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:
I believe it is 8in
Step-by-step explanation:
someone correct me if I'm wrong
The figure is shown below
From the figure
Angle 150 degree and angle p forms angles on a straight
Since the sum of angles on a straight line equals 180 degrees
Hence

Solve for p in the equation

Hence, p = 30
From the figure
Angle p and angle q are vertically opposite angles
Since vertically opposite angles are equal then

Hence, q = 30
Applying the rule of angles on a straight line
This implies

Substitute q = 30 into the equation

Solve for w

Hence, w = 90
Answer:
I don't know what's going on at all possible for you
Answer:
Step-by-step explanation:
a) 5 cm(25000)(1 m/100 cm)(1 km/1000 m) = 1.25 km
b) 2.2 km (1000 m/km)(100 cm/m) / 25000 = 8.8 cm