PhillyCycle analyzed bike rental data to understand customer behavior and guide expansion plans. Most users were registered. Usage peaked in spring and fall, suggesting many are college students who don't need transportation in summer. Registered users predominantly used bikes on weekdays for commuting, while casual use increased on weekends. Overall, the data analysis indicated PhillyCycle should target expanding to college campuses to attract more long-term registered users.

1. “Weather” to Rent a Bike
MICHAEL MITROFF
WISCONSIN SCHOOL OF BUSINESS
DECEMBER 2021

2. 1
Executive Summary
In this case I analyzed data provided by PhillyCycle to gauge costumer behaviors. Initially data
had to be cleaned and organized to further help the analysis process. In this process I cleaned
duplicates and got rid of strange data like temperature data above 50 degrees Celsius. Finally, I
created more columns of data to better understand what was being provided. After data processing
I started the data analysis process. Initially data was summarized by type, casual, registered and
all users. In this process we noticed most of the users happened to be registered. Next, we looked
at users in the spring, summer and fall and noticed a significantly higher proportion of all users
use the PhillyCycle services in the spring and fall. After season we looked at rental volume by day
of the week and saw that registered users use PhillyCycle more during the week than weekends
which is the inverse for casual users. This started to point us in the direction of looking at how
PhillyCycle can target college students since they do not go to school in the summer and use
transportation services mainly on weekdays. Next, we looked at hourly usage on weekdays and
weekends and found that on weekdays registered users are using the services mainly in the
morning and at the late afternoon when people are going to and from school. After this we noticed
that many more registered users use the services during the week compared to casual users. So,
we believe that many of these registered users are using transportation services to go to school or
work. We looked at the correlation between average all users and temperature and found that they
were well correlated, but that during the summer the number of average users is 40.89 less than
the winter, everything else being equal. So, people do not use PhillyCycle in the summer, but do
use it when it is nicer outside. Also, PhillyCycle is used mainly during the peak hours when
people are in need of transportation to and from home. All these inferences point us towards the
fact that a significant proportion of PhillyCycle users could be college students. PhillyCycle
should focus on expanding to college campuses and doing research on who are the users of its
services.
Introduction
PhillyCycle is a company that rents bikes out by the hour. Currently they are thinking about
expanding operations. To understand if and how to expand, PhillyCycle has hired us to organize
and analyze their data.
Analysis
Bike Rental Patterns

3. 2
The Summary statistics for casual and registered users that I would like to show to you today are
the mean, maximum and standard deviation. These numbers for the casual user group are 35.45,
354 and 50.01 respectively. The standard deviation of casual users in the is much higher than where
the mean is, meaning that we see a greater variety of spread in our data when we push past our
mean. The maximum is also ten times greater than the mean. We can then begin to understand just
how large and just how spread out the number of casual users each day really can be. We see this
pattern also in our registered user’s data.
From the graph above we can visualize the number of users of PhillyCycle by user type (Casual
and Registered). The number of registered users is over 81% of the total number of users of
PhillyCycle.
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Bike Rental Users Broken Down by Type
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Average hourly rentals by All users by season

4. 3
The graph above shows the average number of rentals by hour for all users in the seasons. It
becomes apparent that as the seasons grow warmer we see a greater increase in hourly rentals.
The chart above shows the number of casual and registered users of PhillyCycle by month. From
the chart we notice that as months get warmer, they tend to have more users, expect when
reaching the summertime, when we notice the number of users significantly declines. The
number of users spanning from 2011 to 2012 for each month has increased over the years’ time
There is a noticeable difference in Registered user use of PhillyCycle compared to Casual user
use during the week. During the weekday we see a consistent number of Casual users using the
service, until the weekend when we notice an increase. While Registered users seem to be using
the service more during the beginning of the week and slowly use it less especially during the
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5. 4
weekends.
The data graphed above tells us a lot of useful information about Casual and Registered users use
of PhillyCycle by hour on weekdays and weekends. During weekends we see a very similar
pattern of usage of services. Usage ramps up at around 8 in the morning and sees a dip from 1 to
3pm then going back up at 4 pm and slowly declining throughout the night. The numbers
average numbers for each user type is much more comparable on weekends than weekdays. For
weekday usage rate the pattern of casual and registered users is much different. The average
number of casual users stays consistently low and does not get higher than 100. While the
average number of registered users spikes at 8am to around 450 users, and then stays consistently
low until another spike at around 5 in the afternoon to about 500 users.
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Weekend Average of Casual users Weekend Average of Registered Users

