RANCANGAN PERCOBAAN DENGAN SAS
Oleh Kismiantini, M.Si.
JURUSAN PENDIDIKAN MATEMATIKA FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNIVERSITAS NEGERI YOGYAKARTA 2010
0
SAS (Statistical Analysis System) Berikut ini adalah window dari SAS.
Klik tombol ini untuk running program
Gambar 1. SAS window 1. Editor : digunakan untuk memasukkan data dan menganalisis data dengan perintah tertentu. Untuk memudahkan memasukkan data, ketiklah data pada Microsoft Excell lalu copy dan paste di Editor SAS. 2. Log : menunjukkan bahwa program dapat berjalan dengan sukses atau gagal 3. Output : hasil output yang telah di run
1
Gambar 2. Output SAS
Gambar 3. Log SAS
Bila ingin menghapus hasil output atau hasil log, pilih Edit lalu tekan Clear All.
2
RANCANGAN ACAK LENGKAP DENGAN SAS PROGRAM data parasetamol; input waktu kadar; cards; 7 40 6 40 9 40 4 40 7 40 9 50 7 50 8 50 6 50 9 50 5 60 4 60 8 60 6 60 3 60 3 75 5 75 2 75 3 75 7 75 2 90 3 90 4 90 1 90 4 90 ; proc glm data=parasetamol; class kadar; model waktu=kadar; means kadar/duncan; run;
Dapat diganti dengan mengetik t untuk BNT dan tukey untuk BNJ
LOG NOTE: Copyright (c) 2002-2003 by SAS Institute Inc., Cary, NC, USA. NOTE: SAS (r) 9.1 (TS1M3) Licensed to ACADEMIC OF INDONESIA, Site 0045663001. NOTE: This session is executing on the XP_PRO platform. NOTE: SAS initialization used: real time 28.09 seconds cpu time 1.01 seconds 1 2 3
data parasetamol; input waktu kadar; cards;
NOTE: The data set WORK.PARASETAMOL has 25 observations and 2 variables. NOTE: DATA statement used (Total process time): real time 4.78 seconds cpu time 0.04 seconds
3
29 30 31 32 33 34
; proc glm data=parasetamol; class kadar; model waktu=kadar; means kadar/duncan; run;
OUTPUT The GLM Procedure Class Level Information Class kadar
Levels 5
Values 40 50 60 75 90
Number of Observations Read Number of Observations Used
25 25
The GLM Procedure Dependent Variable: waktu Source Model Error Corrected Total
Sum of Squares 79.4400000 57.6000000 137.0400000
DF 4 20 24 R-Square 0.579685
Coeff Var 32.14122
Source kadar Source kadar
Mean Square 19.8600000 2.8800000
Root MSE 1.697056
F Value 6.90
Pr > F 0.0012
waktu Mean 5.280000
DF 4 DF
Type I SS 79.44000000 Type III SS
Mean Square 19.86000000 Mean Square
F Value 6.90 F Value
Pr > F 0.0012 Pr > F
4
79.44000000
19.86000000
6.90
0.0012
The GLM Procedure Duncan's Multiple Range Test for waktu NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha Error Degrees of Freedom Error Mean Square Number of Means Critical Range
2 2.239
0.05 20 2.88
3 2.350
4 2.421
5 2.470
Means with the same letter are not significantly different. Duncan Grouping
Mean
N
kadar
A A A
7.800
5
50
6.600
5
40
C C C
5.200
5
60
4.000
5
75
2.800
5
90
B B B D D D
4
RANCANGAN ACAK KELOMPOK LENGKAP DENGAN SAS PROGRAM data rak; input bobotbadan kelompok perlakuan$; cards; 8 1 A 7 2 A 9 3 A 6 4 A 1 1 B 0 2 B 3 3 B 2 4 B 6 1 C 5 2 C 7 3 C 5 4 C 5 1 D 6 2 D 9 3 D 8 4 D ; proc glm data=rak; class kelompok perlakuan; model bobotbadan=kelompok perlakuan; means perlakuan/t; run;
LOG NOTE: PROCEDURE GLM used (Total process time): real time 20.92 seconds cpu time 1.04 seconds 60 61 62
data rak; input bobotbadan kelompok perlakuan$; cards;
NOTE: The data set WORK.RAK has 16 observations and 3 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 79 80 81 82 83 84
; proc glm data=rak; class kelompok perlakuan; model bobotbadan=kelompok perlakuan; means perlakuan/t; run;
NOTE: Means from the MEANS statement are not adjusted for other terms in the model. means, use the LSMEANS statement.
