### Problem solving with python or r code

Using either R or Python (or anaconda) and the data set provided, I want you to provide me the code and output that completes the following. That said, I will communicate the data structures of this assignment using R.

1. Create a data set with the following values.

x = c(94, 90, 72, 72, 95, 7, 89, 36, 61, 30, 80, 15, 73, 99, 24,

96, 36, 40, 39, 84, 4, 12, 39, 8, 96, 7, 38, 91, 84, 49, 3, 34,

52, 6, 11, 2, 54, 75, 61, 47, 62, 73, 41, 41, 12, 77, 81, 74,

44, 97, 10, 15, 5, 53, 26, 1, 36, 15, 37, 3, 86, 93, 92, 28,

42, 44, 57, 19, 67, 18, 37, 46, 94, 50, 84, 52, 56, 49, 32, 17,

23, 69, 96, 95, 69, 67, 52, 85, 58, 13, 6, 4, 89, 40, 26, 73,

9, 70, 29, 41, 57, 91, 77, 54, 53, 63, 88, 39, 98, 71, 67, 14,

2, 82, 42, 8, 34, 7, 54, 7, 79, 12, 97, 26, 94, 85, 13, 12, 75,

2, 58, 6, 43, 12, 59, 25, 81, 10, 28, 96, 61, 16, 41, 41, 32,

82, 48, 3, 21, 75, 85, 1, 98, 84, 7, 42, 30, 28, 47, 13, 5, 34,

34, 14, 7, 59, 90, 84, 40, 7, 21, 68, 27, 95, 11, 23, 86, 83,

28, 23, 80, 42, 32, 25, 76, 28, 82, 1, 93, 38, 8, 79, 96, 82,

24, 75, 96, 43, 62, 35)

If you are interested in how I created the well formatted vector provided above, below is the code snipped from R that created it.

x=floor(runif( min=1, max=100, n=200))

page(x)

2.  Identify the mean, standard deviation, quartiles, and median using any method available to you. Please clearly identify your answer is! Please provide any code that you use to derive your answer whether R or python.

3. Using the sample function, sample the vector above to create a new vector of length n where n = {10, 50, 75, 100, 150}.  For each of these new vectors, calculate the value of mean, standard deviation, quartiles, and median. Explain what is happening in the above scenario as the sample size increases.

5. This will require some thinking. I would like you provide statistical evidence that a coin flop has a 50/50 probability of landing on heads (or tails) by generating a vector of length 200 and applying the concepts of mean, median, standard deviation, etc. to evaluate the data set. Hint: you may have to run your experiment more than once!