The least-square regression line has a slope of:
m=(nΣxy-ΣyΣx)/(nΣx²-ΣxΣx)
and a y-intercept of:
b=(Σy-mΣx)/n
In this case: n=7, Σxy=4899, Σy=391, Σx=85, Σx²=1153 so
m=(7(4899)-391*85)/(7(1153)-85*85))=1058/846
b=(391*846-85*1058)/(7*846)=34408/846
So the line of best fit is:
y=(1058x+34408)/846 and if we approximated this as your answers see to have done....
y=1.25x+40.67
Answer:
I think it's D...maybe
Step-by-step explanation:
Step-by-step explanation:
<h2> l=P×R×T</h2><h3> 6000=2160×X/100×9</h3><h3> 6000÷194.4</h3><h2> =30.86%</h2>
Answer:
a line that might best estimate the data and be used for predicting values.
Choice B is correct
Step-by-step explanation:
A line of fit might be defined as;
a line that might best estimate the data and be used for predicting values.
This line connects most of the data points thus minimizing the squared residuals of the regression.
I hope this helps...
