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5 days ago+ 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}')

2 months ago+ 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))

3 months ago+ 0 comments

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3 months ago+ 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}")

3 months ago+ 1 comment

The rounding was killing me

Also thew questions was 95% confidence interval, but we use 1.96???

2 months ago+ 0 comments

Read on critical values

7 months ago+ 0 comments

# Enter your code here. Read input from STDIN. Print output to STDOUT
import sys
import numpy as np
from scipy import stats

# Read Data
data = sys.stdin.read().splitlines()
nums = data[1].split(' ')
nums = np.array([int(num) for num in nums])

# Calculate Stats
mean = np.mean(nums)
median = np.median(nums)
mode = stats.mode(nums)[0]
std = np.std(nums)
z = 1.96
std_error = (std/(np.sqrt(len(nums))))
lower = mean - z*std_error
upper = mean + z*std_error

# Print Output
print(f"{mean:.1f}")
print(f"{median:.1f}")
print(mode)
print(f"{std:.1f}")
print(f"{lower:.1f} {upper:.1f}")

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

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