Linear Regression

create a set of points like this ( all on one line )

p = [[0,3.8414709848078967],[0.6931471805599453,6.6818861490654635],[1.0986122886681098,7.5355691627323065],[1.3862943611198906,7.788374949171635],[1.6094379124341003,8.478827375073264],[1.791759469228055,9.887622378713294],[1.9459101490553132,11.440627194940042],[2.0794415416798357,12.307124413342725]]

where:

p[i][0] is an x value

and p[i][1] is the corresponding y value

Find the least square curve that best matches these points using linear regression.

I need four outputs:

n = number of points

m = slope

b = y intercept

r = coefficient of regression.

formulas that might help:

n = p.length

m = (n* xy - x* y)/(n* xx - x* x)

b = ( y - m* x)/n

r = (n* xy - x* y)/sqrt((n* xx - x* x)*(n* yy- y* y))

write a program in notepad++.

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