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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this is correct 100%
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8 dog/cat
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The 3 goes with the square root you don’t need to multiply or anything like that. If you need to simply for example 3 square root of 20 which is when 3 is outside the square root sign. The 20 will be separated with 4*5 in the square root and then you will square root the 4 leaving the 5 in and bringing 2 out. Now you can’t just bring it out and forget about the 3 outside you need to multiply that 2 and 3 and you get 6! So you final answer is 6 square root 5. Hope this helps reply if you have a question!