data sasuser.build (label = 'Quarterly building supply sales');
   input income starts mort sales;
   retain date '2OCT73'D;
   date = intnx( 'QTR' , date , 1 );
   format date yyq4.;
   label income = 'Disposable personal income'
         starts = 'U.S. housing starts'
         mort   = 'Mortgage rates'
         sales  = 'Sales of building supplies';
   cards;
 949.5 1628  8.59 17000.0
 973.1 1527  8.75 16788.3
 999.7 1209  9.08 15263.2
1016.2 1001  9.27 14329.7
1025.4  995  9.17 14851.3
1092.2 1050  8.94 15413.1
1095.7 1258  8.91 16344.8
1124.1 1365  9.01 16449.7
1153.3 1400  8.95 17254.9
1174.1 1428  8.93 16664.3
1193.3 1570  9.02 17583.5
1222.6 1770  9.07 18677.6
1248.0 1756  9.00 18878.3
1285.3 1937  8.96 19991.5
1319.1 2041  9.02 19938.8
1359.6 2146  9.08 20712.5
1398.0 1721  9.20 19019.8
1440.7 2104  9.38 20725.7
1482.1 2044  9.67 20620.8
1531.0 2078  9.91 21029.3
1580.2 1615 10.23 18903.3
1612.8 1815 10.58 20516.9
1663.8 1809 11.05 20644.7
1710.1 1593 11.51 19863.1
1765.1 1263 12.37 17962.0
1784.1 1053 13.53 17513.1
1840.6 1412 12.41 19117.3
1897.0 1535 14.23 19593.8
1947.3 1391 14.56 19352.3
1985.6 1170 15.50 18282.9
2042.0  966 18.00 16926.6
2086.4  903 17.00 16314.0
2115.3  919 17.55 17184.7
2148.9  909 16.37 17054.2
2229.2 1063 16.88 18283.6
2279.6 1239 15.94 19375.1
2323.6 1314 15.76 19936.3
2366.2 1388 15.38 20487.6
2449.8 1377 14.99 20466.3
2497.7 1366 14.78 20263.4
2535.8 1366 15.01 19950.3
;
run;

data sasuser.class(label = 'Student information' );
   input name $ 1-8 sex $ 11 age height weight;
   label name   = 'First name'
         sex    = 'Gender'
         age    = 'Age in years'
         height = 'Height in inches'
         weight = 'Weight in pounds';
   cards;
Alice     F 13 56.5  84.0
Becka     F 13 65.3  98.0
Gail      F 14 64.3  90.0
Karen     F 12 56.3  77.0
Kathy     F 12 59.8  84.5
Mary      F 15 66.5 112.0
Sandy     F 11 51.3  50.5
Sharon    F 15 62.5 112.5
Tammy     F 14 62.8 102.5
Alfred    M 14 69.0 112.5
Duke      M 14 63.5 102.5
Guido     M 15 67.0 133.0
James     M 12 57.3  83.0
Jeffrey   M 13 62.5  84.0
John      M 12 59.0  99.5
Philip    M 16 72.0 150.0
Robert    M 12 64.8 128.0
Thomas    M 11 57.5  85.0
William   M 15 66.5 112.0
run ;

