Please use D2L to turn in both the PDF/ Word output and your R Markdown file.

Q1. Uber Rides (100 pts)

This question will focus on a regression framework using a dataset containing Uber rides in New York City. The dataset can be downloaded from http://math.montana.edu/ahoegh/teaching/stat408/datasets/UberMay2014.csv.

a.

Download the data, create a figure, and discuss the pattern than you see. Talk about this in the context of Uber rides.

uber <- read_csv('http://math.montana.edu/ahoegh/teaching/stat408/datasets/UberMay2014.csv')
## Parsed with column specification:
## cols(
##   Date.Time = col_character(),
##   Month = col_integer(),
##   Day = col_integer(),
##   Year = col_integer(),
##   Time.Stamp = col_time(format = "")
## )
library(lubridate)
uber.day.hour <- uber %>% mutate(hour = hour(Time.Stamp))
b.

Focusing on the seasonality in the data, fit a model that includes seasonal components. Present and describe your results. This should take the form a report and have approximately one page of writing in addition to figures and or tables. Follow the general form of: Introduction, Data, Modeling, Results, Discussion.

c.

Extract and plot your residuals over time. Comment on what you see and what the implications are for your model.