This example illustrates the use of the SURVEYREG procedure to perform a regression in a stratified sample design. Consider a population of 235 farms producing corn in Nebraska and Iowa. You are interested in the relationship between corn yield (CornYield) and total farm size (FarmArea).
Each state is divided into several regions, and each region is used as a stratum. Within each stratum, a simple random sample with replacement is drawn. A total of 19 farms is selected by using a stratified simple random sample. The sample size and population size within each stratum are displayed in Table 90.9.
Number of Farms 


Stratum 
State 
Region 
Population 
Sample 
1 
Iowa 
1 
100 
3 
2 
2 
50 
5 

3 
3 
15 
3 

4 
Nebraska 
1 
30 
6 
5 
2 
40 
2 

Total 
235 
19 
Three models for the data are considered:
Model I — Common intercept and slope:
Model II — Common intercept, different slope:
Model III — Different intercept and different slope:
Data from the stratified sample are saved in the SAS data set Farms. In the data set Farms, the variable Weight represents the sampling weight. In the following DATA step, the sampling weights are the reciprocals of selection probabilities:
data Farms; input State $ Region FarmArea CornYield Weight; datalines; Iowa 1 100 54 33.333 Iowa 1 83 25 33.333 Iowa 1 25 10 33.333 Iowa 2 120 83 10.000 Iowa 2 50 35 10.000 Iowa 2 110 65 10.000 Iowa 2 60 35 10.000 Iowa 2 45 20 10.000 Iowa 3 23 5 5.000 Iowa 3 10 8 5.000 Iowa 3 350 125 5.000 Nebraska 1 130 20 5.000 Nebraska 1 245 25 5.000 Nebraska 1 150 33 5.000 Nebraska 1 263 50 5.000 Nebraska 1 320 47 5.000 Nebraska 1 204 25 5.000 Nebraska 2 80 11 20.000 Nebraska 2 48 8 20.000 ;
The information about population size in each stratum is saved in the SAS data set StratumTotals:
data StratumTotals; input State $ Region _TOTAL_; datalines; Iowa 1 100 Iowa 2 50 Iowa 3 15 Nebraska 1 30 Nebraska 2 40 ;
Using the sample data from the data set Farms and the control information data from the data set StratumTotals, you can fit Model I by using PROC SURVEYREG with the following statements:
title1 'Analysis of Farm Area and Corn Yield'; title2 'Model I: Same Intercept and Slope'; proc surveyreg data=Farms total=StratumTotals; strata State Region / list; model CornYield = FarmArea / covB; weight Weight; run;
Output 90.4.1 displays the data summary and stratification information fitting Model I. The sampling rates are automatically computed by the procedure based on the sample sizes and the population totals in strata.
Analysis of Farm Area and Corn Yield 
Model I: Same Intercept and Slope 
Data Summary  

Number of Observations  19 
Sum of Weights  234.99900 
Weighted Mean of CornYield  31.56029 
Weighted Sum of CornYield  7416.6 
Design Summary  

Number of Strata  5 
Fit Statistics  

Rsquare  0.3882 
Root MSE  20.6422 
Denominator DF  14 
Stratum Information  

Stratum Index 
State  Region  N Obs  Population Total  Sampling Rate 
1  Iowa  1  3  100  3.00% 
2  2  5  50  10.0%  
3  3  3  15  20.0%  
4  Nebraska  1  6  30  20.0% 
5  2  2  40  5.00% 
Output 90.4.2 displays tests of model effects and the estimated regression coefficients.
Tests of Model Effects  

Effect  Num DF  F Value  Pr > F 
Model  1  21.74  0.0004 
Intercept  1  4.93  0.0433 
FarmArea  1  21.74  0.0004 
Note:  The denominator degrees of freedom for the F tests is 14. 
Estimated Regression Coefficients  

Parameter  Estimate  Standard Error  t Value  Pr > t 
Intercept  11.8162978  5.31981027  2.22  0.0433 
FarmArea  0.2126576  0.04560949  4.66  0.0004 
Note:  The denominator degrees of freedom for the t tests is 14. 
Covariance of Estimated Regression Coefficients  

