Determining Factors that Affect a Restaurant’s Yelp Rating

and 3 collaborators

**Keywords:** Logistic regression, principal component analysis, lasso regression, hot spot analysis, kernel density

Exploring the Relationship between the Hour of Day and CitiBike Ridership

and 3 collaborators

# Abstract

In this analysis, we explore whether if there is a difference between the number of CitiBike rides during the rush hours of New York City and during non-rush hours. We define the rush hours of New York City to be the hours between 7 to 9 A.M. and 4 to 9 P.M during business days. We state our hypothesis and test it using a two-sided t-test. The test indicates that there is indeed a difference.

**PUI2016 Urban Informatics Class Project Proposal**

and 1 collaborator

**PUI2016 Urban Informatics Class Project Proposal**

The impact of urban qualities on the popularity of Taxi

and 1 collaborator

CitiBike Project: Do men take longer bike trips than women?

### Do men take longer CitiBike trips than women?

## The number of women taking longer trips on Citi Bike is the same or higher than the number of men taking Citi Bike trips.

## H0 = W(time of the trip) > = M(time of the trip)

## H1 = W(time of the trip) < M(time of the trip)

Citibike Report

# Abstract

## This report aims to find whether mean trip duration of young people is longer than middle-aged people. Z-test is performed to determine whether this hypothesis is true. After performed the test, it's very likely that young people ride longer than middle-aged people.

# Data

# Analysis

Difference in biking times between male and female

### Abstract

This project intends to examine if there is any difference between male bikers and female bikers in the day and night time. More specifically, my initial hypothesis is that men are more likely to bike than women in the night time due to safety concerns. Women are more sensitive to the potential safety risks when traveling at night than men do.

###Data

I use the information of bikers in February, 2015 as the sample for my study. The time they started biking will be the determinant of the time they biked. I divided my sample into two groups, men and women based on the gender information. The day and night times are categorized as the followings:

- Day time: from 7am - 7pm
- Night time: from 7pm - 7 am

Based on the available data, I calculated the normalized ratios of bikers in the day and night times for each gender for illustrative graphs and statistical analysis.

### Analysis

Data Overview

The graphs below show that there may be some differences between men and women in terms of the hours they are most likely to bike. In figure 1, the fractions of men riding bike after 6pm and before 8am are higher than those of women riding bike. In figure 2, which illustrates the fraction of each gender at day and night, the fraction of female riders is higher than that of male riders at day and lower at night.

Exploring the CItibike trip duration differences between single-time customers and subscribers

# Abstract

**Null Hypothesis**

**Alternative Hypothesis**

**Statistical Significance Level**