Story Name: Air Pollution and Mortality

Abstract: Researchers at General Motors collected data on 60 U.S. Standard
Metropolitan Statistical Areas (SMSA's) in a study of whether air pollution
contributes to mortality. The dependent variable for analysis is age adjusted
mortality (called "Mortality"). The data include variables measuring demographic
characteristics of the cities, variables measuring climate characteristics, and
variables recording the pollution potential of three different air pollutants.

The pollution variables are highly skewed. A logarithm transformation makes them
much more nearly symmetric. Various multiple regression models may be used to
determine whether air pollution is significantly related to mortality. Image: A
partial regression plot of Education from the regression of Mortality on
Education, pop density, %NonWhite, Rain and Log(NOx).

Reference: U.S. Department of Labor Statistics Authorization: free use
Description: Properties of 60 Standard Metropolitan Statistical Areas (a standard
Census Bureau designation of the region around a city) in the United States,
collected from a variety of sources.

The data include information on the social and economic conditions in these
areas, on their climate, and some indices of air pollution potentials. Number of
cases: 60 Variable Names:

1.city: City name
2.JanTemp: Mean January temperature (degrees Farenheit)
3.JulyTemp: Mean July temperature (degrees Farenheit)
4.RelHum: Relative Humidity
5.Rain: Annual rainfall (inches)
6.Mortality: Age adjusted mortality
7.Education: Median education
8.PopDensity: Population density
9.%NonWhite: Percentage of non whites
10.%WC: Percentage of white collar workers
11.pop: Population
12.pop/house: Population per household
13.income: Median income
14.HCPot: HC pollution potential
15.NOxPot: Nitrous Oxide pollution potential
16.SO2Pot: Sulfur Dioxide pollution potential
17.NOx: Nitrous Oxide

The Data:

city JanTemp JulyTemp RelHum Rain Mortality Education PopDensity %NonWhite %WC pop pop/house income HCPot NOxPot S02Pot NOx

