Project 1O: Temperature In Charlotte, North Carolina

Overview

The data set consists of actual temperatures in 2014-2015 (mean, min, and max); average temperatures (min and max); and record temperatures (min and max) in Charlotte, North Carolina.

Details

This data set comes from the Weather Underground via FiveThirtyEight’s Github page, and is discussed in a blog post: https://fivethirtyeight.com/features/what-12-months-of-record-setting-temperatures-looks-like-across-the-u-s/

The data were obtained from: https://github.com/fivethirtyeight/data/tree/master/us-weather-history

Data Description

Variable	Description
date	Date from July 1, 2014, to June 30, 2015
actual_mean_temp, actual_min_temp, actual_max_temp	Temperatures in degrees Fahrenheit for that date
average_min_temp, average_max_temp	Average min and max temperatures for that date
record_min_temp, record_max_temp	Record min and max temperatures for that date
record_min_temp_year, record_max_temp_year	Years in which those records occurred

weather_data = read.csv("data/KCLT.csv", stringsAsFactors = FALSE)
months = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
weather_data$month = months[as.numeric(unlist(lapply(strsplit(weather_data$date, "-"), function(x){return(x[2])})))]
head(weather_data)

Data Files

• KCLT.csv

Objectives

We will consider modeling the max and min temperatures in Charlotte in January and June. The goal is to find distributions that fit these variables well. The code above loads in the data and defines a new variable month.

• First, plot the observed temperatures (min and max) against the day of the year, as well as the record temperatures (min and max). Describe any features you observe.
• In future classes you will learn methods for accounting for patterns across time; however, for now assume that these data (within month) form random samples from some distributions. Provide numerical and graphical summaries of the observed min and max temperatures, separately for the months of January and June
• Consider fitting some of the known distributions you’ve discussed in class, both this semester and last semester, to the temperature variables in each month. 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 these variables.