There have been many studies documenting that the average global temperature has been increasing over the last century. The consequences of a continued rise in global temperature will be dire. Rising sea levels and an increased frequency of extreme weather events will affect billions of people.

In this problem, we will attempt to study the relationship between average global temperature and several other factors.

The file climate_change.csv contains climate data from May 1983 to December 2008. The available variables include:

• Year: the observation year.
• Month: the observation month.
• Temp: the difference in degrees Celsius between the average global temperature in that period and a reference value. This data comes from the Climatic Research Unit at the University of East Anglia.
• CO2, N2O, CH4, CFC.11, CFC.12: atmospheric concentrations of carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), trichlorofluoromethane (CCl3F; commonly referred to as CFC-11) and dichlorodifluoromethane (CCl2F2; commonly referred to as CFC-12), respectively. This data comes from the ESRL/NOAA Global Monitoring Division.
  • CO2, N2O and CH4 are expressed in ppmv (parts per million by volume -- i.e., 397 ppmv of CO2 means that CO2 constitutes 397 millionths of the total volume of the atmosphere)
  • CFC.11 and CFC.12 are expressed in ppbv (parts per billion by volume).
• Aerosols: the mean stratospheric aerosol optical depth at 550 nm. This variable is linked to volcanoes, as volcanic eruptions result in new particles being added to the atmosphere, which affect how much of the sun's energy is reflected back into space. This data is from the Godard Institute for Space Studies at NASA.
• TSI: the total solar irradiance (TSI) in W/m2 (the rate at which the sun's energy is deposited per unit area). Due to sunspots and other solar phenomena, the amount of energy that is given off by the sun varies substantially with time. This data is from the SOLARIS-HEPPA project website.
• MEI: multivariate El Nino Southern Oscillation index (MEI), a measure of the strength of the El Nino/La Nina-Southern Oscillation (a weather effect in the Pacific Ocean that affects global temperatures). This data comes from the ESRL/NOAA Physical Sciences Division.

The Programme for International Student Assessment (PISA) is a test given every three years to 15-year-old students from around the world to evaluate their performance in mathematics, reading, and science. This test provides a quantitative way to compare the performance of students from different parts of the world. In this homework assignment, we will predict the reading scores of students from the United States of America on the 2009 PISA exam.

The datasets pisa2009train.csv and pisa2009test.csv contain information about the demographics and schools for American students taking the exam, derived from 2009 PISA Public-Use Data Files distributed by the United States National Center for Education Statistics (NCES). While the datasets are not supposed to contain identifying information about students taking the test, by using the data you are bound by the NCES data use agreement, which prohibits any attempt to determine the identity of any student in the datasets.

Each row in the datasets pisa2009train.csv and pisa2009test.csv represents one student taking the exam. The datasets have the following variables:

• grade: The grade in school of the student (most 15-year-olds in America are in 10th grade)
• male: Whether the student is male (1/0)
• raceeth: The race/ethnicity composite of the student
• preschool: Whether the student attended preschool (1/0)
• expectBachelors: Whether the student expects to obtain a bachelor's degree (1/0)
• motherHS: Whether the student's mother completed high school (1/0)
• motherBachelors: Whether the student's mother obtained a bachelor's degree (1/0)
• motherWork: Whether the student's mother has part-time or full-time work (1/0)
• fatherHS: Whether the student's father completed high school (1/0)
• fatherBachelors: Whether the student's father obtained a bachelor's degree (1/0)
• fatherWork: Whether the student's father has part-time or full-time work (1/0)
• selfBornUS: Whether the student was born in the United States of America (1/0)
• motherBornUS: Whether the student's mother was born in the United States of America (1/0)
• fatherBornUS: Whether the student's father was born in the United States of America (1/0)
• englishAtHome: Whether the student speaks English at home (1/0)
• computerForSchoolwork: Whether the student has access to a computer for schoolwork (1/0)
• read30MinsADay: Whether the student reads for pleasure for 30 minutes/day (1/0)
• minutesPerWeekEnglish: The number of minutes per week the student spend in English class
• studentsInEnglish: The number of students in this student's English class at school
• schoolHasLibrary: Whether this student's school has a library (1/0)
• publicSchool: Whether this student attends a public school (1/0)
• urban: Whether this student's school is in an urban area (1/0)
• schoolSize: The number of students in this student's school
• readingScore: The student's reading score, on a 1000-point scale

Flu epidemics constitute a major public health concern causing respiratory illnesses, hospitalizations, and deaths. According to the National Vital Statistics Reports published in October 2012, influenza ranked as the eighth leading cause of death in 2011 in the United States. Each year, 250,000 to 500,000 deaths are attributed to influenza related diseases throughout the world.

The U.S. Centers for Disease Control and Prevention (CDC) and the European Influenza Surveillance Scheme (EISS) detect influenza activity through virologic and clinical data, including Influenza-like Illness (ILI) physician visits. Reporting national and regional data, however, are published with a 1-2 week lag.

The Google Flu Trends project was initiated to see if faster reporting can be made possible by considering flu-related online search queries -- data that is available almost immediately.

We would like to estimate influenza-like illness (ILI) activity using Google web search logs. Fortunately, one can easily access this data online:

ILI Data - The CDC publishes on its website the official regional and state-level percentage of patient visits to healthcare providers for ILI purposes on a weekly basis.

Google Search Queries - Google Trends allows public retrieval of weekly counts for every query searched by users around the world. For each location, the counts are normalized by dividing the count for each query in a particular week by the total number of online search queries submitted in that location during the week. Then, the values are adjusted to be between 0 and 1.

The csv file FluTrain.csv aggregates this data from January 1, 2004 until December 31, 2011 as follows:

• "Week" - The range of dates represented by this observation, in year/month/day format.
• "ILI" - This column lists the percentage of ILI-related physician visits for the corresponding week.
• "Queries" - This column lists the fraction of queries that are ILI-related for the corresponding week, adjusted to be between 0 and 1 (higher values correspond to more ILI-related search queries).

We often take data for granted. However, one of the hardest parts about analyzing a problem you're interested in can be to find good data to answer the questions you want to ask. As you're learning R, though, there are many datasets that R has built in that you can take advantage of.

In this problem, we will be examining the "state" dataset, which has data from the 1970s on all fifty US states. For each state, the dataset includes the population, per capita income, illiteracy rate, murder rate, high school graduation rate, average number of frost days, area, latitude and longitude, division the state belongs to, region the state belongs to, and two-letter abbreviation.

If you can't access the state dataset in R, here is a CSV file with the same data that you can load into R using the read.csv function: statedata.csv

After you have loaded the data into R, inspect the data set using the command: str(statedata)

This dataset has 50 observations (one for each US state) and the following 15 variables:

• Population - the population estimate of the state in 1975
• Income - per capita income in 1974
• Illiteracy - illiteracy rates in 1970, as a percent of the population
• Life.Exp - the life expectancy in years of residents of the state in 1970
• Murder - the murder and non-negligent manslaughter rate per 100,000 population in 1976
• HS.Grad - percent of high-school graduates in 1970
• Frost - the mean number of days with minimum temperature below freezing from 1931–1960 in the capital or a large city of the state
• Area - the land area (in square miles) of the state
• state.abb - a 2-letter abbreviation for each state
• state.area - the area of each state, in square miles
• x - the longitude of the center of the state
• y - the latitude of the center of the state
• state.division - the division each state belongs to (New England, Middle Atlantic, South Atlantic, East South Central, West South Central, East North Central, West North Central, Mountain, or Pacific)
• state.name - the full names of each state
• state.region - the region each state belong to (Northeast, South, North Central, or West)