Figure 4.1, page 91.
Duplicates figure 2.3 from Chapter 2.
use https://stats.idre.ucla.edu/stat/stata/examples/chp/p095, clear
Table 4.1, page 95.
list
y x1 x2
1. 12.37 2.23 9.66
2. 12.66 2.57 8.94
3. 12 3.87 4.4
4. 11.93 3.1 6.64
..
[remainder of output omitted]
Figure 4.2, page 94. Provide the graph command but we are not displaying the output.
corr y x1 x2
(obs=15)
| y x1 x2
---------+---------------------------
y | 1.0000
x1 | 0.0025 1.0000
x2 | 0.4341 -0.8998 1.0000
graph matrix y x1 x2, half
Coefficients for page 95.
regress y x1
Source | SS df MS Number of obs = 15
---------+------------------------------ F( 1, 13) = 0.00
Model | .000056215 1 .000056215 Prob > F = 0.9930
Residual | 9.0085422 13 .692964784 R-squared = 0.0000
---------+------------------------------ Adj R-squared = -0.0769
Total | 9.00859841 14 .643471315 Root MSE = .83245
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
x1 | .0037476 .4160825 0.009 0.993 -.8951439 .9026391
_cons | 11.98875 1.266891 9.463 0.000 9.251804 14.72571
------------------------------------------------------------------------------
regress y x2
Source | SS df MS Number of obs = 15
---------+------------------------------ F( 1, 13) = 3.02
Model | 1.6973605 1 1.6973605 Prob > F = 0.1060
Residual | 7.31123791 13 .562402916 R-squared = 0.1884
---------+------------------------------ Adj R-squared = 0.1260
Total | 9.00859841 14 .643471315 Root MSE = .74994
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
x2 | .1954562 .1125087 1.737 0.106 -.0476041 .4385165
_cons | 10.63194 .8109425 13.111 0.000 8.880002 12.38387
------------------------------------------------------------------------------
regress y x1 x2
Source | SS df MS Number of obs = 15
---------+------------------------------ F( 2, 12) =39222.21
Model | 9.00722053 2 4.50361027 Prob > F = 0.0000
Residual | .001377876 12 .000114823 R-squared = 0.9998
---------+------------------------------ Adj R-squared = 0.9998
Total | 9.00859841 14 .643471315 Root MSE = .01072
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
x1 | 3.097008 .0122745 252.313 0.000 3.070264 3.123752
x2 | 1.031859 .0036842 280.078 0.000 1.023832 1.039886
_cons | -4.515414 .0611419 -73.851 0.000 -4.648631 -4.382198
------------------------------------------------------------------------------
t-test for table 4.2, page 99.
use https://stats.idre.ucla.edu/stat/stata/examples/chp/p010
regress nitrogen agr forest rsdntial comindl
Source | SS df MS Number of obs = 20
---------+------------------------------ F( 4, 15) = 9.15
Model | 2.56984613 4 .642461533 Prob > F = 0.0006
Residual | 1.0527287 15 .070181913 R-squared = 0.7094
---------+------------------------------ Adj R-squared = 0.6319
Total | 3.62257483 19 .190661833 Root MSE = .26492
------------------------------------------------------------------------------
nitrogen | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
agr | .0058091 .015034 0.386 0.705 -.026235 .0378533
forest | -.0129679 .0139315 -0.931 0.367 -.0426621 .0167264
rsdntial | -.0072268 .03383 -0.214 0.834 -.0793338 .0648803
comindl | .3050278 .1638167 1.862 0.082 -.0441392 .6541947
_cons | 1.722214 1.234082 1.396 0.183 -.908169 4.352596
------------------------------------------------------------------------------
regress nitrogen agr forest rsdntial comindl if river ~= "Neversink"
Source | SS df MS Number of obs = 19
---------+------------------------------ F( 4, 14) = 20.76
Model | 3.07765167 4 .769412918 Prob > F = 0.0000
Residual | .518811319 14 .037057951 R-squared = 0.8557
---------+------------------------------ Adj R-squared = 0.8145
Total | 3.59646299 18 .199803499 Root MSE = .1925
------------------------------------------------------------------------------
nitrogen | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
agr | .0101367 .0109838 0.923 0.372 -.0134213 .0336947
forest | -.0075892 .0102221 -0.742 0.470 -.0295134 .0143349
rsdntial | -.1237929 .039337 -3.147 0.007 -.2081624 -.0394234
comindl | 1.528956 .3437191 4.448 0.001 .7917521 2.26616
_cons | 1.099471 .9116357 1.206 0.248 -.8557928 3.054735
------------------------------------------------------------------------------
regress nitrogen agr forest rsdntial comindl if river ~= "Hackensack"
Source | SS df MS Number of obs = 19
---------+------------------------------ F( 4, 14) = 22.24
Model | 2.49968384 4 .624920959 Prob > F = 0.0000
Residual | .393358087 14 .028097006 R-squared = 0.8640
---------+------------------------------ Adj R-squared = 0.8252
Total | 2.89304192 18 .160724551 Root MSE = .16762
------------------------------------------------------------------------------
nitrogen | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
agr | .0023522 .0095391 0.247 0.809 -.0181072 .0228117
forest | -.0127603 .0088149 -1.448 0.170 -.0316665 .0061458
rsdntial | .181161 .04439 4.081 0.001 .0859538 .2763682
comindl | .0756176 .1139572 0.664 0.518 -.1687963 .3200315
_cons | 1.626014 .7810911 2.082 0.056 -.0492596 3.301288
------------------------------------------------------------------------------
Figure 4.5, page 102.
