In this project you are to develop a good prediction equation for "Mortality".
You should be able to copy and paste the following data set into a Minitab
worksheet. You are to randomly select 5 data points with no missing values
from the data set and set them aside. The following commands will
generate 5 random integers from the first 60 integers -
RANDOM 5 C20;
INTEGER 1 60.
You are not to use these points in developing your model. You
can do this by placing the missing value symbol * as the ‘Mortality’ observation
for each of these 5 points. Minitab will then exclude these
points in any analyses conducted. Don't forget to save your modified
data set on your flash drive. After you have your final model, find prediction
intervals for these points and see how well you did!
The data include information on the social and economic conditions in these areas, on their climate, and some indices of air pollution potentials.
city JanTemp JulyTemp RelHum Rain Mortality Educatn PopDens %NoWhite %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-Schen,NY 23 72 57 35 997.87 11.0 4281 3.5 50.7 835880 3.14 31458 8 10 39 10
Allentown,PA 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,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 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 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,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
FortWorth,TX 45 85 53 31 891.71 11.4 1844 11.5 48.1 * 3.22 * 1 1 1 1
GrandRapids,MI 24 72 61 31 871.34 10.9 3226 5.1 45.2 601680 3.37 29915 5 3 10 3
Greensboro,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
KansasCity,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
LosAngeles,CA 53 68 47 11 861.26 12.1 4700 7.8 48.9 7477503 2.66 36624 648 319 130 319
Louisville,KY 35 71 57 30 989.26 9.9 4474 13.1 42.6 956756 3.37 29621 38 37 193 37
Memphis,TN 42 82 59 50 1006.49 10.4 3497 36.7 43.3 913472 3.49 27910 15 18 34 18
Miami,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,MN 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
NewHaven,CT 30 72 58 46 923.23 11.3 3327 8.8 45.3 500474 3.16 34364 4 3 8 3
NewOrleans,LA 54 81 62 54 1113.16 9.7 3172 31.4 45.5 1256256 3.36 32704 20 17 1 17
NewYork,NY 33 77 58 42 994.65 10.7 7462 11.3 48.7 8274961 3.03 36047 41 26 108 26
Philadelphia,PA 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,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 32 79 57 34 953.56 9.7 5160 17.2 45.1 1808621 3.23 34546 31 15 68 15
SanDiego,CA 55 70 61 10 839.71 12.1 3033 5.9 51.0 1861846 3.11 32586 144 66 20 66
SanFran,CA 48 63 71 18 911.70 12.2 4253 13.7 51.2 1488871 2.92 47966 311 171 86 171
SanJose,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 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 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,OH 28 72 58 38 954.44 10.7 3451 11.7 37.5 531350 3.48 28960 14 13 39 13