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:
AC=14
X=10
Y=1
Hoped this helped!
Step-by-step explanation:
DE is half of BC, therefore x=10. AE and EC have to be the same length, which is 7. SO, y=1 and x is 10.
Answer:
(2/9)cis(30°)
Step-by-step explanation:
We can express the two numbers in magnitude∠angle form, then find their ratio.
|z1| = 3√((-1)² +(√3)²) = 3√4 = 6
∠z1 = arctan((3√3)/(-3)) = -arctan(√3) = 120°
z2 = 27∠90°
So, the ratio is ...
z1/z2 = (6∠120°)/(27∠90°) = (6/27)∠(120°-90°)
z1/z2 = (2/9)∠30°
The mileage is 27.6 mi/gal
Miles driven = 552.4 mi
Calculate gallons used.

Answer: 20.015 gallons (approximately 20 gallons)