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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Yes you or I have ecause thats what friends do.
Answer:
The level of significance is the
b. maximum allowable probability of Type I error.
Step-by-step explanation:
The significance level provides the maximum probability of rejecting the null hypothesis when it is true. It is the same as a type I error (also known as false-positive). This error occurs when a researcher or investigator rejects a true null hypothesis that is supposed to be accepted. It is the opposite of a type II error (false-negative), which occurs when the researcher fails to reject a false null hypothesis.
I think it's 4a+2, what are the different options?
If that's not it then try 4a squared + 2