# The SURVEYMEANS Procedure

### Example 92.2 Domain Analysis

Suppose that you are studying profiles of 800 top-performing companies to provide information about their impact on the economy. You are also interested in the company profiles within each market type. A sample of 66 companies is selected with unequal probability across market types. However, market type is not included in the sample design. Thus, the number of companies within each market type is a random variable in your sample. To obtain statistics within each market type, you should use domain analysis. The data of the 66 companies are saved in the following data set:

```data Company;
length Type \$14;
input Type\$ Asset Sale Value Profit Employee Weight;
datalines;
Other            2764.0  1828.0  1850.3   144.0   18.7   9.6
Energy          13246.2  4633.5  4387.7   462.9   24.3  42.6
Finance          3597.7   377.8    93.0    14.0    1.1  12.2
Transportation   6646.1  6414.2  2377.5   348.2   47.1  21.8
HiTech           1068.4  1689.8  1430.2    72.9    4.6   4.3
Manufacturing    1125.0  1719.4  1057.5    98.1   20.4   4.5
Other            1459.0  1241.4   452.7    24.5   20.1   5.5
Finance          2672.3   262.5   296.2    23.1    2.2   9.3
Finance           311.0   566.2   932.0    52.8    2.7   1.9
Energy           1148.6  1014.6   485.1    60.6    4.0   4.5
Finance          5327.0   572.4   372.9    25.2    4.2  17.7
Energy           1602.7   678.4   653.0    75.6    2.8   6.0
Energy           5808.8  1288.4  2007.0   318.8    5.9  19.2
Medical           268.8   204.4   820.9    45.6    3.7   1.8
Transportation   5222.6  2627.8  1910.0   245.6   22.8  17.4
Other             872.7  1419.4   939.3    69.7   12.2   3.7
Retail           4461.7  8946.8  4662.7   289.0  132.1  15.0
HiTech           6719.2  6942.0  8240.2   381.3   85.8  22.1
Retail            833.4  1538.8  1090.3    64.9   15.4   3.5
Finance           415.9   167.3  1126.8    56.8    0.7   2.2
HiTech            442.4  1139.9  1039.9    57.6   22.7   2.3
Other             801.5  1157.0   664.2    56.9   15.5   3.4
Finance          4954.8   468.8   366.4    41.7    3.0  16.5
Finance          2661.9   257.9   181.1    21.2    2.1   9.3
Finance          5345.8   530.1   337.4    36.4    4.3  17.8
Energy           3334.3  1644.7  1407.8   157.6    6.4  11.4
Manufacturing    1826.6  2671.7   483.2    71.3   25.3   6.7
Retail            618.8  2354.7   767.7    58.6   19.0   2.9
Retail           1529.1  6534.0   826.3    58.3   65.8   5.7
Manufacturing    4458.4  4824.5  3132.1    28.9   67.0  15.0
HiTech           5831.7  6611.1  9464.7   459.6   86.7  19.3
Medical          6468.3  4199.2  3170.4   270.1   59.5  21.3
Energy           1720.7   473.1   811.1    86.6    1.6   6.3
Energy           1679.7  1379.9   721.1    91.8    4.5   6.2
Retail           4018.2 16823.4  2038.3   178.1  162.0  13.6
Other             227.1   575.8  1083.8    62.6    1.9   1.6
Finance          3872.8   362.0   209.3    27.6    2.4  13.1
Retail           3359.3  4844.7  2651.4   224.1   75.6  11.5
Energy           1295.6   356.9   180.8   162.3    0.6   5.0
Energy           1658.0   626.6   688.0   126.0    3.5   6.1
Finance         12156.7  1345.5   680.7   106.6    9.4  39.2
HiTech           3982.6  4196.0  3946.8   313.9   64.3  13.5
Finance          8760.7   886.4  1006.9    90.0    7.5  28.5
Manufacturing    2362.2  3153.3  1080.0   137.0   25.2   8.4
Transportation   2499.9  3419.0   992.6    47.2   25.3   8.8
Energy           1430.4  1610.0   664.3    77.7    3.5   5.4
Energy          13666.5 15465.4  2736.7   411.4   26.6  43.9
Manufacturing    4069.3  4174.7  2907.6   289.2   38.2  13.7
Energy           2924.7   711.9  1067.8   146.7    3.4  10.1
Transportation   1262.1  1716.0   364.3    71.2   14.5   4.9
Medical           684.4   672.9   287.4    61.8    6.0   3.1
Energy           3069.3  1719.0  1439.0   196.4    4.9  10.6
Medical           246.5   318.8   924.1    43.8    3.1   1.7
Finance         11562.2  1128.5   580.4    64.2    6.7  37.3
Finance          9316.0  1059.4   816.5    95.9    8.0  30.2
Retail           1094.3  3848.0   563.3    29.4   44.7   4.4
Retail           1102.1  4878.3   932.4    65.2   47.3   4.4
HiTech            466.4   675.8   845.7    64.5    5.2   2.4
Manufacturing   10839.4  5468.7  1895.4   232.8   47.8  35.0
Manufacturing     733.5  2135.3    96.6    10.9    2.7   3.2
Manufacturing   10354.2 14477.4  5607.2   321.9  188.5  33.5
Energy           1902.1  2697.9   329.3    34.2    2.2   6.9
Other            2245.2  2132.2  2230.4   198.9    8.0   8.0
Transportation    949.4  1248.3   298.9    35.4   10.4   3.9
Retail           2834.4  2884.6   458.2    41.2   49.8   9.8
Retail           2621.1  6173.8  1992.7   183.7  115.1   9.2
;
```

