/* Lesson 11-1 */
/* File Name = les1101.sas 07/01/04 */
data gakusei;
infile 'all04a.prn'
firstobs=2;
input sex $ shintyou taijyuu kyoui
jitaku $ kodukai carryer $ tsuuwa;
if sex^='M' & sex^='F' then delete;
proc print data=gakusei(obs=10);
run;
proc reg data=gakusei; : 回帰分析
model taijyuu=shintyou; : 変量を指定
output out=outreg1 predicted=pred1 residual=resid1; : 結果項目の保存
run; :
:
proc print data=outreg1(obs=15); : 表示してみる
run; :
:
proc plot data=outreg1; : 散布図を描く
plot taijyuu*shintyou/vaxis=20 to 100 by 20; : 体重と身長(縦軸指定)
plot pred1*taijyuu; : 予測値と観測値
plot resid1*pred1 /vref=0; : 残差と予測値(残差解析)(水平軸指定)
plot resid1*shintyou/vref=0; : 残差と説明変数(残差解析)
plot resid1*taijyuu /vref=0; : 残差と目的変数(残差解析)
run; :
:
proc univariate data=outreg1 plot normal; : 残差を正規プロットして確かめる
var resid1; :
run; :
[補足] proc plot
の下に以下の行を追加した方がより正確ではある。
欠損値を含むデータを解析対象から除外する事を指示する命令文である。
「欠損値です」の表示が無くなるだけで、得られる図は同じ(欠損値は描画できないから)。
試しに追加する/しないの両方で実行してみよ。
where shintyou^=. and taijyuu^=.;
SAS システム 2
14:49 Wednesday, June 30, 2004
Model: MODEL1
Dependent Variable: TAIJYUU
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Prob>F
Model 1 10789.17582 10789.17582 252.411 0.0001
Error 251 10728.86228 42.74447
C Total 252 21518.03810
Root MSE 6.53793 R-square 0.5014
Dep Mean 58.72530 Adj R-sq 0.4994
C.V. 11.13307
SAS システム 3
14:49 Wednesday, June 30, 2004
Parameter Estimates
Parameter Standard T for H0:
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 -78.584367 8.65241903 -9.082 0.0001
SHINTYOU 1 0.814033 0.05123749 15.887 0.0001
SAS システム 4
14:49 Wednesday, June 30, 2004
S
H T K C
I A J O A T R
N I K I D R S P E
T J Y T U R U R S
O S Y Y O A K Y U E I
B E O U U K A E W D D
S X U U I U I R A 1 1
1 F 145.0 38.0 . J 10000 . 39.4504 -1.4504
2 F 148.0 42.0 . J 50000 . 41.8925 0.1075
3 F 148.0 43.0 80 J 50000 DoCoMo 4000 41.8925 1.1075
4 F 148.9 . . J 60000 . 42.6251 .
5 F 149.0 45.0 . G 60000 . 42.7065 2.2935
6 F 150.0 46.0 86 40000 . 43.5206 2.4794
7 F 151.0 50.0 . G 60000 J-PHONE . 44.3346 5.6654
8 F 151.7 41.5 80 J 35000 . 44.9044 -3.4044
9 F 152.0 35.0 77 J 60000 DoCoMo 2000 45.1486 -10.1486
10 F 152.0 43.0 . J 20000 au 3500 45.1486 -2.1486
11 F 153.0 41.0 . J 125000 No . 45.9627 -4.9627
12 F 153.0 42.0 . G 0 Vodafone 1000 45.9627 -3.9627
13 F 153.0 46.5 87 G 10000 . 45.9627 0.5373
14 F 153.0 50.0 . G 70000 DoCoMo 10000 45.9627 4.0373
15 F 153.0 55.0 78 J 30000 . 45.9627 9.0373
SAS システム 6
14:49 Wednesday, June 30, 2004
プロット : TAIJYUU*SHINTYOU. 凡例: A = 1 OBS, B = 2 OBS, ...