gen n = _n. graph twoway (scatter nitrogen comindl, mlabel(n)) (lfit nitrogen comindl)
Table 4.3, page 103.
predict r, rstandard
list r p
r phat
1. .032278 1.088083
2. -.0450231 1.026516
3. 1.952922 1.177355
4. -1.847232 1.608324
5. .155291 1.956178
6. .6723057 1.171199
7. 1.923264 1.340508
8. 1.565621 1.072691
9. -.0951495 1.044986
10. .3808243 1.069613
11. .7492378 1.054221
12. -.8103347 1.048065
13. -.8324621 1.035751
14. -.8293861 1.106553
15. -.9376069 1.106553
16. -.475896 1.044986
17. -.7232284 1.066535
18. -.5004942 1.054221
19. -1.031034 1.03883
20. .5747275 1.03883
Figure 4.6(a), page 103.
Note: The yline option draws a horizontal line at zero.
graph twoway (scatter r n), yline(0) xlabel(1(2)19)
Figure 4.6(b), page 103.
graph twoway (scatter hat n), xlabel(1(2)19)
Table 4.4, page 106.
Note 1: The hinflu6 command, which generates the Hadi influence measure, is an updated version of a command published in Stata Technical Bulletin 6. The hinflu can be downloaded from UCLA ATS from within Stata (see How can I use the search command to search for programs and get additional help? for more information about using search).
Note 2: The sort command was used to return the data back to their original order.
predict c, cooks
predict dfits, dfits
hinflu6 h
sort n
list c dfits h
c dfits h
1. .0000301 .0075454 .0579712
2. .0000724 -.011698 .0716953
3. .1011772 .4924355 .5835671
4. .5622548 -1.144754 .7717384
5. .0245928 .2156743 2.042283
6. .0119404 .1521018 .1042828
7. .1665338 .629226 .5965196
8. .074084 .4024879 .3729268
9. .0003009 -.0238455 .0674733
10. .0044255 .0917998 .0772676
11. .0180378 .1875317 .1285208
12. .0215743 -.2056559 .1412572
13. .0238624 -.216514 .1487441
14. .0190677 -.1935145 .1347416
15. .0243684 -.2199832 .1578567
16. .0075266 -.119992 .0919314
17. .0161191 -.1770831 .1213872
18. .008049 -.1241701 .0924663
19. .0361671 -.2694501 .1930732
20. .0112381 .147052 .1053881
Figure 4.7(a), page 107.
graph twoway scatter c n, xlabel(4(4)20) ylabel(.1(.1).5)
Figure 4.7(b), page 107.
graph twoway scatter dfits n, xlabel(4(4)20) ylabel(-1.2(.4).4)
Figure 4.7(c), page 107.
graph twoway scatter h n, xlabel(4(4)20) ylabel(0(.5)2)
Figure 4.8, page 108.
Note: The hadiplot command can be from within Stata as shown below. You can download this program from within Stata by typing search hadiplot (see How can I use the search command to search for programs and get additional help? for more information about using search).
hadiplot
Equation 4.25, page 111.
use https://stats.idre.ucla.edu/stat/stata/examples/chp/p112, clear
regress time distance climb
Source | SS df MS Number of obs = 35
---------+------------------------------ F( 2, 32) = 181.66
Model | 281686567 2 140843283 Prob > F = 0.0000
Residual | 24810081.9 32 775315.059 R-squared = 0.9191
---------+------------------------------ Adj R-squared = 0.9140
Total | 306496649 34 9014607.31 Root MSE = 880.52
------------------------------------------------------------------------------
time | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
distance | 373.0727 36.06841 10.343 0.000 299.6037 446.5416
climb | .662888 .1230519 5.387 0.000 .4122395 .9135365
_cons | -539.4829 258.1607 -2.090 0.045 -1065.339 -13.62671
------------------------------------------------------------------------------
Figure 4.11, page 114.
avplots
Figure 4.12(a), page 114.
Note: The graph in the book is incorrect, see errata .
cprplot distance
Figure 4.12(b), page 114.
Note: The graph in the book is incorrect, see errata .
cprplot climb
Figure 4.13, page 114.
hadiplot