For each company in your sample, the variables are defined as follows:

• `Type` identifies the type of market for the company.

• `Asset` contains the company’s assets, in millions of dollars.

• `Sale` contains sales, in millions of dollars.

• `Value` contains the market value of the company, in millions of dollars.

• `Profit` contains the profit, in millions of dollars.

• `Employee` contains the number of employees, in thousands.

• `Weight` contains the sampling weight.

The following SAS statements use PROC SURVEYMEANS to perform the domain analysis, estimating means, and other statistics for the overall population and also for the subpopulations (or domain) defined by market type. The DOMAIN statement specifies `Type` as the domain variable:

```title 'Top Companies Profile Study';
proc surveymeans data=Company total=800 mean sum;
var Asset Sale Value Profit Employee;
weight Weight;
domain Type;
run;
```

Output 92.2.1 shows that there are 66 observations in the sample. The sum of the sampling weights equals 799.8, which is close to the total number of companies in the study population.

Output 92.2.1: Company Profile Study

 Top Companies Profile Study

The SURVEYMEANS Procedure

Data Summary
Number of Observations 66
Sum of Weights 799.8

Statistics
Variable Mean Std Error of Mean Sum Std Dev
Asset 6523.488510 720.557075 5217486 1073829
Sale 4215.995799 839.132506 3371953 847885
Value 2145.935121 342.531720 1716319 359609
Profit 188.788210 25.057876 150993 30144
Employee 36.874869 7.787857 29493 7148.003298

The Statistics table in Output 92.2.1 displays the estimates of the mean and total for all analysis variables for the entire set of 800 companies, while Output 92.2.2 shows the mean and total estimates for each company type.

Output 92.2.2: Domain Analysis for Company Profile Study

Domain Analysis: Type
Type Variable Mean Std Error of Mean Sum Std Dev
Energy Asset 7868.302932 1941.699163 1449341 785962
Sale 5419.679099 2416.214417 998305 673373
Value 2249.297177 520.295162 414321 213580
Profit 289.564658 52.512141 53338 25927
Employee 14.151194 3.974697 2606.650000 1481.777769
Finance Asset 7890.190264 1057.185336 1855773 704506
Sale 829.210502 115.762531 195030 74436
Value 565.068197 76.964547 132904 48156
Profit 63.716837 10.099341 14986 5801.108513
Employee 5.806293 0.811555 1365.640000 519.658410
HiTech Asset 5031.959781 732.436967 321542 183302
Sale 5464.292019 731.296997 349168 196013
Value 6707.828482 1194.160584 428630 249154
Profit 346.407042 42.299004 22135 12223
Employee 70.766980 8.683595 4522.010000 2524.778281
Manufacturing Asset 7403.004250 1454.921083 888361 492577
Sale 7207.638833 2112.444703 864917 501679
Value 2986.442750 799.121544 358373 196979
Profit 211.933583 39.993255 25432 13322
Employee 83.314333 31.089019 9997.720000 6294.309490
Medical Asset 5046.570609 1218.444638 140799 131942
Sale 3313.219713 758.216303 92439 85655
Value 2561.614695 530.802245 71469 64663
Profit 218.682796 44.051447 6101.250000 5509.560969
Employee 46.518996 11.135955 1297.880000 1213.651734
Other Asset 1850.250000 338.128984 58838 31375
Sale 1620.784906 168.686773 51541 24593
Value 1432.820755 297.869828 45564 24204
Profit 115.089937 27.970560 3659.860000 2018.201371
Employee 14.306604 2.313733 454.950000 216.327710
Retail Asset 2939.845750 393.692369 235188 94605
Sale 7395.453500 1746.187580 591636 263263
Value 2103.863125 529.756409 168309 78304
Profit 157.171875 31.734253 12574 5478.281027
Employee 93.624000 15.726743 7489.920000 3093.832061
Transportation Asset 4712.047359 888.954411 267644 163516
Sale 4030.233275 1015.555708 228917 142669
Value 1703.330282 313.841326 96749 58947
Profit 224.762324 56.168925 12767 8287.585418
Employee 30.946303 6.786270 1757.750000 1066.586615