### Stata Library Survey Sampling Examples

#### Introduction

Survey data generally have one or more of these three characteristics:
• sampling or probability weights
• clustering
• stratification

Stata takes theses characteristics into account through the use of survey procedures. Before issuing any survey commands it is necessary to set one or more of the following items:

• svyset pweight varname1 -- sets sampling weights
• svyset strata varname2 -- sets the strata
• svyset psu varname3 -- sets the primary sampling unit (cluster)
• svyset fpc varname4 -- set the finite population correction

Failure to analyze survey sampling designs without taking these characteristics into account can result in inaccurate point estimates and/or inaccurate estimates of standard errors.

In this unit we will be using data from the book Sampling of Populations by Levy and Lemeshow (1999) with permission of the authors.

#### Some Definitions

sampling fraction
The proportion of the population being sampled. If a district has 30 elementary schools and you sample four of them, then your population is 30 and your sampling fraction is 4/30.
pweight
The sampling weight which is the reciprocal of the sampling fraction. From our previous example, if the sampling fraction is 4/30 then the pweight is 30/4 = 7.5. Thus, each school in our sample represents 7.5 schools.
cluster
Groups, such as, counties, city blocks, schools, or households, that are sampled as a group.
psu
Many sampling designs involve multistage sampling, i.e., multiple levels of clusters. The psu indicates the first or primary level of clusters that are sampled.
fpc
finite population control is used in simple random sampling without replacement. fpc indicates the total number of psu's in the population.
strata
The division of a population into parts known strata for the purposes of drawing samples.
sampling frame
A list of all the elements in the population with some chance of being selected.

#### The Population

California requires that all students in public schools be tested each year. The State Department of Education then puts together the annual Academic Performance Index (API) which rates how a school is doing overall, in terms of the test scores. The file, apipop.dta, contains api ratings and demographic information on 6,194 schools in 757 school districts. To be included in the file schools must have at least 100 students.

Of course, in the normal course of events you wouldn't actually have access to data from the whole population. We were lucky in this instance that California collects and releases these data.

Let's try several computations on the population data.

use http://www.ats.ucla.edu/stat/stata/library/apipop

tabulate stype

stype |      Freq.     Percent        Cum.
------------+-----------------------------------
E |       4421       71.38       71.38
H |        755       12.19       83.56
M |       1018       16.44      100.00
------------+-----------------------------------
Total |       6194      100.00

summarize api00

Variable |     Obs        Mean   Std. Dev.       Min        Max
---------+-----------------------------------------------------
api00 |    6194    664.7126   128.2441        346        969

quietly summarize enroll

display %10.0fc r(sum)
3,811,472

regress api00 meals ell avg_ed

Source |       SS       df       MS                  Number of obs =    6016
---------+------------------------------               F(  3,  6012) = 5837.12
Model |  73775065.7     3  24591688.6               Prob > F      =  0.0000
Residual |  25328472.8  6012  4212.98616               R-squared     =  0.7444
Total |  99103538.5  6015  16476.0662               Root MSE      =  64.908

------------------------------------------------------------------------------
api00 |      Coef.   Std. Err.       t     P>|t|       [95% Conf. Interval]
---------+--------------------------------------------------------------------
meals |  -1.672069   .0568866    -29.393   0.000      -1.783587   -1.560551
ell |  -.6775632   .0616073    -10.998   0.000      -.7983355   -.5567908
avg_ed |   72.30502    2.09055     34.587   0.000       68.20679    76.40325
_cons |    558.443   7.969069     70.076   0.000       542.8207    574.0652
------------------------------------------------------------------------------


#### Simple Random Sampling Example

Let's take a simple random sample of 200 schools from the population file. This can be accomplished with the commands:
generate i = uniform()
sort i . keep in 1/200 
In this example, the sampling frame contains the 6,194 school so fpc = 6194 and the sampling weights (pw) = 6194/200 = 30.97.

Of course, in the real world you probably wouldn't take a sample of 200 school from a computer file of 6,194, you would just analyze the entire dataset. But suppose you had to go out to each school to collect the data that you needed, then it would take much less time and cost much less money to go to 200 schools than to over 6,000 schools.

