imports the Python function median() you used in Block 2 Part 5 to calculate the median of a list of numbers.

Use this function to find the median of visits.

In your solution document, give the median. Also provide the Python code you used for calling the median() function and explain how you executed it.

import math

def median(alist): """ Calculates the median of a list of numbers. The list must not be empty. """

number_of_values = len(alist) sorted_list = sorted(alist)

# Two cases, depending on whether the number of values is odd or even. quotient = number_of_values // 2 remainder = number_of_values % 2 if (remainder == 1): result = sorted_list[quotient] else: result = (sorted_list[quotient - 1] + sorted_list[quotient]) / 2 return result def test_median(): assert median([2]) == 2 assert median([4, 3]) == 3.5 assert median([3, 1, 8, 4, 7, 6, 4, 2, 5, 9]) == 4.5 assert median([7, 2, 6, 2, 5, 3, 1, 0, 8, 6, 6, 4, 9]) == 5

# Unit test test_median()

def mean(list): """Return mean of list""" sum = 0 count = 0 for item in list: sum = sum + item count = count + 1 return sum / count

def test_mean(): list = [1, 2, 3, 4, 5] assert(mean(list) == 3) # Unit test test_mean()

def corr_coef(list_x, list_y): """ Return correlation between values in list_x and list_y.

Lists must be of equal length. """ x_bar = mean(list_x) y_bar = mean(list_y) sxy = 0 sxx = 0 syy = 0 for index in range(len(list_x)): x = list_x[index] y = list_y[index] sxy = sxy + (x - x_bar) * (y - y_bar) sxx = sxx + (x - x_bar) * (x - x_bar) syy = syy + (y - y_bar) * (y - y_bar) return sxy / math.sqrt(sxx * syy)

def test_corr_coef(): # Data from M140 Unit 9 Example 5 list1 = [78.9, 75.8, 77.3, 74.2, 78.1, 72.8, 77.6, 77.9] list2 = [56.7, 53.1, 56.1, 55.9, 54.1, 48.6, 59.4, 54.0] assert round(corr_coef(list1, list2), 2) == 0.64 # Unit test test_corr_coef()

this is the other python code with the details

from tma02_stats import median from tma02_stats import mean from tma02_stats import corr_coef

""" You can use one of two approaches: 1) add suitable code below and then run this file 2) run this file first then do the calculation in the Python interactive shell. """

# Quarterly change from a year earlier in visits to the UK from overseas residents (ONS)

visits = [6.9,1.6,2,7.4,8.8,1.5,3.6,13.1,7.9,8.6,9.2,-5.8,-3.4,-4.5,-3,3.8,-2.5,-1.5,2.8,6.4,-16.1]

# Quarterly change from a year earlier in spending during visits to the UK (ONS)

spending = [3.6,5.7,-4.8,5.7,4.1,4.1,8.7,8.3,14.1,9.5,22.5,-1.9,2.3,-3,-16.7,-1.8,-7.5,-0.6,9.4,26.5,-9.6]