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The examples below use Stata 7 or 8. If you are using Stata version 9, please see this page.Page 64, figure 3.1
use http://www.ats.ucla.edu/stat/stata/examples/lohr/agsrs.dta, clear
twoway (scatter acres92 acres87) (lfit acres92 acres87), ylabel( , nogrid angle(0)) ///
xtitle("Millions of Acres Devoted to Farms (1987)") ///
ytitle("Millions of Acres Devoted to Farms (1992)")
Page 65, table 3.1
di 297897.0467/301953.7233
gen residual = acres92 - .98656524*acres87
list county state acres92 acres87 residual in 1/6
+-----------------------------------------------------------+
| county state acres92 acres87 residual |
|-----------------------------------------------------------|
1. | COFFEE COUNTY AL 175209 179311 -1693 |
2. | COLBERT COUNTY AL 138135 145104 -5019.563 |
3. | LAMAR COUNTY AL 56102 59861 -2954.782 |
4. | MARENGO COUNTY AL 199117 220526 -18446.29 |
5. | MARION COUNTY AL 89228 105586 -14939.48 |
|-----------------------------------------------------------|
6. | TUSCALOOSA COUNTY AL 96194 120542 -22728.55 |
+-----------------------------------------------------------+
list county state acres92 acres87 residual in -5/l
+----------------------------------------------------------+
| county state acres92 acres87 residual |
|----------------------------------------------------------|
296. | OZAUKEE COUNTY WI 78772 85201 -5284.345 |
297. | ROCK COUNTY WI 343115 357751 -9829.701 |
298. | KANAWHA COUNTY WV 19956 21369 -1125.913 |
299. | PLEASANTS COUNTY WV 15650 15716 145.1407 |
300. | PUTNAM COUNTY WV 55827 55635 939.4429 |
+----------------------------------------------------------+
tabstat acres92 acres87 residual, s(sum mean sd) format(%11.0g)
stats | acres92 acres87 residual
---------+------------------------------
sum | 89369114 90586117-.244007111
mean | 297897.047 301953.723-.000813357
sd | 344551.895 344829.596 31657.2178
----------------------------------------
Page 72, table 3.3
clear
input tree x y
1 1 0
2 0 0
3 8 1
4 2 2
5 76 10
6 60 15
7 25 3
8 2 2
9 1 1
10 31 27
end
list
+----------------+
| tree x y |
|----------------|
1. | 1 1 0 |
2. | 2 0 0 |
3. | 3 8 1 |
4. | 4 2 2 |
5. | 5 76 10 |
|----------------|
6. | 6 60 15 |
7. | 7 25 3 |
8. | 8 2 2 |
9. | 9 1 1 |
10. | 10 31 27 |
+----------------+
tabstat x y, s(sum mean sd)
stats | x y
---------+--------------------
sum | 206 61
mean | 20.6 6.1
sd | 27.47201 8.824839
------------------------------
Page 73, figure 3.4
graph twoway (scatter y x) (function y = .2961*x, range(0 80)), ylabel( , nogrid angle(0))
Page 73 in the middle
di 6.1/20.6 2961165
Page 75 in the middle
clear
input photo field
10 15
12 14
7 9
13 14
13 8
6 5
17 18
16 15
15 13
10 15
14 11
12 15
10 12
5 8
12 13
10 9
10 11
9 12
6 9
11 12
7 13
9 11
11 10
10 9
10 8
end
list
+---------------+
| photo field |
|---------------|
1. | 10 15 |
2. | 12 14 |
3. | 7 9 |
4. | 13 14 |
5. | 13 8 |
|---------------|
6. | 6 5 |
7. | 17 18 |
8. | 16 15 |
9. | 15 13 |
10. | 10 15 |
|---------------|
11. | 14 11 |
12. | 12 15 |
13. | 10 12 |
14. | 5 8 |
15. | 12 13 |
|---------------|
16. | 10 9 |
17. | 10 11 |
18. | 9 12 |
19. | 6 9 |
20. | 11 12 |
|---------------|
21. | 7 13 |
22. | 9 11 |
23. | 11 10 |
24. | 10 9 |
25. | 10 8 |
+---------------+
tabstat photo field, s(n mean sd sum min max)
stats | photo field
---------+--------------------
N | 25 25
mean | 10.6 11.56
sd | 3.068659 3.014963
sum | 265 289
min | 5 5
max | 17 18
------------------------------
regress field photo
Source | SS df MS Number of obs = 25
-------------+------------------------------ F( 1, 23) = 14.68
Model | 84.999823 1 84.999823 Prob > F = 0.0009
Residual | 133.160177 23 5.78957291 R-squared = 0.3896
-------------+------------------------------ Adj R-squared = 0.3631