The file apisrs.dta has a simple random sample of 200 cases.
use http://www.ats.ucla.edu/stat/stata/library/apisrs

tabulate stype

stype |      Freq.     Percent        Cum.
------------+-----------------------------------
E |        145       72.50       72.50
H |         25       12.50       85.00
M |         30       15.00      100.00
------------+-----------------------------------
Total |        200      100.00

tabulate dnum

district |
number |      Freq.     Percent        Cum.
------------+-----------------------------------
1 |          1        0.50        0.50
40 |          1        0.50        1.00
41 |          1        0.50        1.50
43 |          1        0.50        2.00
46 |          3        1.50        3.50
48 |          1        0.50        4.00
55 |          1        0.50        4.50
56 |          2        1.00        5.50
57 |          1        0.50        6.00
60 |          1        0.50        6.50
67 |          1        0.50        7.00
80 |          1        0.50        7.50
90 |          2        1.00        8.50
98 |          1        0.50        9.00
103 |          1        0.50        9.50
105 |          1        0.50       10.00
108 |          2        1.00       11.00
124 |          1        0.50       11.50
131 |          1        0.50       12.00
135 |          2        1.00       13.00
148 |          2        1.00       14.00
154 |          1        0.50       14.50
159 |          1        0.50       15.00
162 |          1        0.50       15.50
166 |          3        1.50       17.00
175 |          1        0.50       17.50
176 |          1        0.50       18.00
184 |          1        0.50       18.50
190 |          1        0.50       19.00
209 |          1        0.50       19.50
217 |          1        0.50       20.00
222 |          1        0.50       20.50
229 |          1        0.50       21.00
231 |          1        0.50       21.50
238 |          1        0.50       22.00
248 |          2        1.00       23.00
253 |          3        1.50       24.50
255 |          1        0.50       25.00
259 |          1        0.50       25.50
266 |          1        0.50       26.00
272 |          1        0.50       26.50
274 |          1        0.50       27.00
278 |          2        1.00       28.00
293 |          1        0.50       28.50
301 |          1        0.50       29.00
304 |          1        0.50       29.50
335 |          1        0.50       30.00
351 |          1        0.50       30.50
352 |          1        0.50       31.00
353 |          1        0.50       31.50
358 |          1        0.50       32.00
360 |          1        0.50       32.50
379 |          1        0.50       33.00
390 |          1        0.50       33.50
393 |          1        0.50       34.00
395 |          2        1.00       35.00
401 |         18        9.00       44.00
416 |          1        0.50       44.50
418 |          2        1.00       45.50
436 |          1        0.50       46.00
444 |          1        0.50       46.50
445 |          1        0.50       47.00
451 |          1        0.50       47.50
457 |          2        1.00       48.50
459 |          1        0.50       49.00
460 |          1        0.50       49.50
470 |          1        0.50       50.00
473 |          1        0.50       50.50
479 |          1        0.50       51.00
491 |          1        0.50       51.50
495 |          1        0.50       52.00
498 |          1        0.50       52.50
503 |          2        1.00       53.50
507 |          5        2.50       56.00
509 |          1        0.50       56.50
513 |          2        1.00       57.50
529 |          2        1.00       58.50
532 |          1        0.50       59.00
533 |          1        0.50       59.50
536 |          1        0.50       60.00
537 |          2        1.00       61.00
539 |          3        1.50       62.50
541 |          1        0.50       63.00
542 |          1        0.50       63.50
547 |          1        0.50       64.00
556 |          2        1.00       65.00
564 |          1        0.50       65.50
570 |          1        0.50       66.00
579 |          1        0.50       66.50
590 |          1        0.50       67.00
600 |          1        0.50       67.50
602 |          1        0.50       68.00
605 |          1        0.50       68.50
614 |          2        1.00       69.50
620 |          3        1.50       71.00
623 |          1        0.50       71.50
627 |          3        1.50       73.00
629 |          1        0.50       73.50
630 |          2        1.00       74.50
632 |          5        2.50       77.00
633 |          1        0.50       77.50
635 |          1        0.50       78.00
636 |          2        1.00       79.00
637 |          1        0.50       79.50
640 |          1        0.50       80.00
642 |          1        0.50       80.50
643 |          1        0.50       81.00
644 |          1        0.50       81.50
645 |          1        0.50       82.00
648 |          1        0.50       82.50
651 |          1        0.50       83.00
653 |          1        0.50       83.50
658 |          1        0.50       84.00
665 |          1        0.50       84.50
688 |          1        0.50       85.00
689 |          1        0.50       85.50
702 |          1        0.50       86.00
711 |          1        0.50       86.50
716 |          1        0.50       87.00
720 |          1        0.50       87.50
731 |          1        0.50       88.00
739 |          1        0.50       88.50
744 |          3        1.50       90.00
745 |          1        0.50       90.50
750 |          1        0.50       91.00
751 |          1        0.50       91.50
754 |          1        0.50       92.00
756 |          1        0.50       92.50
761 |          1        0.50       93.00
779 |          2        1.00       94.00
780 |          1        0.50       94.50
782 |          1        0.50       95.00
788 |          1        0.50       95.50
796 |          4        2.00       97.50
797 |          1        0.50       98.00
803 |          1        0.50       98.50
815 |          1        0.50       99.00
830 |          1        0.50       99.50
834 |          1        0.50      100.00
------------+-----------------------------------
Total |        200      100.00

