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  1. Prepare
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  3. Statistics and Machine Learning
  4. Basic Statistics Warmup
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Basic Statistics Warmup

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73 Discussions

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  • thabasvini
     + 0 comments

    Full explanation and code for Mean, Mean....Check out, https://youtu.be/OsajsGX60Yk?si=6d_Y6hRuEdHOlbII

  • omkar_magar_test
     + 0 comments
    import numpy as np
from scipy.stats import mode

n = int(input())
numbers = list(map(int, input().split()))

m = np.mean(numbers)
s = round(np.std(numbers),1)
z = 1.96
std_error = (s/(np.sqrt(len(numbers))))
lower = round(m - (z*std_error), 1)
upper = round(m + (z*std_error), 1)

print(m)
print(np.median(numbers))
print(mode(numbers)[0])
print(s)
print(f'{lower} {upper}')

  • thanhan_nguyen61
     + 0 comments

    Enter your code here. Read input from STDIN. Print output to STDOUT

    import numpy as np import pandas as pd from functools import reduce from scipy.stats import norm

    i = int(input()) l = input() ls = list(map(lambda x: int(x), l.split()))

    def mean(ls, n): return reduce(lambda acc, cur : acc + cur, ls, 0) / n

    def median(ls, n): ls_sort = ls ls_sort.sort() if n % 2 != 0: return ls_sort[int(n / 2)]

    return (ls_sort[int(n / 2)] + ls_sort[int(n / 2) - 1]) / 2

    def mode(ls, n): keys = list(set(ls)) keys.sort() freq_el = {} for key in keys: freq_el[key] = 0

    for el in ls:
    freq_el[el] += 1

sorted_freq = dict(sorted(freq_el.items(), key = lambda item : item[1], reverse = True))

return list(sorted_freq.keys())[0]

    def std(ls, n): m = mean(ls, n) st = ((reduce(lambda acc, cur: acc + (cur - m)**2 , ls, 0)) / n)**0.5 return round(st, 1)

    def Confidence_Interval(ls, n): m = mean(ls, n) st = std(ls, n) # z_score = norm.ppf(0.975) z_score = 1.96

    upperBound = m + z_score * (st / n**0.5)

lowerBound = m - z_score * (st / n**0.5)

return (round(lowerBound, 1), round(upperBound, 1))

    print(mean(ls, i))
    print(median(ls, i))
    print(mode(ls, i))
    print(std(ls, i)) print(*Confidence_Interval(ls, i))

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     + 0 comments

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  • ericstricker73
     + 1 comment
    import pandas as pd
import numpy as np
import math

def custom_round(num, decimals=0):
    factor = 10 ** decimals
    return math.floor(num * factor + 0.5) / factor

i = input()
s = input()
l = list(map(int, s.split(" ")))
numbers_series = pd.Series(l)

#Calculate the mean
mean = numbers_series.mean()
print(f"{custom_round(mean,1):.1f}")

#Median
print(f"{custom_round(numbers_series.median(),1):.1f}")

#mode (which is a lsit)
mode = numbers_series.mode()
print(f"{mode[0]}")

std = numbers_series.std(ddof=0)
print(f"{custom_round(std,1):.1f}")


#Find the critical value for a 95% confidence interval
margin_of_error = 1.96 * (std / (len(numbers_series) ** 0.5))
#margin_of_error = confidence_level * (std/(np.sqrt(len(numbers_series))))

#Calculate the confidence interval
lower_bound = mean - margin_of_error
upper_bound = mean + margin_of_error

print(f"{custom_round(lower_bound,1):.1f} {custom_round(upper_bound,1):.1f}")