Exercise 1.10
I'm confused as to what part c of Exercise 1.10 is asking. What does it mean by the following?
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"plot estimates for P[νμ > ε] as a function of ε, together with the Hoeffding bound 2e<sup>2ε<sup>2</sup>N</sup> (on the same graph)." HTML Code:
Does this mean to plot P[νμ > ε] and 2e<sup>2ε<sup>2</sup>N</sup> each as a separate graph? I can plot 2e<sup>2ε<sup>2</sup>N</sup> as a function of ε easily, but how would I go about plotting P[νμ > ε]? Would I define a function that plots the likelihood that νμ > ε based on the input ε, using the data obtained in part b? Am I on the right track with this thinking? HTML Code:
Also, is the book asking to plot a separate graph for each graph in b (i.e. ν<sub>1</sub>, ν<sub>rand</sub>, and ν<sub>min</sub>), based on the distribution of ν for each? 
Re: Exercise 1.10
I apologize for the previous format. I can't seem to find a way to edit or delete the thread. Here's a more readable version.
What does it mean by the following? "plot estimates for P[νμ > ε] as a function of ε, together with the Hoeffding bound 2e^(2(ε^2)N) (on the same graph)." Does this mean to plot P[νμ > ε] and 2e^(2(ε^2)N) each as a separate graph? I can plot 2e^(2(ε^2)N) as a function of ε easily, but how would I go about plotting P[νμ > ε]? Would I define a function that plots the likelihood that νμ > ε based on the input ε, using the data obtained in part b? Am I on the right track with this thinking? Also, is the book asking to plot a separate graph for each graph in b (i.e. ν_1 ν_rand, and ν_min), based on the distribution of ν for each? 
Re: Exercise 1.10
The problem asks you to compute P[νμ > ε] from your data for ε equal to (say) 0,0.01,0.02,0.03,....0.5
Now plot this computed probability for each value of epsilon versus epsilon. Quote:

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