svyset
pweight is pw
fpc is fpc

svymean api00

Survey mean estimation

pweight:  pw                                      Number of obs    =       200
Strata:   <one>                                   Number of strata =         1
PSU:      <observations>                          Number of PSUs   =       200
FPC:      fpc                                     Population size  = 6193.9999

------------------------------------------------------------------------------
Mean |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
api00 |    660.165    9.186887    642.0489    678.2811           1
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.
Weights must represent population totals for deff to be correct when
using an FPC.  Note: deft is invariant to the scale of weights.

svytotal enroll

Survey total estimation

pweight:  pw                                      Number of obs    =       200
Strata:   <one>                                   Number of strata =         1
PSU:      <observations>                          Number of PSUs   =       200
FPC:      fpc                                     Population size  = 6193.9999

------------------------------------------------------------------------------
Total |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
enroll |    3924828    220705.4     3489607     4360049           1
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.
Weights must represent population totals for deff to be correct when
using an FPC.  Note: deft is invariant to the scale of weights.

svyreg api00 meals ell avg_ed

Survey linear regression

pweight:  pw                                      Number of obs    =       200
Strata:   <one>                                   Number of strata =         1
PSU:      <observations>                          Number of PSUs   =       200
FPC:      fpc                                     Population size  = 6193.9999
F(   3,    197)  =    217.11
Prob > F         =    0.0000
R-squared        =    0.7640

------------------------------------------------------------------------------
api00 |      Coef.    Std. Err.      t    P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
meals |  -1.367668    .3544273    -3.86   0.000   -2.066583   -.6687524
ell |  -1.266818    .3895673    -3.25   0.001   -2.035028   -.4986079
avg_ed |   75.49145    14.28649     5.28   0.000    47.31912    103.6638
_cons |   544.7082    56.15402     9.70   0.000    433.9749    655.4414
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.

#### Stratified Random Sampling Example

This time instead of taking a simple random sample of the whole population we will take separate simple random samples of elementary schools, high school and middle schools. This is known as stratified random sampling. We will sample 100 elementary schools, 50 high schools and 50 middle schools.

In this example, there are three sampling frames: 4,421 elementary schools, 755 high schools, and 1,018 middle schools.

The file apistrat.dta contains the data for the stratified random sample.
use http://www.ats.ucla.edu/stat/stata/library/apistrat

tabulate stype

stype |      Freq.     Percent        Cum.
------------+-----------------------------------
E |        100       50.00       50.00
H |         50       25.00       75.00
M |         50       25.00      100.00
------------+-----------------------------------
Total |        200      100.00

