You should make x and y numpy arrays, not lists:
x = np.array([0.46,0.59,0.68,0.99,0.39,0.31,1.09,
0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78])
y = np.array([0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,
0.478,0.335,0.365,0.424,0.390,0.585,0.511])
With this change, it produces the expect plot. If they are lists, m * x will not produce the result you expect, but an empty list. Note that m is anumpy.float64 scalar, not a standard Python float.
I actually consider this a bit dubious behavior of Numpy. In normal Python, multiplying a list with an integer just repeats the list:
In [42]: 2 * [1, 2, 3]
Out[42]: [1, 2, 3, 1, 2, 3]
while multiplying a list with a float gives an error (as I think it should):
In [43]: 1.5 * [1, 2, 3]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-d710bb467cdd> in <module>()
----> 1 1.5 * [1, 2, 3]
TypeError: can't multiply sequence by non-int of type 'float'
The weird thing is that multiplying a Python list with a Numpy scalar apparently works:
In [45]: np.float64(0.5) * [1, 2, 3]
Out[45]: []
In [46]: np.float64(1.5) * [1, 2, 3]
Out[46]: [1, 2, 3]
In [47]: np.float64(2.5) * [1, 2, 3]
Out[47]: [1, 2, 3, 1, 2, 3]
So it seems that the float gets truncated to an int, after which you get the standard Python behavior of repeating the list, which is quite unexpected behavior. The best thing would have been to raise an error (so that you would have spotted the problem yourself instead of having to ask your question on Stackoverflow) or to just show the expected element-wise multiplication (in which your code would have just worked). Interestingly, addition between a list and a Numpy scalar does work:
In [69]: np.float64(0.123) + [1, 2, 3]
Out[69]: array([ 1.123, 2.123, 3.123])