# statistical thinking 2-5 DataCamp course note

statistical thinking 2-5 DataCamp course note

# statistical thinking 2-4 DataCamp course note

statistical thinking 2-4 DataCamp course note

# statistical thinking 2-3 DataCamp course note

statistical thinking 2-3 DataCamp course note

# statistical thinking 2-2 DataCamp course note

statistical thinking 2-2 DataCamp course note

# ECDFs stat Empirical cumulative distribution functions

In this exercise, you will write a function that takes as input a 1D array of data and then returns the x and y values of the ECDF.  ECDFs are among the most important plots in statistical analysis. You can write your own function, foo(x,y) according to the following skeleton:

def foo(a,b):
"""State what function does here"""
# Computation performed here
return x, y


The function foo() above takes two arguments a and b and returns two values x and y. The function header def foo(a,b): contains the function signaturefoo(a,b), which consists of the function name, along with its parameters.

Define a function with the signature ecdf(data). Within the function definition,

• Compute the number of data points, n, using the len() function.
• The xx-values are the sorted data. Use the np.sort() function to perform the sorting.
• The yy data of the ECDF go from 1/n to 1 in equally spaced increments. You can construct this using np.arange() and then dividing by n.
• The function returns the values x and y.

def ecdf(data):
"""Compute ECDF for a one-dimensional array of measurements."""

# Number of data points: n
n=len(data)

# x-data for the ECDF: x
x=np.sort(data)

# y-data for the ECDF: y
y=np.arange(1,n+1)/n

return x, y