sources of prediction error

pl = pyplt.plot(xVals, yVals, label="std dev(sec)")
pyplt.legend(loc=2, prop={'size':18})
<matplotlib.legend.Legend at 0x145c51a50>

Inherent variance
f=a*x
(x,f),…
x1,a*x,
f=a*x + NOISE

(x1,v1),(x1,v2),…
V1,V2…

dataPoints_1 = []
x = np.arrange(0, 100, 10)
for j in xrange(100):
	points = [(i, i*2 + 3 + numpy.random.normal(scale=50.0)) for i in x]
	dataPoints_1.extend(points)

pointToVals = []
pointToBounds = []
for i in np.arrange(0, 100, 10):
	valsForDataPoint = [v for v dataPoints_l if v[0]==i]
	pointToVals.append(valsForDataPoint)
	upperBound = numpy.percentile(valsForDataPoint, 95)
	lowerBound = numpy.percentile(valsForDataPoint, 5)
	pointToBounds.append([i, (upperBound, lowerBound)])

pyplt.plot(x, [v[1][0] for v in pointToBounds])
pyplt.plot(x, [v[1][1] for v in pointToBounds])
pyplt.plot(x, [(i*2+3) for i in x], color="red", label="true")