(NOTE: 40 オブザベーションが欠損値です．)
TAIJYUU |
100 + A
| A A
80 + A A A A B A A
| A B CBDDC DCEAD CCF B AA
60 + A AA D B CABBF HBOHKBIFFDC BADBB A
| AAA CABDA CCH F EBCGF DAAAB BA
40 + A B C BA BA
|
20 +
|
--+-----------+-----------+-----------+-----------+-----------+-
140 150 160 170 180 190
SHINTYOU
SAS システム 7
14:49 Wednesday, June 30, 2004
プロット : PRED1*TAIJYUU. 凡例: A = 1 OBS, B = 2 OBS, ...
(NOTE: 40 オブザベーションが欠損値です．)
80 +
|
PRED1 | A B A
| A ADAAFAB F B A A A
| ABBBBLFDDBGBA A BB
60 + BEBJHGGJBGBAADABA A
| AF EHCF BCAAD A
| BBDCCEAC AAA
| BAABBACA A A
| A CAAB B A
40 + A AA
---+------------+------------+------------+------------+--
20 40 60 80 100
TAIJYUU
SAS システム 8
14:49 Wednesday, June 30, 2004
プロット : RESID1*PRED1. 凡例: A = 1 OBS, B = 2 OBS, ...
(NOTE: 40 オブザベーションが欠損値です．)
|
R 50 +
e |
s | A A
i 25 + A
d | A B A A AAA A
u | A A A A BBAB BBBCDCDAA BA A A
a 0 +-------------A--BAA-BCBCDABBI-DEBCCGHBMHHHGHBEBBHA-AA------------
l | AA BAA C BA AGDDACCEBBCE BBACA
| A
-25 +
---+-----------+-----------+-----------+-----------+-----------+--
30 40 50 60 70 80
Predicted Value of TAIJYUU
SAS システム 9
14:49 Wednesday, June 30, 2004
プロット : RESID1*SHINTYOU. 凡例: A = 1 OBS, B = 2 OBS, ...
(NOTE: 40 オブザベーションが欠損値です．)
|
R 50 +
e |
s | A A
i 25 + A
d | A B A A A B A
u | A A A A B BAB B BBCDC CBA BA A A
a 0 +--------A---BAA-B-DBBDA-BBI-D-EBCCG-HBMFIAHGHBE-BBH-A--AA--------
l | A A BA AAB B A AFE DACCDABBCCB BABAB A
| A
-25 +
---+-----------+-----------+-----------+-----------+-----------+--
140 150 160 170 180 190
SHINTYOU
SAS システム 10
14:49 Wednesday, June 30, 2004
プロット : RESID1*TAIJYUU. 凡例: A = 1 OBS, B = 2 OBS, ...
(NOTE: 40 オブザベーションが欠損値です．)
|
R 50 +
e |
s | A A
i 25 + A
d | A BABB A
u | A AAABBAIBCCFAC A A
a 0 +--------------A-CBBDDDKIDMGISOFKCI-E---------------------
l | A CABCH CKDHCCFCAA
| A
-25 +
---+------------+------------+------------+------------+--
20 40 60 80 100
TAIJYUU
SAS システム 11
14:49 Wednesday, June 30, 2004
Univariate Procedure
Variable=RESID1 Residual
Moments
N 253 Sum Wgts 253
Mean 0 Sum 0
Std Dev 6.524941 Variance 42.57485
Skewness 1.414355 Kurtosis 4.06384
USS 10728.86 CSS 10728.86
CV . Std Mean 0.41022
T:Mean=0 0 Pr>|T| 1.0000
Num ^= 0 253 Num > 0 110
M(Sign) -16.5 Pr>=|M| 0.0440
Sgn Rank -1902.5 Pr>=|S| 0.1026
W:Normal 0.921391 Pr< W 0.0001
Missing Value .
