ŷ= 1.795x +2.195 is the equation for the line of best fit for the data
<h3>How to use regression to find the equation for the line of best fit?</h3>
Consider the table in the image attached:
∑x = 29, ∑y = 74, ∑x²= 125, ∑xy = 288, n = 10 (number data points)
The linear regression equation is of the form:
ŷ = ax + b
where a and b are the slope and y-intercept respectively
a = ( n∑xy -(∑x)(∑y) ) / ( n∑x² - (∑x)² )
a = (10×288 - 29×74) / ( 10×125-29² )
= 2880-2146 / 1250-841
= 734/409
= 1.795
x' = ∑x/n
x' = 29/10 = 2.9
y' = ∑y/n
y' = 74/10 = 7.4
b = y' - ax'
b = 7.4 - 1.795×2.9
= 7.4 - 5.2055
= 2.195
ŷ = ax + b
ŷ= 1.795x +2.195
Therefore, the equation for the line of best fit for the data is ŷ= 1.795x +2.195
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Answer:

x | y
10 1 = 1/2 (10) -4=5-5= 1
-2 -5 = 1/2(-2)-4=-1-4= -5
4 -2 = 1/2(4)-4=2-4= -2
-8 -8 = 1/2(-8)-4=-4-4= -8
Answer:
(x = -20) if what your saying is 6 = -2x - 4 and (x = -16) if what your saying is x = -2 x 6 - 4
Step-by-step explanation:
To find k, divide both sides by 1/4
K = 2/3 / 1/4
When dividing by a fraction flip the second fraction over then multiply:
K = 2/3 x 4/1 = (2 x 4) / (3 x 1) = 8/3
K = 8/3 = 2 and 2/3