PROC SURVEYMEANS: Domain Analysis :: SAS/STAT(R) 9.22 User's Guide

The SURVEYMEANS Procedure

Example 86.2 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 86.2.1 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 86.2.1 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 86.2.1 displays the estimates of the mean and total for all analysis variables for the entire set of 800 companies, while Output 86.2.2 shows the mean and total estimates for each company type.

Output 86.2.2 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

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