Hi,

I'm trying to apply a linear regressipn function to my numpy array and then store the results in a new array. But i have 2 things that are not working for me.

```
def regressioncal(valarray):
new_col = []
linregres
valarray = numpy.array(valarray)
l=20000
for t in xrange(1,5000,10):
for j in xrange(1,5000,10):
for di in range(len(valarray)):
for dj in range(len(valarray[di])):
if(sum(t,j,100) >= l/2):
new_col.append(l - j - valarray[di][8])
else:
new_col.append(l - (t + valarray[di][8]/2))
numpy.insert(valarray, len(valarray)+1, 1, axis=1)
slope, intercept, r_value, p_value, std_err = stats.linregress(valarray[:,8:9],valarray[:,5:6])
line = slope*valarray[:,5:6]+intercept
err=sqrt(sum((line-valarray[:,5:6])**2)/len(valarray[:,5:6]))
linregres.append((t,j,slope,intercept, r_value, p_value, std_err,err))
return valarray
```

so basically i want to apply the linear regression to specific columns in the array, and one of them is the new_column that i'm trying to append to the array before calculationg the regression.

but the problem is :

1- the new_column is not being appended to the array

2- the identation problem : `numpy.insert`

should be outside the loops but the linear regression calculation should be inside the `(t,j)`

loops in order to get different regression for each combination.

Thank you for your help.