4, 5.6),
(−3, 4.8),
(−2, 3.1),
(−1, 2.5),
(0, 1.0),
(1, −1.4)
Sum of X = -14
Sum of Y = 21.9
Mean X = -2
Mean Y = 3.1286
Sum of squares (SSX) = 28
Sum of products (SP) = -34.6
Regression Equation,
ŷ = bX + a
b = SP/SSX = -34.6/28 = -1.23571
a = MY - bMX = 3.13 - (-1.24*-2) = 0.65714
ŷ = -1.23571X + 0.65714
ŷ = -1.24X + 0.66
Learn more about regression line here:
brainly.com/question/7656407
#SPJ1
x = 3 + y Eqn(1)
y = -2x + 9 Eqn(2)
Let us solve the system of equations with the substitution method
x - 3 = y (Subtracting 3 from both sides of the Eqn(1))
Replacing y = x - 3 in Eqn (2), we have:
x - 3 = -2x + 9
x = -2x + 9 + 3 (Adding 3 to both sides of the equation)
x + 2x = 9 + 3 (Adding 2x to both sides of the equation)
3x = 12 ( Adding like terms)
x = 12/3 (Dividing by 3 on both sides of the equation)
x = 4
Replacing x=4 in Eqn(1), we have:
4 = 3 + y
4 - 3 = y (Subtracting 3 from both sides of the equation)
y=1
The answers are:
x= 4 and y=1
