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:
The sum of 1.8 and 1.56 is 3.36 according to the model
Sum of decimals
Given the expression 1.8 + 1.56
Using the partial sum method
1.8 + 1.56 = (1.0 + 0.8) + (1.0+0.56)
Regroup as whole and decimal number
1.8 + 1.56 = (1.0 + 1.0) + (0.8 +0.56)
1.8 + 1.56 = 2.0 + 1.36
1.8 + 1.56 = 3.36
Hence the sum of 1.8 and 1.56 is 3.36 according to the model
Answer:
.201
Step-by-step explanation:
Well to answer question 43, you would have to multiply the radius but 2 so 8*2= 16 and 16* 3.14= 50.24
ANSWER: 50.24
Step-by-step explanation:
i was busy so i did this much ...will solve later