6. 5
In the two graphs above we get a visual representation of data of hourly weekday rentals for
casual and registered users. For casual users we notice that almost 80 percent of weekday rentals
amount to only between 0 and 50. From this we can determine that not many casual users are
interested in renting bikes on weekends. Registered users on the other can have only 30 percent
of their total weekday rentals in the bin of 0 to 50 users. While the percentage of users in the bins
after 0 through 50 declines. It is much greater than the percentage of casual users in bins greater
than 50. Meaning registered users have a greater desire to rent bikes in weekdays.
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7. 6
This graph shows us the average rentals per temperature for all users. The R squared for the
trendline of this graph is .6934. This means that the percentage of variation in rentals that is
explained by the percentage of variation in temperature is 69.34%. The regression equation for
the data is y = .233x + 15.762. This means that for every one degree in increase in Fahrenheit the
average rental of bikes by all users goes up by .233. The correlation between average rentals and
average temperature is 83% meaning the data suggests these variables are strongly correlated to
one another.
While analyzing the data of PhillyCycle I conducted hypothesis tests at the 5 percent significance
level to help determine if there is a significant difference in hourly rentals for weekends versus
weekdays for casual, registered and all users. For the hypothesis test on all users, I determined
that there was not a significant difference between hourly rentals for weekends and weekdays,
meaning that I did not reject my null hypothesis. The hypothesis test that I conducted on casual
users had a significant difference between hourly rentals for weekends and weekdays, meaning
that I did reject my null hypothesis. Finally, the hypothesis test that I conducted on registered
users had a significant difference between hourly rentals for weekends and weekdays, meaning
that I did reject my null hypothesis. These hypotheses tests showed us that the number of rentals
on weekends is significantly different than weekends for casual and registered user but not for all
users.
Regression Analysis
In my data analysis of PhillyCycle I conducted a regression analysis to get a better understanding
of why and when people use PhillyCycle. I ran a multiple regression, where I regressed all users
with multiple variables including temperature (Fahrenheit), windspeed, humidity, year (2012),
weekend, spring, summer, fall, mist, and precipitation. At the 1% level I found that temperature,
year, humidity, summer, and fall were all variables found to be statistically significant. Mist was
found to be statistically significant at the 5% level. The coefficients from our regression table can
tell us a lot of information. For example, all else being equal in summer we find there are 40.89
y = 0.233x + 15.762
R² = 0.6934
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8. 7
less rentals than winter. Also, a one degree increase in the temperature (Fahrenheit) can increase
the number of rentals by 5.08. All else being equal we can expect 3.48 more rentals on a clear day
compared to a day with precipitation. According to my regression 34% of the variation in bike
rentals is explained by all my independent variables. The regression equation for my regression is
the following
𝑌 = 1.25 + 5.08 ∗ 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 + .74 ∗ 𝑊𝑖𝑛𝑑𝑠𝑝𝑒𝑒𝑑 − 2.82 ∗ 𝐻𝑢𝑚𝑖𝑑𝑖𝑡𝑦 + 78.66
∗ 𝑌𝑒𝑎𝑟2012 + .61 ∗ 𝑊𝑒𝑒𝑘𝑒𝑛𝑑 + 13.17 ∗ 𝑆𝑝𝑟𝑖𝑛𝑔 − 40.89 ∗ 𝑆𝑢𝑚𝑚𝑒𝑟 + 𝐹𝑎𝑙𝑙
∗ 66.96 + 14.45 ∗ 𝑀𝑖𝑠𝑡 − 3.48 ∗ 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛
SUMMARY
OUTPUT
Regression
Statistics
Multiple R 0.58
R Square 0.34
Adjusted R
Square 0.34
Standard Error 150.54
Observations 2583.00
ANOVA
df SS MS F
Significance
F
Regression 10 29848407.67 2984840.77 131.7089
1.7619E-
222
Residual 2572 58287729.89 22662.4144
Total 2582 88136137.55
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Intercept 1.25 20.05 0.06 0.95 -38.06 40.57
TempF 5.08 0.28 17.97 0.00 4.53 5.64
windspeed 0.74 0.39 1.90 0.06 -0.02 1.51
humidity -2.82 0.18 -15.53 0.00 -3.17 -2.46
Year2012 78.66 5.98 13.14 0.00 66.92 90.39
Weekend 0.61 6.66 0.09 0.93 -12.45 13.67
Spring 13.17 10.42 1.26 0.21 -7.27 33.61
Summer -40.89 13.46 -3.04 0.00 -67.27 -14.50
Fall 66.96 9.28 7.21 0.000 48.75 85.16
Mist 14.45 7.26 1.99 0.047 0.21 28.68
Precipitation -3.48 11.55 -0.30 0.76 -26.13 19.18