Akron, OH 27 71 59 36 921.87 11.4 3243 8.8 42.6 660328 3.34 29560 21 15 59 15
Albany-Schenectady-Troy, NY 23 72 57 35 997.87 11.0 4281 3.5 50.7 835880 3.14 31458 8 10 39 10
Allentown, Bethlehem, PA-NJ 29 74 54 44 962.35 9.8 4260 0.8 39.4 635481 3.21 31856 6 6 33 6
Atlanta, GA 45 79 56 47 982.29 11.1 3125 27.1 50.2 2138231 3.41 32452 18 8 24 8
Baltimore, MD 35 77 55 43 1071.29 9.6 6441 24.4 43.7 2199531 3.44 32368 43 38 206 38
Birmingham, AL 45 80 54 53 1030.38 10.2 3325 38.5 43.1 883946 3.45 27835 30 32 72 32
Boston, MA 30 74 56 43 934.70 12.1 4679 3.5 49.2 2805911 3.23 36644 21 32 62 32
Bridgeport-Milford, CT 30 73 56 45 899.53 10.6 2140 5.3 40.4 438557 3.29 47258 6 4 4 4
Buffalo, NY 24 70 61 36 1001.90 10.5 6582 8.1 42.5 1015472 3.31 31248 18 12 37 12
Canton, OH 27 72 59 36 912.35 10.7 4213 6.7 41.0 404421 3.36 29089 12 7 20 7
Chattanooga, TN-GA 42 79 56 52 1017.61 9.6 2302 22.2 41.3 426540 3.39 25782 18 8 27 8
Chicago, IL 26 76 58 33 1024.89 10.9 6122 16.3 44.9 606387 3.20 36593 88 63 278 63
Cincinnati, OH-KY-IN 34 77 57 40 970.47 10.2 4101 13.0 45.7 1401491 3.21 31427 26 26 146 26
Cleveland, OH 28 71 60 35 985.95 11.1 3042 14.7 44.6 1898825 3.29 35720 31 21 64 21
Columbus, OH 31 75 58 37 958.84 11.9 4259 13.1 49.6 124833 3.26 29761 23 9 15 9
Dallas, TX 46 85 54 35 860.10 11.8 1441 14.8 51.2 1957378 3.22 38769 1 1 1 1
Dayton-Springfield, OH 30 75 58 36 936.23 11.4 4029 12.4 44.0 942083 3.35 30232 6 4 16 4
Denver, CO 30 73 38 15 871.77 12.2 4824 4.7 53.1 1428836 3.15 39099 17 8 28 8
Detroit, MI 27 74 59 31 959.22 10.8 4834 15.8 43.5 4488072 3.44 33858 52 35 124 35
Flint, MI 24 72 61 30 941.18 10.8 3694 13.1 33.8 450449 3.53 32000 11 4 11 4
Fort Worth, TX 45 85 53 31 891.71 11.4 1844 11.5 48.1 3.22 1 1 1 1
Grand Rapids, MI 24 72 61 31 871.34 10.9 3226 5.1 45.2 601680 3.37 29915 5 3 10 3
Greensboro-Winston-Salem-High Point, NC 40 77 53 42 971.12 10.4 2269 22.7 41.4 851851 3.45 29450 8 3 5 3
Hartford, CT 27 72 56 43 887.47 11.5 2909 7.2 51.6 715923 3.25 37565 7 3 10 3
Houston, TX 55 84 59 46 952.53 11.4 2647 21.0 46.9 2735766 3.35 39558 6 5 1 5
Indianapolis, IN 29 75 60 39 968.67 11.4 4412 15.6 46.6 1166575 3.23 31461 13 7 33 7
Kansas City, MO 31 81 55 35 919.73 12.0 3262 12.6 48.6 914427 3.10 30783 7 4 4 4
Lancaster, PA 32 74 54 43 844.05 9.5 3214 2.9 43.7 362346 3.38 30248 11 7 32 7
Los Angeles, Long Beach, CA 53 68 47 11 861.26 12.1 4700 7.8 48.9 7477503 2.66 36624 648 319 130 319
Louisville, KY-IN 35 71 57 30 989.26 9.9 4474 13.1 42.6 956756 3.37 29621 38 37 193 37
Memphis, TN-AR-MS 42 82 59 50 1006.49 10.4 3497 36.7 43.3 913472 3.49 27910 15 18 34 18
Miami-Hialeah, FL 67 82 60 60 861.44 11.5 4657 13.5 47.3 1625781 2.65 32808 3 1 1 1
Milwaukee, WI 20 69 64 30 929.15 11.1 2934 5.8 44.0 1397143 3.26 35272 33 23 125 23
Minneapolis-St. Paul, MN-WI 12 73 58 25 857.62 12.1 2095 2.0 51.9 2137133 3.28 35871 20 11 26 11
Nashville, TN 40 80 56 45 961.01 10.1 2682 21.0 46.1 850505 3.32 28641 17 14 78 14
New Haven-Meriden, CT 30 72 58 46 923.23 11.3 3327 8.8 45.3 500474 3.16 34364 4 3 8 3
New Orleans, LA 54 81 62 54 1113.16 9.7 3172 31.4 45.5 1256256 3.36 32704 20 17 1 17
New York, NY 33 77 58 42 994.65 10.7 7462 11.3 48.7 8274961 3.03 36047 41 26 108 26
Philadelphia, PA-NJ 32 76 54 42 1015.02 10.5 6092 17.5 45.3 4716818 3.32 33449 29 32 161 32
Pittsburgh, PA 29 72 56 36 991.29 10.6 3437 8.1 45.5 2218870 3.32 32934 45 59 263 59
Portland, OR 38 67 73 37 893.99 12.0 3387 3.6 50.3 1105699 2.66 33020 56 21 44 21
Providence, RI 29 72 56 42 938.50 10.1 3508 2.2 38.8 618514 3.16 30094 6 4 18 4
Reading, PA 33 77 54 41 946.19 9.6 4843 2.7 38.6 312509 3.08 32449 11 11 89 11
Richmond-Petersburg, VA 39 78 53 44 1025.50 11.0 3768 28.6 49.5 761311 3.32 33510 12 9 48 9
Rochester, NY 25 72 60 32 874.28 11.1 4355 5.0 46.4 971230 3.21 34896 7 4 18 4
St. Louis, MO-IL 32 79 57 34 953.56 9.7 5160 17.2 45.1 1808621 3.23 34546 31 15 68 15
San Diego, CA 55 70 61 10 839.71 12.1 3033 5.9 51.0 1861846 3.11 32586 144 66 20 66
San Francisco, CA 48 63 71 18 911.70 12.2 4253 13.7 51.2 1488871 2.92 47966 311 171 86 171
San Jose, CA 49 68 71 13 790.73 12.2 2702 3.0 51.9 1295071 3.36 41994 105 32 3 32
Seattle, WA 40 64 72 35 899.26 12.2 3626 5.7 54.3 1607469 3.02 37069 20 7 20 7
Springfield, MA 28 74 56 45 904.16 11.1 1883 3.4 41.9 515259 3.21 29327 5 1 20 1
Syracuse, NY 24 72 61 38 950.67 11.4 4923 3.8 50.5 642971 3.34 30114 8 5 25 5
Toledo, OH 26 73 59 31 972.46 10.7 3249 9.5 43.9 616864 3.22 30497 11 7 25 7
Utica-Rome, NY 23 71 60 40 912.20 10.3 1671 2.5 47.4 320180 3.28 27305 5 2 11 2
Washington, DC-MD-VA 37 78 52 42 967.80 12.3 5308 25.9 59.7 3250822 3.25 41888 65 28 102 28
Wichita, KS 32 81 54 28 823.76 12.1 3665 7.5 51.6 411313 3.27 34812 4 2 1 2
Wilmington, DE-NJ-MD 33 76 56 65 1003.50 11.3 3152 12.1 47.3 523221 3.39 33927 14 11 42 11
Worcester, MA 24 70 56 65 895.70 11.1 3678 1.0 44.8 402918 3.25 29374 7 3 8 3
York, PA 33 76 54 62 911.82 9.0 9699 4.8 62.2 381255 3.22 28985 8 8 49 8
Youngstown-Warren, OH 28 72 58 38 954.44 10.7 3451 11.7 37.5 531350 3.48 28960 14 13 39 13