## How to calculate the intercept using numpy.linalg.lstsq

After running a multiple linear regression using numpy.linalg.lstsq I get 4 arrays as described in the documentation, however it is not clear to me how do I get the intercept value. Does anyone know this? I'm new to statistical analysis. Here is my model: X1 = np.array(a) X2 = np.array(b) X3 = np.array(c) X4 = np.array(d) X5 = np.array(e) X6 = np.array(f) X1l = np.log(X1) X2l = np.log(X2) X3l = np.log(X3) X6l = np.log(X6) Y = np.array(g) A = np.column_stack([X1l, X2l, X3l, X4, X5, X6l, np.ones(len(a), float)]) result = np.linalg.lstsq(A, Y) This is a sample of what my model is generating: (array([ 654.12744154, -623.28893569, 276.50269246, 11.52493817, 49.92528734, -375.43282832, 3852.95023087]), array([ 4.80339071e+11]), 7, array([ 1060.38693842, 494.69470547, 243.14700033, 164.97697748, 58.58072929, 19.30593045, 13.35948642])) I believe the intercept is the second array, still I'm not sure about that, as its value is just too high.

## Solutions/Answers:

### Answer 1:

The intersect is the coefficient that corresponds to the column of `ones`

, which in this case is:

```
result[0][6]
```

To make it clearer to see, consider your regression, which is something like:

```
y = c1*x1 + c2*x2 + c3*x3 + c4*x4 + m
```

written in matrix form as:

```
[[y1], [[x1_1, x2_1, x3_1, x4_1, 1], [[c1],
[y2], [x1_2, x2_2, x3_2, x4_2, 1], [c2],
[y3], = [x1_3, x2_3, x3_3, x4_3, 1], * [c3],
... ... [c4],
[yn]] [x1_n, x2_n, x3_n, x4_n, 1]] [m]]
```

or:

```
Y = A * C
```

where `A`

is the so called “Coefficient’ matrix and `C`

the vector containing the solution for your regression. Note that `m`

corresponds to the column of `ones`

.

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