title : One sample hypothesis testing description :
--- type:NormalExercise lang:python xp:100 skills:2 key:13e65f0ce8
type: NormalExercise
lang: python
xp: 100
skills: 1
key: 7942f6cb9a*** =instructions
New York City has an open access dataset containing all 311 service calls requesting service or registering a complaint. The data has been preloaded in complaints dataframe. The Mayor's Office in requesting an assessment on the complaints happening in NYC
*** =hint
*** =pre_exercise_code
import numpy as np
import pandas as pd
import scipy.stats as stats
import urllib.request
filename = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_6777/datasets/nyc_complaints_311.csv.gz.csv'
urllib.request.urlretrieve(filename, "nyc_complaints_311.csv.gz")
complaints = pd.read_csv('nyc_complaints_311.csv.gz')
*** =sample_code
*** =solution
*** =sct
--- type:NormalExercise lang:python xp:100 skills:2 key:dea9c09967
type: NormalExercise
lang: python
xp: 100
skills: 1
key: de7add3320*** =instructions
The us_flight_delays.csv dataset contains US flight information, such as departure location, arrival location, cancellations, and delays of US airline companies. Select one of the airline companies (e.g. United Airlines), and using a significance level of
Based on your obtained p-value, the following can be inferred about United Airlines:
a) UA experiences significantly different delays
b) there is no statistically significant difference between UA delays and overall all airline company delays
c) UA experiences on average more delays
d) UA experiences on average less delays
*** =hint
*** =pre_exercise_code
import numpy as np
import pandas as pd
import scipy.stats as stats
import urllib.request
filename = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_6777/datasets/us_flight_delays.csv.gz.csv'
urllib.request.urlretrieve(filename, "us_flight_delays.csv.gz")
df_flights = pd.read_csv('us_flight_delays.csv.gz')
all_delays = df_flights['DEPARTURE_DELAY'].dropna()
df_ua = df_flights.loc[df_flights['AIRLINE'] == 'UA']
ua_delays = df_ua['DEPARTURE_DELAY'].dropna()
# preloaded \alpha value
alpha = 0.01
*** =sample_code
print(all_delays.mean())
print(ua_delays.mean())
# one sample t-test; compare population mean to one sample mean
popmean = all_delays.mean()
st, p = stats.ttest_1samp(ua_delays, popmean)
# use the obtained p-value and given significance level to assess whether you should reject or fail to reject the hypothesis
if (p > alpha):
print('p-value > alpha, thus we fail to reject the NULL hypothesis.')
else:
print('p-value <= alpha, thus we reject the NULL hypothesis.')
*** =solution
*** =sct
success_msg("Great work!")