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i. Draw 10 normal random number with average 10 and standard deviation 3.
ii. Evaluate the average of these 10 numbers.
iii. Write a single line of code that draws 10 random numbers and evaluates \
their average.
iv. Build a list with 100 averages of 10 normal random numbers.
v. Histogram that list.
vi. Repeat item iv, but now using 1000 simulated sets, and draw its histogram.
vii. Build a list with the averages of a 1000 data sets, each one with 90 \
normal random numbers, 
     and draw its histogram.
viii. Compare the histograms of items v, vi and vii, and discuss the result.\
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The code: vertical = Table[ {t,20t-5t^2}, {t,0,4,0.25} ]
generates a data set that, once plotted with ListPlot[ ], 
represents the vertical position of a projectile falling freely,
in meters. In order to simulate a measurement with 
standard deviation 0.5 m in the position, replace
20t-5t^2  by RandomVariate[NormalDistribution[ 20t-5t^2, 0.5 ].
i. Build a table with the vertical position affected by statistical \
fluctuation.
ii. ListPlot the table obtained in item i.
iii. Write the random sampling and the plot of the 
     simulated values in a single Mathematica instruction.
iv. See what happens every time you recalculate the instruction of item iii.
Obs.: Similar simulation tasks will be used when we study the 
          least squares method to provide for test cases. 
\
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