Intercept  FarmArea  
Intercept  28.300381277  0.146471538 
FarmArea  0.146471538  0.0020802259 
Alternatively, you can assume that the linear relationship between corn yield (CornYield) and farm area (FarmArea) is different among the states (Model II). In order to analyze the data by using this model, you create auxiliary variables FarmAreaNE and FarmAreaIA to represent farm area in different states:
The following statements create these variables in a new data set called FarmsByState and use PROC SURVEYREG to fit Model II:
data FarmsByState; set Farms; if State='Iowa' then do; FarmAreaIA=FarmArea; FarmAreaNE=0; end; else do; FarmAreaIA=0; FarmAreaNE=FarmArea; end; run;
The following statements perform the regression by using the new data set FarmsByState. The analysis uses the auxiliary variables FarmAreaIA and FarmAreaNE as the regressors:
title1 'Analysis of Farm Area and Corn Yield'; title2 'Model II: Same Intercept, Different Slopes'; proc surveyreg data=FarmsByState total=StratumTotals; strata State Region; model CornYield = FarmAreaIA FarmAreaNE / covB; weight Weight; run;
Output 90.4.3 displays the fit statistics and parameter estimates. The estimated slope parameters for each state are quite different from the estimated slope in Model I. The results from the regression show that Model II fits these data better than Model I.
Analysis of Farm Area and Corn Yield 
Model II: Same Intercept, Different Slopes 
Fit Statistics  

Rsquare  0.8158 
Root MSE  11.6759 
Denominator DF  14 
Estimated Regression Coefficients  

Parameter  Estimate  Standard Error  t Value  Pr > t 
Intercept  4.04234816  3.80934848  1.06  0.3066 
FarmAreaIA  0.41696069  0.05971129  6.98  <.0001 
FarmAreaNE  0.12851012  0.02495495  5.15  0.0001 
Note:  The denominator degrees of freedom for the t tests is 14. 
Covariance of Estimated Regression Coefficients  

Intercept  FarmAreaIA  FarmAreaNE  
Intercept  14.511135861  0.118001232  0.079908772 
FarmAreaIA  0.118001232  0.0035654381  0.0006501109 
FarmAreaNE  0.079908772  0.0006501109  0.0006227496 
For Model III, different intercepts are used for the linear relationship in two states. The following statements illustrate the use of the NOINT option in the MODEL statement associated with the CLASS statement to fit Model III:
title1 'Analysis of Farm Area and Corn Yield'; title2 'Model III: Different Intercepts and Slopes'; proc surveyreg data=FarmsByState total=StratumTotals; strata State Region; class State; model CornYield = State FarmAreaIA FarmAreaNE / noint covB solution; weight Weight; run;
The model statement includes the classification effect State as a regressor. Therefore, the parameter estimates for effect State present the intercepts in two states.
Output 90.4.4 displays the regression results for fitting Model III, including parameter estimates, and covariance matrix of the regression coefficients. The estimated covariance matrix shows a lack of correlation between the regression coefficients from different states. This suggests that Model III might be the best choice for building a model for farm area and corn yield in these two states.
However, some statistics remain the same under different regression models—for example, Weighted Mean of CornYield. These estimators do not rely on the particular model you use.
Analysis of Farm Area and Corn Yield 
Model III: Different Intercepts and Slopes 
Fit Statistics  

Rsquare  0.9300 
Root MSE  11.9810 
Denominator DF  14 
Estimated Regression Coefficients  

Parameter  Estimate  Standard Error  t Value  Pr > t 
State Iowa  5.27797099  5.27170400  1.00  0.3337 
State Nebraska  0.65275201  1.70031616  0.38  0.7068 
FarmAreaIA  0.40680971  0.06458426  6.30  <.0001 
FarmAreaNE  0.14630563  0.01997085  7.33  <.0001 
Note:  The denominator degrees of freedom for the t tests is 14. 
Covariance of Estimated Regression Coefficients  

State Iowa  State Nebraska  FarmAreaIA  FarmAreaNE  
State Iowa  27.790863033  0  0.205517205  0 
State Nebraska  0  2.8910750385  0  0.027354011 
FarmAreaIA  0.205517205  0  0.0041711265  0 
FarmAreaNE  0  0.027354011  0  0.0003988349 