Historical Trends and Patterns in Temperatures around the World
The file contains Annual Average Temperature records for two cities: New York, USA and Sydney, Australia. The Annual Average
Temperature value for any year is a result of averaging daily high temperature readings from every day of that year observed in a
specific location (Central Park for New York City and Observatory Hill for Sydney.)
We would like to use statistical methods to determine whether the data shows any significant patterns or trends in temperature over
time.
In the SPSS data file, three variables are recorded: the Year (“year”), the Annual Average Temperature for New York City (“NYCtemp”)
and the Annual Average Temperature for Sydney (“Sydtemp”.)The data are sorted by year in ascending order. (In the Excel file, NYC and
Sydney data are placed into two separate worksheets. ) New York data goes back to 1869, while Sydney has records from 1859 to present.
For your convenience, Sydney temperatures have been converted from Celsius to Fahrenheit.
Original sources of data (for reference only, TempRecs.sav is the only data file you need to use for the project)
New York City: http://www.erh.noaa.gov/okx/climate/records/monthannualtemp.html
Sydney:http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?
p_nccObsCode=36&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=066062
Exercise 1
• Use a computer package to produce a scatter plot (or time series plot) of the temperatures for New York City using the
“year” as the x-axis.
• Provide a paragraph describing what you see and whether any trend or pattern appears in your graph.
• Produce a similar plot for Sydney.
• In a paragraph, describe what you see for that city and describe any differences you observe between Sydney and New
York City.
Exercise 2
• Use a statistical package to find the summary measurements: min, max, mean, median, standard deviation for New York
City, using all of the historical temperature values provided.
• Draw a Box Plot for the data set. Identify the hottest year and the coldest year.
• Repeat this exercise for the historical temperature values in Sydney.
• Write a paragraph describing how the two cities differ in temperature. Hint: Base you answer on any notable differences
you observe in the two Box Plots.
Exercise 3
• Use a statistical package to obtain a relative frequency (percent) histogram of the annual temperatures in New York
City. Describe the “shape” of distribution.
• Repeat the process for Sydney, using the same class limits you had for New York City.
• In a paragraph, describe any similarities and differences you observe between the shapes of the two distributions and
interpret the meaning of these differences.
Exercise 4-A
Limit your attention to the most “recent” temperature data, (which we define as years 1990-2013), for New York City. Answer the
following questions:
• For how many years during the recent period, did the annual temperature rise above the historical mean calculated for
the entire period? Does this represent a reasonable, relatively large or relatively small percentage of the recent years?
• Of the 25 warmest years in recorded history, what percentage occurs during the recent period? Do you think the recent
period has more or less than its fair share of warmer years? (Hint: It may help to re-sort the data according to temperature).
• Is the mean temperature for the recent period higher or lower than that of the entire period? Calculate the z-score for
the recent mean based on the mean and standard deviation for the entire period. What does the value of the z-score tell you about the
difference between the two means?
• Extra Credit: Draw a Box Plot for the recent years and place it next to the Box Plot obtained from the data
corresponding to the years prior to 1990. Are there any noteworthy differences between the two?
Exercise 4-B Repeat the process for the data associated with Sydney
Exercise 5-A
• Use a statistical package to find the value of the linear correlation coefficient between “year” and “NYCtemp”
• Is the correlation significant? Explain the reason for your answer
• If the correlation is significant, what does it imply about the trend in temperatures?
• Use technology( SPSS or Excel) to find the equation for the least squares regression (LSR) line
• Interpret the meaning of the slope of the LSR line.
• Based on the equation of LSR line, what is the “best predicted” value for the NYC Annual Average Temperature for 2013?
How accurate is the prediction?
• Can we use this LSR line equation to predict the Annual Average Temperatures for the future? Explain.
• Write a paragraph to summarize your findings: Is there statistically significant evidence of any pattern or trend in
temperature over the observation period in NYC?
Exercise 5-B Repeat the process for Sydney.
Exercise 6
For depositing in the assessment area of the ePortfolio. Please read the instructions carefully!)
Write a short (1-page) essay which includes the following:
• Using information from the internet and/or other reliable sources, define
the terms “Global Warming” and “Global Climate Change”. Please cite your sources. • Provide a couple of reasons that people believe
Global Warming is occurring. What are some opinions to the contrary? • Based upon your analysis of the historical temperature data for
New York City and Sydney, tell if you believe it is reasonable to conclude that Global Warming is occurring.
With your essay, include one graph or chart that you have created in this project that would best support your argument.
Year NYCtemp
1869 51.4
1870 53.6
1871 51.1
1872 51
1873 51
1874 51.3
1875 49.4
1876 51.9
1877 52.8
1878 53.6
1879 52.3
1880 53.2
1881 52.4
1882 52
1883 50.5
1884 52.4
1885 51.1
1886 51
1887 50.9
1888 49.3
1889 52.7
1890 52.7
1891 53.8
1892 51.9
1893 50.4
1894 52.8
1895 51.3
1896 51.7
1897 51.6
1898 53
1899 52
1900 52.4
1901 50.6
1902 51.2
1903 51.2
1904 48.7
1905 51.2
1906 52.7
1907 50.4
1908 52.5
1909 51.4
1910 51
1911 52.2
1912 51.7
1913 53.9
1914 51.1
1915 52.5
1916 51.1
1917 49.7
1918 52.1
1919 52.8
1920 51.7
1921 54.4
1922 52.8
1923 52
1924 51.1
1925 52.7
1926 50.2
1927 52.6
1928 52.7
1929 53.2
1930 53.7
1931 55.1
1932 54.3
1933 53.5
1934 52
1935 52.3
1936 52.6
1937 53.5
1938 54
1939 53.5
1940 50.7
1941 53.6
1942 52.8
1943 52.6
1944 53.4
1945 53
1946 54.2
1947 52.8
1948 53
1949 55.9
1950 52.6
1951 54
1952 54.8
1953 56
1954 53.9
1955 53.9
1956 52.7
1957 54.7
1958 51.8
1959 54.6
1960 53.1
1961 53.9
1962 52.4
1963 52.5
1964 53.5
1965 53.1
1966 53.9
1967 51.9
1968 53
1969 53.7
1970 53.2
1971 54.1
1972 53
1973 55.3
1974 54.2
1975 54.4
1976 52.8
1977 53.4
1978 52
1979 54.1
1980 53.6
1981 53.9
1982 53.5
1983 54.7
1984 54.1
1985 54.2
1986 54.2
1987 54.3
1988 54
1989 53.2
1990 56.4
1991 56.4
1992 53.1
1993 54.7
1994 54.4
1995 54.4
1996 53.7
1997 54.3
1998 57.1
1999 56.5
2000 53.8
2001 56.2
2002 56.4
2003 53.4
2004 54.5
2005 55.7
2006 56.8
2007 55
2008 55.3
2009 54
2010 56.7
2011 56.4
2012 57.3
2013 55.5
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