Count 40
% Count/Nobs 13.65
SAS システム 15
14:49 Wednesday, June 30, 2004
Univariate Procedure
Variable=RESID1 Residual
Histogram # Boxplot
35+* 1 *
.* 3 0
.***** 13 0
.******************************* 93 +--+--+
.*********************************************** 139 *-----*
-15+** 4 |
----+----+----+----+----+----+----+----+----+--
* may represent up to 3 counts
SAS システム 16
14:49 Wednesday, June 30, 2004
Univariate Procedure
Variable=RESID1 Residual
Normal Probability Plot
35+ *
| ** *
| *******++++
| ++**************
| ************************
-15+*+**++++++
+----+----+----+----+----+----+----+----+----+----+
-2 -1 0 +1 +2
[注意] 誤差は「説明変量」の軸と垂直に取ることに注意せよ。 誤差は測定時に混入していると考えてモデルが構築されているから。
/* Lesson 11-2 */
/* File Name = les1102.sas 07/01/04 */
data gakusei;
infile 'all04a.prn'
firstobs=2;
input sex $ shintyou taijyuu kyoui
jitaku $ kodukai carryer $ tsuuwa;
if sex^='M' & sex^='F' then delete;
proc print data=gakusei(obs=10);
run;
proc reg data=gakusei; : 回帰分析
model taijyuu=shintyou kyoui; : 複数変量を指定
output out=outreg1 predicted=pred1 residual=resid1; : 結果項目の保存
run; :
proc print data=outreg1(obs=15);
run;
:
proc plot data=outreg1; : 散布図を描く
where shintyou^=. and taijyuu^=. and kyoui^=.; : 解析に使ったデータのみ
plot taijyuu*shintyou; :
plot taijyuu*kyoui; :
plot taijyuu*pred1; : 観測値と予測値
plot resid1*pred1 /vref=0; : 残差と予測値(残差解析)
plot resid1*shintyou/vref=0; : 残差と説明変量(残差解析)
plot resid1*kyoui /vref=0; : 残差と説明変量(残差解析)
plot resid1*taijyuu /vref=0; : 残差と目的変量(残差解析)
run; :
:
proc univariate data=outreg1 plot normal; : 残差を正規プロットして確かめる
var resid1; :
run; :
SAS システム 2
19:47 Wednesday, June 23, 2004
Model: MODEL1
Dependent Variable: TAIJYUU
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Prob>F
Model 2 7682.00845 3841.00423 102.149 0.0001
Error 90 3384.18983 37.60211
C Total 92 11066.19828
Root MSE 6.13206 R-square 0.6942
Dep Mean 59.19570 Adj R-sq 0.6874
C.V. 10.35896
SAS システム 3
19:47 Wednesday, June 23, 2004
Parameter Estimates
Parameter Standard T for H0:
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 -109.642478 12.60968451 -8.695 0.0001
SHINTYOU 1 0.672459 0.08035699 8.368 0.0001
KYOUI 1 0.646057 0.08814219 7.330 0.0001
SAS システム 4
19:47 Wednesday, June 23, 2004
S
H T K C
I A J O A T R
N I K I D R S P E
T J Y T U R U R S
O S Y Y O A K Y U E I
B E O U U K A E W D D
S X U U I U I R A 1 1
1 F 145.0 38.0 . J 10000 . . .
2 F 148.0 42.0 . J 50000 . . .
3 F 148.0 43.0 80 J 50000 DoCoMo 4000 41.5660 1.4340
4 F 148.9 . . J 60000 . . .
5 F 149.0 45.0 . G 60000 . . .
6 F 150.0 46.0 86 40000 . 46.7873 -0.7873
7 F 151.0 50.0 . G 60000 J-PHONE . . .
8 F 151.7 41.5 80 J 35000 . 44.0541 -2.5541
9 F 152.0 35.0 77 J 60000 DoCoMo 2000 42.3177 -7.3177
10 F 152.0 43.0 . J 20000 au 3500 . .
11 F 153.0 41.0 . J 125000 No . . .
12 F 153.0 42.0 . G 0 Vodafone 1000 . .
13 F 153.0 46.5 87 G 10000 . 49.4507 -2.9507
14 F 153.0 50.0 . G 70000 DoCoMo 10000 . .
15 F 153.0 55.0 78 J 30000 . 43.6362 11.3638
SAS システム 6
19:47 Wednesday, June 23, 2004
プロット : TAIJYUU*SHINTYOU. 凡例: A = 1 OBS, B = 2 OBS, ...