9. 8
Above are the results of the multiple regression I ran. The R squared value is rounded to .34. This
means that the percentage of variation in all user rentals that is explained by the independent
variables tested is 33.87%. In addition, the multiple R is rounded to .58, meaning that the
correlation of all user rentals with the variables above is 58%. Additionally, the significant factors
to my regression analysis are temperature, year, humidity, summer, and fall.
Conclusion
For PhillyCycle to make the best decisions concerning future expansion it is important to look at
all the organized data. Initially we noticed that PhillyCycle’s users are over 81% registered user.
This tells us that most people using PhillyCycle are repeat users who may use the service on a
regular basis. PhillyCycle may be concerned with the number of future registered users. As most
people will start out as casual users then transfer to registered user. PhillyCycle should seek to
increase casual usage and think about the best ways to convert these casual users. When
analyzing data by monthly use we noticed a strong increase in usage of the PhillyCycle services
from 2011 to 2012. Another strong distinction in this data is that the data seems to rise steadily
as the months get warmer and decrease as they get colder. An important note that this is that
usage drops off in the summer months. This tells us that PhillyCycle is used by people who work
or maybe even study seasonally. Like those in college needing to get to school or even
professors. PhillyCycle may want to look at expanding onto college campuses in the future.
Next, we looked at data concerning usage by day of the week and user type. During the weekday
we see a consistent number of Casual users using the service, until the weekend when we notice
an increase. While Registered users seem to be using the service more during the beginning of
the week and slowly use it less especially during the weekends. This could tell us that those in
the registered group are those who work or study during the weekdays and need transportation,
like those in college. During weekends we see a typical pattern of usage of services. Usage
ramps up at around 8 in the morning and sees a small dip from 1 to 3pm then going back up at 4
pm and slowly declining throughout the night. This tells us that as people on the weekends are
using PhillyCycle as they need throughout the day, and use the service less as the day grows
longer. For weekday usage rate the pattern of casual and registered users is much different. The
average number of casual users stays consistently low and does not get higher than 100. While
the average number of registered users spikes at 8am and then stays consistently low until
another spike at around 5 in the afternoon. Registered users may be using the service to get to
work or school in the morning and then the usage is very low while at work and school. 5
o’clock is when many people get off work and school, registered users need transportation home
from school or work. When organizing our data about hourly usage of registered users and casual
users on weekdays we see a big difference. Most of the number of casual users happens to be
within 0 to 50 on average, while registered users have only 30% of the average amount of users
falling into this category. This tells us there are a lot more registered users are using the
PhillyCycle service during the weekday compared to the small number of casual users. When
analyzing solely average of all users and temperature we see that there is a strong correlation of
83%. Also, the R squared for this correlation is .69, this means that the percentage of variation in
rentals that is explained by the percentage of variation in temperature is 69.34%. We noticed
from our regression analysis that people there are 40.89 less users per day on average in summer
than in the winter, since winter is the time in which students are in class it suggests that people
do not use PhillyCycle for fun but necessary transportation. Taking all this information into

10. 9
account we notice a lot of similarities in registered users and college students. PhillyCycle
should seek to expand their services in college campuses. The usage of PhillyCycle by the time
of year, day and week all correlate with those who are in college. College students are in classes
in the fall and spring when PhillyCycle is used most. And most registered users are using the
services at peak times to get to and from classes. PhillyCycle should conduct more research on
where their services are being used and who are using them to make an accurate prediction to
expand to colleges.
Appendix
Notes on Data Preparation
When we were first given our data there were some errors that needed to be corrected to analyze
the data more perfectly. My first step in cleaning up the data was to remove any duplicate data in
the data set. Next, I got rid of variables that did not make sense. For example, I got rid of all data
with temperature greater than 50 degrees Celsius. I then created thirteen new columns titled
“Year”, “Month”, “DayofWeek”, “Hour”, “Weekend”, “WeekNumber”, "Mist", "Precipitation",
"Spring", "Summer", "Fall", "Temperature" and "Year2012”. With these new variables I was
better able to analyze the data that was provided to me and make suggestions to PhillyCycle. The
data provided was quite high quality and I am not concerned about the data collection processes.
Elevator Charts
In an elevator pitch I would use the graphs on the number of monthly users by type 2011-2012,
rental volume across days of week, weekday hourly average number of rentals by user type. I
would use these graphs because they would all help me create an argument as to why PhillyCycle
should seek to expand on college campuses.
The monthly graph would help me show that many the number of users significantly decreases
over the summer.
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11. 10
My graph on rental volume across days of the weeks shows that registered users are using the
services mostly on weekdays, not weekend when classes aren’t in session.
Finally, the graph for weekday hourly average number of rentals by user type shows a strong
correlation between using the service in the morning and at late afternoon when students are going
to and returning from classes.
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