@x = (1,2,3,4,5,6,7,8,9,10,11); #x data
@y = (0.06,0.1,0.14,0.21,0.26,0.29,0.35,0.41,0.46,0.5,0.57); #y data
my ($m, $d, $b, @xy, @x2, @xy_res, @x_res_sq, @y_res_sq) = (0); #vars, arrays
$n = $#x + 1; #define number of elements in arrays/ a.k.a. sample size
foreach $i (0..$#x) #need one loop
    {                 
    $x2[$i]=$x[$i]*$x[$i]; #needed for summation of x[i]**2  
    $xy[$i]=$x[$i]*$y[$i]; #needed for summation of x[i]*y[i]
    $xy_res[$i] = abs($y[$i] - (Sum(@y)/$n))*abs($x[$i] - (Sum(@x)/$n)); #needed for summation of residuals of x[i]*y[i] in r2
    $y_res_sq[$i] = (abs($y[$i] - (Sum(@y)/$n)))**2; #needed for summations in y-sigma and r2 calcs
    $x_res_sq[$i] = (abs($x[$i] - (Sum(@x)/$n)))**2; #needed for summations in x-sigma and r2 calcs
    }       
$y_sigma = sqrt(Sum(@y_res_sq)/($#y)); #calculate the sigma of data in y-array
$x_sigma = sqrt(Sum(@x_res_sq)/($#x)); #calculate the sigma of data in x-array
$d = ($n*Sum(@x2)) - (Sum(@x)*Sum(@x)); #calculate the deviation
$m = (($n*Sum(@xy))-(Sum(@x)*Sum(@y)))/$d; #calculate the slope
$b = ((Sum(@x2)*Sum(@y)) - (Sum(@xy)*Sum(@x)))/$d; #calculate the intercept
$r2 = (Sum(@xy_res)/sqrt(Sum(@x_res_sq)*Sum(@y_res_sq)))**2; #calculate the r-squared correlation coefficient (goodness of fit)
print "Slope = $m,  Intercept = $b, x-sigma = $x_sigma, y-sigma = $y_sigma, r2 = $r2\n"; #print out the goods
sub Sum #quick function for doing summing called many many times above
{   
    $sum = 0;
    $sum += $_ for @_; 
    return $sum;       
}