data sasuser.crime (label = 'Crime rates per 100,000 pop. by state');
   input staten $1-15 state murder rape robbery assault burglary
         larceny auto;
   label staten = 'State name'
         state  = 'FIPS code';
   cards;
Alabama        01 14.2 25.2  96.8 278.3 1135.5 1881.9 280.7
Alaska         02 10.8 51.6  96.8 284.0 1331.7 3369.8 753.3
Arizona        04  9.5 34.2 138.2 312.3 2346.1 4467.4 439.5
Arkansas       05  8.8 27.6  83.2 203.4  972.6 1862.1 183.4
California     06 11.5 49.4 287.0 358.0 2139.4 3499.8 663.5
Colorado       08  6.3 42.0 170.7 292.9 1935.2 3903.2 477.1
Connecticut    09  4.2 16.8 129.5 131.8 1346.0 2620.7 593.2
Delaware       10  6.0 24.9 157.0 194.2 1682.6 3678.4 467.0
Florida        12 10.2 39.6 187.9 449.1 1859.9 3840.5 351.4
Georgia        13 11.7 31.1 140.5 256.5 1351.1 2170.2 297.9
Hawaii         15  7.2 25.5 128.0  64.1 1911.5 3920.4 489.4
Idaho          16  5.5 19.4  39.6 172.5 1050.8 2599.6 237.6
Illinois       17  9.9 21.8 211.3 209.0 1085.0 2828.5 528.6
Indiana        18  7.4 26.5 123.2 153.5 1086.2 2498.7 377.4
Iowa           19  2.3 10.6  41.2  89.8  812.5 2685.1 219.9
Kansas         20  6.6 22.0 100.7 180.5 1270.4 2739.3 244.3
Kentucky       21 10.1 19.1  81.1 123.3  872.2 1662.1 245.4
Louisiana      22 15.5 30.9 142.9 335.5 1165.5 2469.9 337.7
Maine          23  2.4 13.5  38.7 170.0 1253.1 2350.7 246.9
Maryland       24  8.0 34.8 292.1 358.9 1400.0 3177.7 428.5
Massachusetts  25  3.1 20.8 169.1 231.6 1532.2 2311.3 1140.1
Michigan       26  9.3 38.9 261.9 274.6 1522.7 3159.0 545.5
Minnesota      27  2.7 19.5  85.9  85.8 1134.7 2559.3 343.1
Mississippi    28 14.3 19.6  65.7 189.1  915.6 1239.9 144.4
Missouri       29  9.6 28.3 189.0 233.5 1318.3 2424.2 378.4
Montana        30  5.4 16.7  39.2 156.8  804.9 2773.2 309.2
Nebraska       31  3.9 18.1  64.7 112.7  760.0 2316.1 249.1
Nevada         32 15.8 49.1 323.1 355.0 2453.1 4212.6 559.2
New Hampshire  33  3.2 10.7  23.2  76.0 1041.7 2343.9 293.4
New Jersey     34  5.6 21.0 180.4 185.1 1435.8 2774.5 511.5
New Mexico     35  8.8 39.1 109.6 343.4 1418.7 3008.6 259.5
New York       36 10.7 29.4 472.6 319.1 1728.0 2782.0 745.8
North Carolina 37 10.6 17.0  61.3 318.3 1154.1 2037.8 192.1
North Dakota   38  0.9  9.0  13.3  43.8  446.1 1843.0 144.7
Ohio           39  7.8 27.3 190.5 181.1 1216.0 2696.8 400.4
Oklahoma       40  8.6 29.2  73.8 205.0 1288.2 2228.1 326.8
Oregon         41  4.9 39.9 124.1 286.9 1636.4 3506.1 388.9
Pennsylvania   42  5.6 19.0 130.3 128.0  877.5 1624.1 333.2
Rhode Island   44  3.6 10.5  86.5 201.0 1489.5 2844.1 791.4
South Carolina 45 11.9 33.0 105.9 485.3 1613.6 2342.4 245.1
South Dakota   46  2.0 13.5  17.9 155.7  570.5 1704.4 147.5
Tennessee      47 10.1 29.7 145.8 203.9 1259.7 1776.5 314.0
Texas          48 13.3 33.8 152.4 208.2 1603.1 2988.7 397.6
Utah           49  3.5 20.3  68.8 147.3 1171.6 3004.6 334.5
Vermont        50  1.4 15.9  30.8 101.2 1348.2 2201.0 265.2
Virginia       51  9.0 23.3  92.1 165.7  986.2 2521.2 226.7
Washington     53  4.3 39.6 106.2 224.8 1605.6 3386.9 360.3
West Virginia  54  6.0 13.2  42.2  90.9  597.4 1341.7 163.3
Wisconsin      55  2.8 12.9  52.2  63.7  846.9 2614.2 220.7
Wyoming        56  5.4 21.9  39.7 173.9  811.6 2772.2 282.0
;
run;

