Overview

The data set consists of the number of 911 calls per day in Seattle in 2015.

Details

The data were processed from the data available at: https://data.seattle.gov/Public-Safety/Seattle-Real-Time-Fire-911-Calls/kzjm-xkqj which provides Seattle Fire Department 911 dispatches and is updated every 5 minutes. The calls with a time stamp from 2015 are included in this data.

Variable	Description
`day_of_the_year`	Day of the year from January 1 (1) to December 31 (365), 2015
`number_of_calls`	Number of 911 calls received that day

calls_911 = read.csv("data/seattle_groupI_calls_per_day.csv")
head(calls_911)

We will consider modeling the number of 911 calls per day. The goal is to find distributions that fit this variable well.

First, plot the number of calls against the day of the year. Do you notice any patterns?
In future classes you will learn methods for accounting for patterns across time; however, for now assume that these data are a random sample from some distribution. Provide numerical and graphical summaries of the number of calls per day.
Consider fitting some of the known distributions you’ve discussed in class, both this semester and last semester, to the variable number_of_calls. For each distribution you consider, explain how you are estimating the relevant parameters (e.g., are you using the MLEs? MOMs? etc.). Consider at least 2 distributions.
Provide some assessment of fit of the distributions to the data. For example, q-q plots are useful for this purpose.
Make some conclusions about modeling this variable.