tabulate dnum

district |
number |      Freq.     Percent        Cum.
------------+-----------------------------------
19 |          1        0.50        0.50
20 |          1        0.50        1.00
25 |          1        0.50        1.50
27 |          1        0.50        2.00
40 |          1        0.50        2.50
41 |          1        0.50        3.00
64 |          1        0.50        3.50
69 |          1        0.50        4.00
105 |          1        0.50        4.50
108 |          1        0.50        5.00
114 |          1        0.50        5.50
135 |          1        0.50        6.00
140 |          1        0.50        6.50
148 |          2        1.00        7.50
153 |          5        2.50       10.00
155 |          1        0.50       10.50
158 |          2        1.00       11.50
160 |          1        0.50       12.00
162 |          1        0.50       12.50
176 |          1        0.50       13.00
182 |          1        0.50       13.50
185 |          2        1.00       14.50
196 |          1        0.50       15.00
202 |          1        0.50       15.50
208 |          1        0.50       16.00
214 |          1        0.50       16.50
215 |          2        1.00       17.50
216 |          1        0.50       18.00
223 |          1        0.50       18.50
225 |          1        0.50       19.00
226 |          1        0.50       19.50
233 |          1        0.50       20.00
238 |          2        1.00       21.00
247 |          1        0.50       21.50
253 |          4        2.00       23.50
259 |          4        2.00       25.50
266 |          2        1.00       26.50
270 |          2        1.00       27.50
273 |          1        0.50       28.00
275 |          1        0.50       28.50
279 |          1        0.50       29.00
284 |          1        0.50       29.50
294 |          1        0.50       30.00
308 |          1        0.50       30.50
316 |          1        0.50       31.00
324 |          1        0.50       31.50
333 |          1        0.50       32.00
339 |          1        0.50       32.50
348 |          1        0.50       33.00
349 |          1        0.50       33.50
351 |          1        0.50       34.00
358 |          1        0.50       34.50
364 |          1        0.50       35.00
376 |          1        0.50       35.50
382 |          2        1.00       36.50
390 |          1        0.50       37.00
394 |          1        0.50       37.50
395 |          3        1.50       39.00
401 |         16        8.00       47.00
419 |          1        0.50       47.50
423 |          1        0.50       48.00
432 |          1        0.50       48.50
439 |          1        0.50       49.00
448 |          1        0.50       49.50
450 |          1        0.50       50.00
457 |          1        0.50       50.50
459 |          1        0.50       51.00
460 |          1        0.50       51.50
465 |          1        0.50       52.00
473 |          3        1.50       53.50
475 |          1        0.50       54.00
478 |          1        0.50       54.50
484 |          1        0.50       55.00
492 |          1        0.50       55.50
495 |          1        0.50       56.00
497 |          1        0.50       56.50
498 |          1        0.50       57.00
499 |          1        0.50       57.50
501 |          1        0.50       58.00
507 |          4        2.00       60.00
509 |          1        0.50       60.50
512 |          1        0.50       61.00
513 |          2        1.00       62.00
514 |          1        0.50       62.50
515 |          1        0.50       63.00
531 |          2        1.00       64.00
532 |          1        0.50       64.50
537 |          1        0.50       65.00
541 |          3        1.50       66.50
550 |          1        0.50       67.00
554 |          1        0.50       67.50
569 |          1        0.50       68.00
575 |          2        1.00       69.00
590 |          2        1.00       70.00
596 |          1        0.50       70.50
602 |          2        1.00       71.50
605 |          1        0.50       72.00
620 |          2        1.00       73.00
621 |          3        1.50       74.50
627 |          1        0.50       75.00
630 |          2        1.00       76.00
632 |          4        2.00       78.00
635 |          2        1.00       79.00
636 |          2        1.00       80.00
639 |          2        1.00       81.00
650 |          1        0.50       81.50
653 |          2        1.00       82.50
655 |          1        0.50       83.00
656 |          1        0.50       83.50
662 |          1        0.50       84.00
685 |          1        0.50       84.50
689 |          5        2.50       87.00
702 |          1        0.50       87.50
706 |          1        0.50       88.00
722 |          1        0.50       88.50
725 |          2        1.00       89.50
735 |          1        0.50       90.00
738 |          1        0.50       90.50
751 |          1        0.50       91.00
756 |          1        0.50       91.50
760 |          1        0.50       92.00
766 |          1        0.50       92.50
767 |          2        1.00       93.50
774 |          1        0.50       94.00
780 |          2        1.00       95.00
781 |          1        0.50       95.50
784 |          1        0.50       96.00
787 |          1        0.50       96.50
796 |          1        0.50       97.00
797 |          1        0.50       97.50
802 |          1        0.50       98.00
806 |          1        0.50       98.50
813 |          1        0.50       99.00
819 |          1        0.50       99.50
825 |          1        0.50      100.00
------------+-----------------------------------
Total |        200      100.00

svyset
pweight is pw
strata is stype
fpc is fpc

svymean api00

Survey mean estimation

pweight:  pw                                      Number of obs    =       200
Strata:   stype                                   Number of strata =         3
PSU:      <observations>                          Number of PSUs   =       200
FPC:      fpc                                     Population size  =      6194

------------------------------------------------------------------------------
Mean |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
api00 |   662.2874    9.408941    643.7322    680.8425    1.204457
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.
Weights must represent population totals for deff to be correct when
using an FPC.  Note: deft is invariant to the scale of weights.