data sasuser.fitness(label = 'Exercise/fitness study data set');
   input age weight runtime rstpulse runpulse maxpulse oxygen group;
   label age      = 'Age in years'
         weight   = 'Weight in kg'
         runtime  = 'Min. to run 1.5 miles'
         rstpulse = 'Heart rate while resting'
         runpulse = 'Heart rate while running'
         maxpulse = 'Maximum heart rate'
         oxygen   = 'Oxygen consumption'
         group    = 'Experimental group';
   cards;
57 73.37 12.63 58 174 176 39.407   2
54 79.38 11.17 62 156 165 46.080   2
52 76.32 9.63 48 164 166 45.441    2
50 70.87 8.92 48 146 155 54.625    2
51 67.25 11.08 48 172 172 45.118   2
54 91.63 12.88 44 168 172 39.203   2
51 73.71 10.47 59 186 188 45.790   2
57 59.08 9.93 49 148 155 50.545    2
49 76.32 9.4 56 186 188 48.673     2
48 61.24 11.5 52 170 176 47.920    2
52 82.78 10.5 53 170 172 47.467    2
44 73.03 10.13 45 168 168 50.541   1
45 87.66 14.03 56 186 192 37.388   1
45 66.45 11.12 51 176 176 44.754   1
47 79.15 10.6 47 162 164 47.273    1
54 83.12 10.33 50 166 170 51.855   1
49 81.42 8.95 44 180 185 49.156    1
51 69.63 10.95 57 168 172 40.836   1
51 77.91 10.00 48 162 168 46.672   1
48 91.63 10.25 48 162 164 46.774   1
49 73.37 10.08 76 168 168 50.388   1
44 89.47 11.37 62 178 182 44.609   0
40 75.07 10.07 62 185 185 45.313   0
44 85.84 8.65 45 156 168 54.297    0
42 68.15 8.17 40 166 172 59.571    0
38 89.02 9.22 55 178 180 49.874    0
47 77.45 11.63 58 176 176 44.811   0
40 75.98 11.95 70 176 180 45.681   0
43 81.19 10.85 64 162 170 49.091   0
44 81.42 13.08 63 174 176 39.442   0
38 81.87 8.63 48 170 186 60.055    0
run;

data sasuser.highway (label = 'Motor vehicle accident data');
   length steering $ 7 seatbelt $ 3 speed $ 5 status $ 7 count 8;
   input steering seatbelt speed status $ count;
   label steering = 'Status of energy-absorbing steering'
         seatbelt = 'Seatbelt usage'
         speed    = 'Speed of car prior to impact'
         status   = 'Driver injury status'
         count    = 'Frequency of observations in category';
   cards;
absent   no    0-29  serious    31
absent   no    0-29  not      1419
absent   no   30-49  serious   191
absent   no   30-49  not      2004
absent   no     50+  serious   216
absent   no     50+  not      1030
absent   yes   0-29  serious     6
absent   yes   0-29  not       255
absent   yes  30-49  serious    14
absent   yes  30-49  not       339
absent   yes    50+  serious    19
absent   yes    50+  not       200
present  no    0-29  serious    22
present  no    0-29  not       652
present  no   30-49  serious    57
present  no   30-49  not       928
present  no     50+  serious   108
present  no     50+  not       515
present  yes   0-29  serious     4
present  yes   0-29  not       199
present  yes  30-49  serious    11
present  yes  30-49  not       265
present  yes    50+  serious    20
present  yes    50+  not       157
;
run;

data sasuser.houses(label = 'Residential housing for sale' );
   input style $ 1-8 sqfeet 10-13 bedrooms 15
         baths 17-19 street $ 21-36 price 40-45;
   label style    = "Style of homes"
         sqfeet   = "Square footage"
         bedrooms = "Number of bedrooms"
         baths    = "Number of bathrooms"
         street   = "Street address"
         price    = "Asking price";
   format price dollar12.;
   informat price comma12.;
   cards;
RANCH    1250 2 1   Sheppard Avenue     64000
SPLIT    1190 1 1   Rand Street         65850
CONDO    1400 2 1.5 Market Street       80050
TWOSTORY 1810 4 3   Garris Street      107250
RANCH    1500 3 3   Kemble Avenue       86650
SPLIT    1615 4 3   West Drive          94450
SPLIT    1305 3 1.5 Graham Avenue       73650
CONDO    1390 3 2.5 Hampshire Avenue    79350
TWOSTORY 1040 2 1   Sanders Road        55850
CONDO    2105 4 2.5 Jeans Avenue       127150
RANCH    1535 3 3   State Highway       89100
TWOSTORY 1240 2 1   Fairbanks Circle    69250
RANCH     720 1 1   Nicholson Drive     34550
TWOSTORY 1745 4 2.5 Highland Road      102950
CONDO    1860 2 2   Arcata Avenue      110700
run;