100 + A
| A A
TAIJYUU | A A A
| B BABAB AAAAA A B A AA
| A A A A B BA BAFBC ABA ABBA
50 + A A ACA ABD C BBACB A
| A B A A
|
|
|
0 +
--+-----------+-----------+-----------+-----------+-----------+-
140 150 160 170 180 190
SHINTYOU
SAS システム 7
19:47 Wednesday, June 23, 2004
プロット : TAIJYUU*KYOUI. 凡例: A = 1 OBS, B = 2 OBS, ...
100 + A
| A A
TAIJYUU | AA A
| A C BBF BAAA A A
| A A B C AAC FBI AAA A
50 + A A AA B HCFBBA
| A A B A
|
|
|
0 +
---+-------+-------+-------+-------+-------+-------+-------+--
50 60 70 80 90 100 110 120
KYOUI
SAS システム 8
19:47 Wednesday, June 23, 2004
プロット : TAIJYUU*PRED1. 凡例: A = 1 OBS, B = 2 OBS, ...
100 + A
| A A
TAIJYUU | A A A
| A A CBBBB AA CB A
| A B AA B AABB BFDABB AB
50 + B BBBC DBCDB B
| BAA A
|
|
|
0 +
--+---------+---------+---------+---------+---------+---------+-
30 40 50 60 70 80 90
Predicted Value of TAIJYUU
SAS システム 9
19:47 Wednesday, June 23, 2004
プロット : RESID1*PRED1. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s | A
i 20 + A
d | A A A A
u | A B AA B A A BABA A A
a 0 +--------------AAA--BBBB-CAAAAAABB-BDCAB-AA-BB--------A-----------
l | A B AABCA B ABAABB ABA A
|
-20 +
---+---------+---------+---------+---------+---------+---------+--
30 40 50 60 70 80 90
Predicted Value of TAIJYUU
SAS システム 10
19:47 Wednesday, June 23, 2004
プロット : RESID1*SHINTYOU. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s | A
i 20 + A
d | A A A A
u | B A A BBBAB AAAA
a 0 +------------A-A-A-AAABA-AAC-B-BAABB-A-CAB-BAB-A-B-B-A--A---------
l | A A AA B AA CC A BAA A ABBA A
|
-20 +
---+-----------+-----------+-----------+-----------+-----------+--
140 150 160 170 180 190
SHINTYOU
SAS システム 11
19:47 Wednesday, June 23, 2004
プロット : RESID1*KYOUI. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s | A
i 20 + A
d | A A A A
u | B A A A A B ABD B
a 0 +-----------------------B---D-BCDGCCAG-A-AB---B--------A----------
l | AA B CA FABBC AAB A
|
-20 +
-+--------+--------+--------+--------+--------+--------+--------+-
50 60 70 80 90 100 110 120
KYOUI
SAS システム 12
19:47 Wednesday, June 23, 2004
プロット : RESID1*TAIJYUU. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s | A
i 20 + A
d | AA A A
u | A B AAB B ABBA AA
a 0 +----------------AAABCBDCAB-AEDBDAA-D----A----------------
l | A A BDABB B AEAAC A
|
-20 +
---+------------+------------+------------+------------+--
20 40 60 80 100
TAIJYUU
SAS システム 17
19:47 Wednesday, June 23, 2004
Univariate Procedure
Variable=RESID1 Residual
Stem Leaf # Boxplot
2 4 1 *
1 8 1 0
1 01134 5 0
0 5556777778888 13 |
0 0000111111233444 16 +--+--+
-0 44433333333333332222222221111111000 35 *-----*
-0 998777776666666555555 21 |
-1 0 1 |
----+----+----+----+----+----+----+
Multiply Stem.Leaf by 10**+1
SAS システム 18
19:47 Wednesday, June 23, 2004
Univariate Procedure
Variable=RESID1 Residual
Normal Probability Plot
22.5+ *
| * +
| *+**+++++
| ********+
| +++******
| ************
|* * ** *+*******
-12.5+ ++++++++
+----+----+----+----+----+----+----+----+----+----+
-2 -1 0 +1 +2
/* Lesson 11-3 */
