StatAnal-exercises-lecture-3
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I. Programming exercise

Implement example from slide 5:
   -->  make histogram of Gaussian distribution chracterised by (mu, sigma)
   -->  calculate probability of |x_obs - mu| > 1.5 sigma 
   -->  draw corresponding chi2 distribution
   -->  confirm value of probability chi2 > 2.25
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II. Programming exercise

Implement example from slide 12-14:
   -->  generate datapoints, assume Poissonian errors
   -->  evaluate chi2
   -->  perform chi2 consistency test
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III. Programming exercise

Implement example from slide 15-16:
   -->  generate datapoints, assume Poissonian errors
   -->  evaluate lnL
   -->  generate many MC toy distribution, see code on slice 16
   -->  obtain expected lnL ditribution, compare with observed
   -->  is your observed lnL imply good agreement?

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IV. Programming exercise

Implement example from slide 18-19:
   -->  for the given measured data points obtain best fit value
   -->  reproduce figure shown on the bottom of page 19

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V. Programming exercise

Implement example from slide 21-23:
   -->  generate datapoints, assume Gaussian errors
   -->  find the best straight line and uncertainties
        implement formulas, get numerical values
   -->  draw datapoints and fitted line  	  

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VI. Programming exercise

Implement example from slide 24-27:
   -->  generate datapoints, assume Poissonian errors
   -->  find the best straight line and uncertainties,
        implement formulas, get numerical values
   -->  draw datapoints and fitted line  	  

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VII. Programming exercise

Implement example from slide 31-32:
   -->  generate datapoints for lifetime mesurement
        with assumed PDF:   P(t) = 1/tau exp(-t/tau)
   -->  perfom unbinned likelihood fit, estimate measured value
        and its uncertainties
   -->  draw likelihood function	
	 

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