svytotal enroll

Survey total estimation

pweight:  pw                                      Number of obs    =       200
Strata:   stype                                   Number of strata =         3
PSU:      <observations>                          Number of PSUs   =       200
FPC:      fpc                                     Population size  =      6194

------------------------------------------------------------------------------
Total |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
enroll |    3687178    114641.7     3461095     3913260    .3620181
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.
Weights must represent population totals for deff to be correct when
using an FPC.  Note: deft is invariant to the scale of weights.

svyreg api00 meals ell avg_ed

Survey linear regression

pweight:  pw                                      Number of obs    =       200
Strata:   stype                                   Number of strata =         3
PSU:                                Number of PSUs   =       200
FPC:      fpc                                     Population size  =      6194
F(   3,    195)  =    190.97
Prob > F         =    0.0000
R-squared        =    0.7125

------------------------------------------------------------------------------
api00 |      Coef.    Std. Err.      t    P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
meals |  -1.818234    .4076227    -4.46   0.000   -2.622098    -1.01437
ell |  -.0191524    .3890413    -0.05   0.961   -.7863726    .7480679
avg_ed |   77.47879    16.93665     4.57   0.000    44.07838    110.8792
_cons |   534.4453    65.57342     8.15   0.000    405.1294    663.7613
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.

#### One-Stage Cluster Sampling

Another approach to sampling from the population is cluster sampling. In this example we will use school districts as the cluster or primary sampling units. We will take a random sample of 15 school districts and look at all of the schools in each one.

In this example, the sampling frame contains the 757 school districts.

The file apiclus1.dta will contain the data for the one-stage cluster sampling design.
use http://www.ats.ucla.edu/stat/stata/library/apiclus1

tabulate stype

stype |      Freq.     Percent        Cum.
------------+-----------------------------------
E |        144       78.69       78.69
H |         14        7.65       86.34
M |         25       13.66      100.00
------------+-----------------------------------
Total |        183      100.00

tabulate dnum

district |
number |      Freq.     Percent        Cum.
------------+-----------------------------------
61 |         13        7.10        7.10
135 |         34       18.58       25.68
178 |          4        2.19       27.87
197 |         13        7.10       34.97
255 |         16        8.74       43.72
406 |          2        1.09       44.81
413 |          1        0.55       45.36
437 |          4        2.19       47.54
448 |         12        6.56       54.10
510 |         21       11.48       65.57
568 |          9        4.92       70.49
637 |         11        6.01       76.50
716 |         37       20.22       96.72
778 |          2        1.09       97.81
815 |          4        2.19      100.00
------------+-----------------------------------
Total |        183      100.00

svyset
pweight is pw
psu is dnum
fpc is fpc
/* list fpc pw dnum -- to see the values for these items */

svymean api00

Survey mean estimation

pweight:  pw                                      Number of obs    =       183
Strata:   <one>                                   Number of strata =         1
PSU:      dnum                                    Number of PSUs   =        15
FPC:      fpc                                     Population size  = 6194.0003

------------------------------------------------------------------------------
Mean |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
api00 |   644.1694    23.54224    593.6763    694.6625    9.345869
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.
Weights must represent population totals for deff to be correct when
using an FPC.  Note: deft is invariant to the scale of weights.

svytotal enroll

Survey total estimation

pweight:  pw                                      Number of obs    =       183
Strata:   <one>                                   Number of strata =         1
PSU:      dnum                                    Number of PSUs   =        15
FPC:      fpc                                     Population size  = 6194.0003

------------------------------------------------------------------------------
Total |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
enroll |    3404940      932235     1405495     5404385    31.31066
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.
Weights must represent population totals for deff to be correct when
using an FPC.  Note: deft is invariant to the scale of weights.

svyreg api00 meals ell avg_ed

Survey linear regression

pweight:  pw                                      Number of obs    =       157
Strata:   <one>                                   Number of strata =         1
PSU:      dnum                                    Number of PSUs   =        15
FPC:      fpc                                     Population size  = 5313.9784
F(   3,     12)  =     54.36
Prob > F         =    0.0000
R-squared        =    0.6978

------------------------------------------------------------------------------
api00 |      Coef.    Std. Err.       t     P>|t|      [95% Conf. Interval]
---------+--------------------------------------------------------------------
meals |  -2.948702    .3266161     -9.028   0.000     -3.649224    -2.24818
ell |  -.2227005    .3938377     -0.565   0.581     -1.067398    .6219974
avg_ed |   16.42832    15.32151      1.072   0.302     -16.43304    49.28968
_cons |   755.4386    55.61202     13.584   0.000      636.1626    874.7145
------------------------------------------------------------------------------
Finite population correction (FPC) assumes simple random sampling without
replacement of PSUs within each stratum with no subsampling within PSUs.