/* File Name = les1103.sas 07/01/04 */
data gakusei;
infile 'all04a.prn'
firstobs=2;
input sex $ shintyou taijyuu kyoui
jitaku $ kodukai carryer $ tsuuwa;
if sex^='M' & sex^='F' then delete; : 性別不明は除外
if shintyou=. | taijyuu=. | kyoui=. then delete; : 欠損のあるデータは除外
proc print data=gakusei(obs=10);
run;
proc corr data=gakusei; : 相関係数
where sex='M'; : 男性について
run; :
:
proc reg data=gakusei; : 回帰分析
model taijyuu=shintyou kyoui; :
where sex='M'; : 男性について
output out=outreg1 predicted=pred1 residual=resid1; :
run; :
proc print data=outreg1(obs=15);
run;
proc plot data=outreg1;
where sex='M'; : 対象データについて
plot taijyuu*shintyou;
plot taijyuu*kyoui;
plot taijyuu*pred1;
plot resid1*(pred1 shintyou kyoui taijyuu)/vref=0; : まとめて記述
/*
plot resid1*pred1 /vref=0;
plot resid1*shintyou/vref=0;
plot resid1*kyoui /vref=0;
plot resid1*taijyuu /vref=0;
*/
run;
proc univariate data=outreg1 plot normal;
var resid1;
run;
SAS システム 2
14:53 Tuesday, June 29, 2004
Correlation Analysis
5 'VAR' Variables: SHINTYOU TAIJYUU KYOUI KODUKAI TSUUWA
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum
SHINTYOU 61 172.3 6.2101 10513.1 156.0 185.0
TAIJYUU 61 64.6344 9.2524 3942.7 46.0000 100.0
KYOUI 61 88.7049 8.6146 5411.0 56.0000 112.0
KODUKAI 57 54491.2 57395.6 3106000 0 300000
TSUUWA 5 8200.0 3271.1 41000.0 5000.0 13000.0
SAS システム 3
14:53 Tuesday, June 29, 2004
Correlation Analysis
Pearson Correlation Coefficients / Prob > |R| under Ho: Rho=0
/ Number of Observations
SHINTYOU TAIJYUU KYOUI KODUKAI TSUUWA
SHINTYOU 1.00000 0.42019 0.22042 0.11293 -0.19869
0.0 0.0007 0.0878 0.4029 0.7487
61 61 61 57 5
TAIJYUU 0.42019 1.00000 0.66894 -0.08201 0.17683
0.0007 0.0 0.0001 0.5442 0.7760
61 61 61 57 5
KYOUI 0.22042 0.66894 1.00000 -0.11888 0.14486
0.0878 0.0001 0.0 0.3785 0.8162
61 61 61 57 5
KODUKAI 0.11293 -0.08201 -0.11888 1.00000 -0.58004
0.4029 0.5442 0.3785 0.0 0.3053
57 57 57 57 5
TSUUWA -0.19869 0.17683 0.14486 -0.58004 1.00000
0.7487 0.7760 0.8162 0.3053 0.0
5 5 5 5 5
SAS システム 6
14:53 Tuesday, June 29, 2004
Model: MODEL1
Dependent Variable: TAIJYUU
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Prob>F
Model 2 2700.06291 1350.03146 32.138 0.0001
Error 58 2436.39479 42.00681
C Total 60 5136.45770
Root MSE 6.48127 R-square 0.5257
Dep Mean 64.63443 Adj R-sq 0.5093
C.V. 10.02758
SAS システム 7
14:53 Tuesday, June 29, 2004
Parameter Estimates
Parameter Standard T for H0:
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 -66.687175 23.51117249 -2.836 0.0063
SHINTYOU 1 0.427106 0.13813439 3.092 0.0031
KYOUI 1 0.650603 0.09957815 6.534 0.0001
SAS システム 10
14:53 Tuesday, June 29, 2004
プロット : TAIJYUU*SHINTYOU. 凡例: A = 1 OBS, B = 2 OBS, ...
TAIJYUU |
100 + A
| A A
|
75 + A A A A A AA
| B B A C A A A A A A D A A A
| A A A B A B A D B C A AAA A AA A
50 + A B A
|
|
25 +
--+---------+---------+---------+---------+---------+---------+-
155 160 165 170 175 180 185
SHINTYOU
SAS システム 11
14:53 Tuesday, June 29, 2004
プロット : TAIJYUU*KYOUI. 凡例: A = 1 OBS, B = 2 OBS, ...
