saving . . . saved Solution to Exercise 2. Is is correct? has been deleted. Solution to Exercise 2. Is is correct? has been hidden .
Solution to Exercise 2. Is is correct?
Title
Question
  I have tried the exercise 2 question as follows:

  S = [0.19, 0.38, 0.57, 0.77, 0.96, 1.15, 1.34, 1.54, 1.73]

  n = [10.74, 14.01, 18.52, 20.23, 22.88, 24.59, 27.55, 28.48, 30.20]

  delta_S = [0.006, 0.006, 0.005, 0.003, 0.004, 0.007, 0.004, 0.004, 0.007]

  delta_n = [0.61, 0.69, 0.53, 0.38, 0.46, 0.37, 0.46, 0.46, 0.37]

  nsquare = square(n)

  Plot 1:
  plot(S,n,'.')
  errorbar(S, n, xerr = delta_S, yerr = delta_n, fmt = 'bo')

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">
  Each point is of the form: <img alt="" src="data:image/png;base64,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">
  Similarly Plot2:
  plot(S,nsquare,'.')
  errorbar(S, nsquare, xerr = delta_S, yerr = delta_n, fmt = 'bo')

  Here we have to zoom a lot more to see the error in the measurement data.
  Kindly comment whether it is correct or not.

Python-3.4.3 Plotting-Data 05-06 min 30-40 sec 04-04-21, 6:26 p.m. mohnishb11

Answers:

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