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
Learn more about regression equation on:
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Answer:
sum of two function
Step-by-step explanation:
11 / 12 - 8 / 12 = 3 / 12 = 1/4 square meters;
Answer:

Step-by-step explanation:
so you already have the formula, which is
![\sqrt[3]{ \frac{3v}{4\pi} }](https://tex.z-dn.net/?f=%20%5Csqrt%5B3%5D%7B%20%5Cfrac%7B3v%7D%7B4%5Cpi%7D%20%7D%20)
the v represents Volume.
and 3v would be 3×volume.
![\sqrt[3]{ \frac{3 \times 1000}{4 \times \pi} }](https://tex.z-dn.net/?f=%20%5Csqrt%5B3%5D%7B%20%5Cfrac%7B3%20%5Ctimes%201000%7D%7B4%20%5Ctimes%20%5Cpi%7D%20%7D%20)
![\sqrt[3]{ \frac{3000}{12.56637061} }](https://tex.z-dn.net/?f=%20%5Csqrt%5B3%5D%7B%20%5Cfrac%7B3000%7D%7B12.56637061%7D%20%7D%20)
![\sqrt[3]{238.7324147 }](https://tex.z-dn.net/?f=%20%5Csqrt%5B3%5D%7B238.7324147%20%7D%20)

to the nearest tenth place.
