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
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Answer:
See attached for a "Neat" quadratic formula.
a = -1
b = 2
c = 1
x = [-2 +- sqr root( 4 -4*-1*1)] / -2
x = [-2 +- sqr root( 4 -4*-1*1)] / -2
x = [-2 +- sqr root(8)] / -2
x1 = (-2 +2.8284271247) / -2
x1 = .8284271247 / -2
x1 = -0.41421
x2 = (-2 -2.8284271247) / -2
x2 = ( -4.8284271247) / -2
x2 = 2.4142135624
So the equation has the positive solution of
2.4142135624
Step-by-step explanation:
Answer:

Step-by-step explanation:
The slope-intercept form of an equation of a line:

m - slope
b - y-intercept → (0, b)
From the graph we have the points (4, 4) and (0, 3) → b = 3.
We have the equation:

The formula of a slope:

Put the coordinates of the points:

Finally we have:

Perimeter 430
Length 172
172 next
430 last(after the =)