For adjusted
OUTPUT The GLM Procedure Class Level Information Class kelompok perlakuan
Levels 4 4
Number of Observations Read Number of Observations Used
Values 1 2 3 4 A B C D 16 16
5
The GLM Procedure Dependent Variable: bobotbadan Source
DF
Sum of Squares
Model Error Corrected Total
6 9 15
103.3750000 8.5625000 111.9375000
Mean Square
F Value
Pr > F
17.2291667 0.9513889
18.11
0.0001
R-Square
Coeff Var
Root MSE
bobotbadan Mean
0.923506
17.93824
0.975392
5.437500
Source kelompok perlakuan Source kelompok perlakuan
DF
Type I SS
Mean Square
F Value
Pr > F
3 3
14.18750000 89.18750000
4.72916667 29.72916667
4.97 31.25
0.0265 <.0001
DF
Type III SS
Mean Square
F Value
Pr > F
3 3
14.18750000 89.18750000
4.72916667 29.72916667
4.97 31.25
0.0265 <.0001
The GLM Procedure t Tests (LSD) for bobotbadan NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 9 Error Mean Square 0.951389 Critical Value of t 2.26216 Least Significant Difference 1.5602 Means with the same letter are not significantly different. t Grouping
Mean
N
perlakuan
A A A
7.5000
4
A
7.0000
4
D
5.7500
4
C
1.5000
4
B
B B B
C
6
RANCANGAN BUJUR SANGKAR LATIN DENGAN SAS PROGRAM data rbsl1; input nilai matakuliah$ perlakuan$ waktu$; datalines; 84 Aljabar A W1 91 Aljabar B W2 59 Aljabar C W3 75 Aljabar D W4 79 Geometri B W1 82 Geometri C W2 70 Geometri D W3 91 Geometri A W4 63 Statistika C W1 80 Statistika D W2 77 Statistika A W3 75 Statistika B W4 97 Kalkulus D W1 93 Kalkulus A W2 80 Kalkulus B W3 68 Kalkulus C W4 ; proc anova; class matakuliah perlakuan waktu; model nilai=waktu matakuliah perlakuan; means perlakuan/tukey; run;
LOG 119 120 121
data rbsl1; input nilai matakuliah$ perlakuan$ datalines;
waktu$;
NOTE: The data set WORK.RBSL1 has 16 observations and 4 variables. NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 138 139 140 141 142 143
; proc anova; class matakuliah perlakuan waktu; model nilai=waktu matakuliah perlakuan; means perlakuan/tukey; run;
OUTPUT The ANOVA Procedure Class Level Information Class matakuliah perlakuan waktu
Levels 4 4 4
Values Aljabar Geometri Kalkulus Statisti A B C D W1 W2 W3 W4
Number of Observations Read Number of Observations Used
16 16
7
The ANOVA Procedure Dependent Variable: nilai Source
DF
Sum of Squares
Model Error Corrected Total
9 6 15
1450.500000 287.500000 1738.000000
Mean Square
F Value
Pr > F
161.166667 47.916667
3.36
0.0768
R-Square
Coeff Var
Root MSE
nilai Mean
0.834580
8.762261
6.922187
79.00000
Source waktu matakuliah perlakuan
DF
Anova SS
Mean Square
F Value
Pr > F
3 3 3
474.5000000 252.5000000 723.5000000
158.1666667 84.1666667 241.1666667
3.30 1.76 5.03
0.0994 0.2550 0.0446
The ANOVA Procedure Tukey's Studentized Range (HSD) Test for nilai NOTE: This test controls the Type I experimentwise error rate, but it generally has a higher Type II error rate than REGWQ. Alpha 0.05 Error Degrees of Freedom 6 Error Mean Square 47.91667 Critical Value of Studentized Range 4.89559 Minimum Significant Difference 16.944 Means with the same letter are not significantly different. Tukey Grouping
Mean
N
perlakuan
A A A A A
86.250
4
A
81.250
4
B
80.500
4
D
68.000
4
C
B B B B B
8
FAKTORIAL RAL DENGAN SAS PROGRAM data fakral; input respons jenis_pupuk varietas_padi; cards; 64 1 1 66 1 1 70 1 1 72 1 2 81 1 2 64 1 2 74 1 3 51 1 3 65 1 3 65 2 1 63 2 1 58 2 1 57 2 2 43 2 2 52 2 2 47 2 3 58 2 3 67 2 3 59 3 1 68 3 1 65 3 1 66 3 2 71 3 2 59 3 2 58 3 3 39 3 3 42 3 3 58 4 1 41 4 1 46 4 1 57 4 2 61 4 2 53 4 2 53 4 3 59 4 3 38 4 3 ; proc glm data=fakral; class jenis_pupuk varietas_padi; model respons=jenis_pupuk varietas_padi jenis_pupuk*varietas_padi; test h=jenis_pupuk e=jenis_pupuk*varietas_padi; run;
LOG NOTE: PROCEDURE ANOVA used (Total process time): real time 8:21.67 cpu time 3.00 seconds
144 145 146
data fakral; input respons jenis_pupuk varietas_padi; cards;
NOTE: SAS went to a new line when INPUT statement reached past the end of a line. NOTE: The data set WORK.FAKRAL has 36 observations and 3 variables.