#### Two-Stage Cluster Sampling

In this two-stage cluster sampling design we will first randomly sample 40 school districts from all districts. After selecting the 40 school districts we will randomly sample five schools from each district. Obviously, any district that has five or fewer schools will have all of their schools selected.

Once again, the sampling frame contains the 757 school districts.

The file apiclus2.dta contains the data for the two-stage cluster sampling design.
use http://www.ats.ucla.edu/stat/stata/library/apiclus2

tabulate stype

stype |      Freq.     Percent        Cum.
------------+-----------------------------------
E |         83       65.87       65.87
H |         20       15.87       81.75
M |         23       18.25      100.00
------------+-----------------------------------
Total |        126      100.00

tabulate dnum

district |
number |      Freq.     Percent        Cum.
------------+-----------------------------------
15 |          1        0.79        0.79
63 |          1        0.79        1.59
83 |          3        2.38        3.97
117 |          1        0.79        4.76
132 |          3        2.38        7.14
152 |          3        2.38        9.52
173 |          4        3.17       12.70
176 |          1        0.79       13.49
198 |          4        3.17       16.67
200 |          5        3.97       20.63
228 |          2        1.59       22.22
264 |          1        0.79       23.02
295 |          5        3.97       26.98
302 |          4        3.17       30.16
403 |          5        3.97       34.13
452 |          4        3.17       37.30
456 |          1        0.79       38.10
480 |          5        3.97       42.06
523 |          2        1.59       43.65
534 |          5        3.97       47.62
549 |          5        3.97       51.59
552 |          2        1.59       53.17
570 |          5        3.97       57.14
574 |          1        0.79       57.94
575 |          5        3.97       61.90
596 |          5        3.97       65.87
620 |          5        3.97       69.84
638 |          5        3.97       73.81
639 |          5        3.97       77.78
674 |          2        1.59       79.37
679 |          4        3.17       82.54
687 |          3        2.38       84.92
701 |          2        1.59       86.51
711 |          2        1.59       88.10
719 |          1        0.79       88.89
731 |          5        3.97       92.86
742 |          1        0.79       93.65
768 |          2        1.59       95.24
781 |          5        3.97       99.21
795 |          1        0.79      100.00
------------+-----------------------------------
Total |        126      100.00

svyset
pweight is pw
psu is dnum
/* list pw dnum -- to see the values for these items */

svymean api00

Survey mean estimation

pweight:  pw                                      Number of obs    =       126
Strata:                                      Number of strata =         1
PSU:      dnum                                    Number of PSUs   =        40
Population size  = 5128.6749

------------------------------------------------------------------------------
Mean |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
api00 |   670.8118    30.71158    608.6918    732.9318    6.347638
------------------------------------------------------------------------------

svytotal enroll

Survey total estimation

pweight:  pw                                      Number of obs    =       120
Strata:                                      Number of strata =         1
PSU:      dnum                                    Number of PSUs   =        38
Population size  = 5015.1249

------------------------------------------------------------------------------
Total |   Estimate    Std. Err.   [95% Conf. Interval]        Deff
---------+--------------------------------------------------------------------
enroll |    2639273    815060.9    987802.5     4290743    24.53485
------------------------------------------------------------------------------

svyreg api00 meals ell avg_ed

Survey linear regression

pweight:  pw                                      Number of obs    =       126
Strata:                                      Number of strata =         1
PSU:      dnum                                    Number of PSUs   =        40
Population size  = 5128.6749
F(   3,     37)  =    200.50
Prob > F         =    0.0000
R-squared        =    0.7405

------------------------------------------------------------------------------
api00 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
meals |   .4461714   .4670056     0.96   0.345    -.4984366    1.390779
ell |  -1.922801   .8664354    -2.22   0.032    -3.675332   -.1702702
avg_ed |   134.7738     19.193     7.02   0.000     95.95232    173.5953
_cons |   306.6302   79.29093     3.87   0.000     146.2492    467.0112
------------------------------------------------------------------------------

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