Total | 218.16 24 9.09 Root MSE = 2.4062
------------------------------------------------------------------------------
field | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
photo | .6132743 .1600549 3.83 0.001 .2821755 .9443732
_cons | 5.059292 1.763512 2.87 0.009 1.41119 8.707394
------------------------------------------------------------------------------
Page 76, figure 3.5
graph twoway (scatter field photo) (lfit field photo), ylabel(6(2)18, nogrid angle(0)) /// ytitle(Field Count of Dead Trees) xtitle(Photo Count of Dead Trees) xlabel(6(2)18)
Page 79 in the middle
use http://www.ats.ucla.edu/stat/stata/examples/lohr/agsrs.dta, clear
gen west = 0
replace west = 1 if state =="AK" | state =="AZ" | state =="CA" | state =="CO" | state =="HI" | ///
state =="ID" | state =="MT" | state =="NV" | state =="NM" | state =="OR" | state =="UT" | ///
state =="WA" | state =="WY"
drop if west == 0
tabstat acres92, s(mean sd)
variable | mean sd
-------------+--------------------
acres92 | 598680.6 516157.7
----------------------------------
Page 83 at the bottom
NOTE: The three values that are predicted after the svyreg command are used for the table on the next page.
use http://www.ats.ucla.edu/stat/stata/examples/lohr/agsrs.dta, clear
gen wt = 1/acres87
svyset [pweight = wt]
svyreg acres92 acres87, nocons
predict yhat
predict sterror, stdp
predict residual, resid
Survey linear regression
pweight: wt Number of obs = 299
Strata: <one> Number of strata = 1
PSU: <observations> Number of PSUs = 299
Population size = .00759475
F( 1, 298) = 26564.60
Prob > F = 0.0000
R-squared = 0.9929
------------------------------------------------------------------------------
acres92 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
acres87 | .9865652 .006053 162.99 0.000 .9746531 .9984774
------------------------------------------------------------------------------
Page 84 at the top
NOTE: It is unclear what two line were added to the bottom of the SAS data file. Also, Appendix E is table of random numbers.
list wt acres92 yhat sterror residual in 1/10
+------------------------------------------------------+
| wt acres92 yhat sterror residual |
|------------------------------------------------------|
1. | 5.58e-06 175209 176902 1085.378 -1692.999 |
2. | 6.89e-06 138135 143154.6 878.3216 -5019.562 |
3. | .0000167 56102 59056.78 362.3416 -2954.782 |
4. | 4.53e-06 199117 217563.3 1334.855 -18446.29 |
5. | 9.47e-06 89228 104167.5 639.1172 -14939.48 |
|------------------------------------------------------|
6. | 8.30e-06 96194 118922.5 729.6466 -22728.55 |
7. | .0000151 57253 65414.21 401.3474 -8161.208 |
8. | 4.47e-06 210692 220590.1 1353.425 -9898.067 |
9. | .0000125 78498 79188.63 485.86 -690.6319 |
10. | 4.26e-06 219444 231453.1 1420.075 -12009.14 |
+------------------------------------------------------+
list wt acres92 yhat sterror residual in -4/l
+------------------------------------------------------+
| wt acres92 yhat sterror residual |
|------------------------------------------------------|
297. | 2.80e-06 343115 352944.7 2165.484 -9829.7 |
298. | .0000468 19956 21081.91 129.3476 -1125.913 |
299. | .0000636 15650 15504.86 95.12971 145.1407 |
300. | .000018 55827 54887.56 336.7614 939.443 |
+------------------------------------------------------+
Page 85, figure 3.6
NOTE: Although the title is shown on the graph below as it is in the text, the scale of the x-axis was not changed as it was in the text.
gen wtdresid = (acres92 - .986565*acres87)/sqrt(acres87)
graph scatter wtdresid acres87, ylabel(-200(100)200, angle(0) nogrid) ytitle(Weighted Residuals) ///
xtitle("Millions of Acres Devoted to Farms (1987)")
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