data sasuser.ingots (label = 'Foundry data');
   input heat soak r n;
   label n= 'Number tested'
         r= 'Number not ready for rolling'
         heat= 'Heating time'
         soak= 'Soaking time';
   cards;
 7 1.0 0 10
 7 1.7 0 17
 7 2.2 0  7
 7 2.8 0 12
 7 4.0 0  9
14 1.0 0 31
14 1.7 0 43
14 2.2 2 33
14 2.8 0 31
14 4.0 0 19
27 1.0 1 56
27 1.7 4 44
27 2.2 0 21
27 2.8 1 22
27 4.0 1 16
51 1.0 3 13
51 1.7 0  1
51 2.2 0  1
51 4.0 0  1
;
run;

data sasuser.oranges (label = 'Data on sale of oranges');
   length store day $ 1 price1 price2 sales1 sales2 8;
   input store day $ price1 price2 sales1 sales2;
   label day    = 'Day of week'
         price1 = 'Price of first variety of oranges'
         price2 = 'Price of second variety of oranges'
         sales1 = 'Sales of first variety of oranges'
         sales2 = 'Sales of second variety of oranges';
   cards;
1 1 37 61 11.3208  0.0047
1 2 37 37 12.9151  0.0037
1 3 45 53 18.8947  7.5429
1 4 41 41 14.6739  7.0652
1 5 57 41  8.6493 21.2085
1 6 49 33  9.5238 16.6667
2 1 49 49  7.6923  7.1154
2 2 53 53  0.0017  1.0000
2 3 53 45  8.0477 24.2176
2 4 53 53  6.7358  2.9361
2 5 61 37  6.1441 40.5720
2 6 49 65 21.7939  2.8324
3 1 53 45  4.2553  6.0284
3 2 57 57  0.0017  2.0906
3 3 49 49 11.0196 13.9329
3 4 53 53  6.2762  6.5551
3 5 53 45 13.2316 10.6870
3 6 53 53  5.0676  5.1351
4 1 57 57  5.6235  3.9120
4 2 49 49 14.9893  7.2805
4 3 53 53 13.7233 16.3105
4 4 53 45  6.0669 23.8494
4 5 53 53  8.1602  4.1543
4 6 61 37  1.4423 21.1538
5 1 45 45  6.9971  6.9971
5 2 53 45  5.2308  3.6923
5 3 57 57  8.2560 10.6679
5 4 49 49 14.5000 16.7500
5 5 53 53 20.7627 15.2542
5 6 53 45  3.6115 21.5442
6 1 53 53 11.3475  4.9645
6 2 53 45  9.4650 11.7284
6 3 53 53 22.6103 14.8897
6 4 61 37  0.0020 19.2000
6 5 49 65 20.5997  2.3468
6 6 37 37 28.1828 17.9543
;
run;

data sasuser.retail (label = 'Memphis retail sales');
   input sales @@;
   retain date '2OCT64'D;
   date = intnx( 'QTR' , date , 1 );
   format date yyq4.;
   label sales = 'Retail sales in millions of $';
   cards;
220 257 258 295
247 292 286 323
284 307 318 343
299 351 359 384
342 388 385 413
337 399 412 448
419 472 490 541
484 543 542 595
546 607 616 643
594 666 662 670
606 674 705 749
703 709 728 807
692 797 826 889
758 909 920 991
876 998
;
run;

data sasuser.veneer (label = 'Wood veneer wear data');
   input brand $ 1-5 wear 7-9;
   label brand = 'Brand of veneer'
         wear  = 'Amount of material worn away';
   cards;
ACME  2.3
ACME  2.1
ACME  2.4
ACME  2.5
CHAMP 2.2
CHAMP 2.3
CHAMP 2.4
CHAMP 2.6
AJAX  2.2
AJAX  2.0
AJAX  1.9
AJAX  2.1
TUFFY 2.4
TUFFY 2.7
TUFFY 2.6
TUFFY 2.7
XTRA  2.3
XTRA  2.5
XTRA  2.3
XTRA  2.4
;
run;
