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:
E
Step-by-step explanation:
Its simple
The answer is 12 because it is a negative
Linear would be the answer
Answer:28.6
I started by seeing how many 2.4 can go into 68.64. I found that it is pretty easy to find that if you multiply the 2.4 by ten you get 24 so we do that twice and have 48. We then subtract 48 from the 68.64 which leaves us with 20.64. If we multiply 2.4 by 5 we get 12 so once again subtract 12 from that 20.64 and now we are left with 8.64. So now we need to figure out how many more 2.4 are left well it is more than three because three gives us 7.2 so let’s subtract that from it now we are left with 1.44. Now we need to find out what times 2.4 gives us 1.44. Well it would be .6. So now if we add up everything we get the answer of 28.6. Sorry if this very complicated