TAIJYUU |
100 + A
| A A
|
75 + AA BA A A
| A C BAH BA B A
| A A A C AAC EBE AA A
50 + A A A A
|
|
25 +
---+-------+-------+-------+-------+-------+-------+-------+--
50 60 70 80 90 100 110 120
KYOUI
SAS システム 12
14:53 Tuesday, June 29, 2004
プロット : TAIJYUU*PRED1. 凡例: A = 1 OBS, B = 2 OBS, ...
TAIJYUU |
100 + A
| A A
|
75 + AA AAAA A
| AA A DABACC A AB
| A AA AAAAAABBDDB BA
50 + A A A A
|
|
25 +
--+-----------+-----------+-----------+-----------+-----------+-
40 50 60 70 80 90
Predicted Value of TAIJYUU
SAS システム 13
14:53 Tuesday, June 29, 2004
プロット : RESID1*PRED1. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s |
i 20 + A A
d | A A
u | A A A AA A CBA AA
a 0 +---------------A---A----A-AAAAABBAAC--A-BA---------A-------------
l | A A A AADBB BD B
|
-20 +
---+-----------+-----------+-----------+-----------+-----------+--
40 50 60 70 80 90
Predicted Value of TAIJYUU
SAS システム 14
14:53 Tuesday, June 29, 2004
プロット : RESID1*SHINTYOU. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s |
i 20 + A A
d | A A
u | A B A C A B A A AA
a 0 +----A-------A-----------A-B---A-B-A-A-A--AB---A-AA--B-A-A---A----
l | A B A A B C A B A A A BA A A
|
-20 +
---+---------+---------+---------+---------+---------+---------+--
155 160 165 170 175 180 185
SHINTYOU
SAS システム 15
14:53 Tuesday, June 29, 2004
プロット : RESID1*KYOUI. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s |
i 20 + A A
d | A A
u | A A A A B BD B
a 0 +-----------------------B---B---ABABAE-A-AB---A--------A----------
l | A B BBBAE AAB A A
|
-20 +
-+--------+--------+--------+--------+--------+--------+--------+-
50 60 70 80 90 100 110 120
KYOUI
SAS システム 16
14:53 Tuesday, June 29, 2004
プロット : RESID1*TAIJYUU. 凡例: A = 1 OBS, B = 2 OBS, ...
|
R 40 +
e |
s |
i 20 + A A
d | A A
u | A A A B A AC A A AA
a 0 +----------A-------A---DACA-DA-A-BB------A------------------------
l | A A BA B FABABA A
|
-20 +
---+---------+---------+---------+---------+---------+---------+--
40 50 60 70 80 90 100
TAIJYUU
SAS システム 17
14:53 Tuesday, June 29, 2004
Univariate Procedure
Variable=RESID1 Residual
Moments
N 61 Sum Wgts 61
Mean 0 Sum 0
Std Dev 6.372329 Variance 40.60658
Skewness 1.224565 Kurtosis 1.785444
USS 2436.395 CSS 2436.395
CV . Std Mean 0.815893
T:Mean=0 0 Pr>|T| 1.0000
Num ^= 0 61 Num > 0 24
M(Sign) -6.5 Pr>=|M| 0.1237
Sgn Rank -115.5 Pr>=|S| 0.4113
W:Normal 0.909005 Pr< W 0.0001
SAS システム 20
14:53 Tuesday, June 29, 2004
Univariate Procedure
Variable=RESID1 Residual
Stem Leaf # Boxplot
2 2 1 0
1 8 1 0
1 024 3 |
0 5555566777 10 |
0 001123444 9 +--+--+
-0 44443333322211111100 20 *-----*
-0 99888766655555555 17 +-----+
----+----+----+----+
Multiply Stem.Leaf by 10**+1
SAS システム 21
14:53 Tuesday, June 29, 2004
Univariate Procedure
Variable=RESID1 Residual
Normal Probability Plot
22.5+ *
| * ++
| **++++++
7.5+ ++*****+
| +++*******
| ***********
-7.5+ * * **+*******
+----+----+----+----+----+----+----+----+----+----+
-2 -1 0 +1 +2
where sex='M' and taijyuu<85;