This report is based
on my PUI Assignment 2 on Homework3 about a Citibike analysis with the python
tool. The goal is to explore the Citibike trip duration difference between one-time
customers and subscribers in terms of the MTP (Mean Trip Duration). The idea is
that to prove that the average trip duration of single time customers is more
than that of the subscribers, and further concludes that a single time customer
would make better maximize the utilization than a subscriber.
A hypothesis is established
below. As the samples are not equal, a
further Two-sided T-test is implemented, the results support the hypothesis.
CitiBike Data, Data
Wrangling, Null Hypothesis, Alternative Hypothesis, Statistical Significance
Level, Two-sided t-test
H0: T(customer) <=
The mean trip duration
of single time customers over a week is less than or equal to the mean trip
duration of the subscribers over a week
H1: T(customer) > T(subscriber)
The mean trip duration of single time customers over a week is
more than the mean trip duration of the subscribers over a week.
Statistical Significance Level
Significance level: α =
A significance level alpha(α) is chosen here to reflect how significant the hypothesis
testing will be at the end of the test.
The data was collected from the CitiBike_Data_Website
for the trip duration from both one-time customer and subscriber. Later, this data was used to clean, organize, select, analyze, plot and visualize. First, the Null and
Alternative hypothesis were established with a statistical significance level at
0.05, and then the data was collected, tabulated, cleaned, and reshaped.
The analysis is
conducted by applying Pandas and DataFrames to the Python to
get the mean trip duration for Customers(one-time user) and Subscribers respectively. The figures are plotted by using
Matplotlib accordingly. Meanwhile, as t-test
applies for testing the difference between the samples
when the variances of two normal distributions are unknown, which fit in the situation, the distribution of data is subjected to a two-sided t-test.