9
NOTE: DATA statement used (Total process time): real time 0.04 seconds cpu time 0.00 seconds 184 185 186 187 188 189
; proc glm data=fakral; class jenis_pupuk varietas_padi; model respons=jenis_pupuk varietas_padi jenis_pupuk*varietas_padi; test h=jenis_pupuk e=jenis_pupuk*varietas_padi; run;
OUTPUT The GLM Procedure Class Level Information Class
Levels
jenis_pupuk varietas_padi
Values
4 3
1 2 3 4 1 2 3
Number of Observations Read Number of Observations Used
36 36
The GLM Procedure Dependent Variable: respons Source
DF
Sum of Squares
Model Error Corrected Total
11 24 35
2277.222222 1501.333333 3778.555556
R-Square 0.602670 Source jenis_pupuk varietas_padi jenis_pup*varietas_p
Source jenis_pupuk varietas_padi jenis_pup*varietas_p
Coeff Var 13.49438
Mean Square
F Value
Pr > F
207.020202 62.555556
3.31
0.0069
Root MSE 7.909207
respons Mean 58.61111
DF
Type I SS
Mean Square
F Value
Pr > F
3 2 6
1156.555556 349.388889 771.277778
385.518519 174.694444 128.546296
6.16 2.79 2.05
0.0029 0.0812 0.0971
DF
Type III SS
Mean Square
F Value
Pr > F
3 2 6
1156.555556 349.388889 771.277778
385.518519 174.694444 128.546296
6.16 2.79 2.05
0.0029 0.0812 0.0971
Tests of Hypotheses Using the Type III MS for jenis_pup*varietas_p as an Error Term Source jenis_pupuk
DF
Type III SS
Mean Square
F Value
Pr > F
3
1156.555556
385.518519
3.00
0.1170
10
PROGRAM data fakral; input respons lama dosis; cards; 96 2 0 98 2 0 94 2 0 90 4 0 94 4 0 92 4 0 92 2 16 88 2 16 90 2 16 88 4 16 92 4 16 94 4 16 92 2 32 94 2 32 84 2 32 78 4 32 82 4 32 74 4 32 74 2 48 74 2 48 68 2 48 0 4 48 0 4 48 0 4 48 50 2 64 50 2 64 54 2 64 0 4 64 0 4 64 0 4 64 ; proc glm data=fakral; class lama dosis; model respons=lama dosis lama*dosis; lsmeans lama*dosis / pdiff=all adjust=tukey; run;
Dapat diganti dengan bon, dunnet, scheffe, sidak
LOG NOTE: PROCEDURE GLM used (Total process time): real time 23.17 seconds cpu time 1.32 seconds 1124 1125 1126
data fakral; input respons lama dosis; cards;
NOTE: The data set WORK.FAKRAL has 30 observations and 3 variables. NOTE: DATA statement used (Total process time): real time 0.04 seconds cpu time 0.01 seconds 1157 1158 1159 1160 1161 1162
; proc glm data=fakral; class lama dosis; model respons=lama dosis lama*dosis; lsmeans lama*dosis / pdiff=all adjust=tukey; run;
11
OUTPUT The GLM Procedure Class Level Information Class
Levels
lama dosis
2 5
Values 2 4 0 16 32 48 64
Number of Observations Read Number of Observations Used
30 30
The GLM Procedure Dependent Variable: respons Source
DF
Sum of Squares
Model Error Corrected Total
9 20 29
37430.53333 165.33333 37595.86667
Source lama dosis lama*dosis Source lama dosis lama*dosis
Mean Square
F Value
Pr > F
4158.94815 8.26667
503.10
<.0001
R-Square
Coeff Var
Root MSE
respons Mean
0.995602
4.351939
2.875181
66.06667
DF
Type I SS
Mean Square
F Value
Pr > F
1 4 4
5713.20000 25459.20000 6258.13333
5713.20000 6364.80000 1564.53333
691.11 769.94 189.26
<.0001 <.0001 <.0001
DF
Type III SS
Mean Square
F Value
Pr > F
1 4 4
5713.20000 25459.20000 6258.13333
5713.20000 6364.80000 1564.53333
691.11 769.94 189.26
<.0001 <.0001 <.0001
12
The GLM Procedure Least Squares Means Adjustment for Multiple Comparisons: Tukey lama
dosis
2 2 2 2 2 4 4 4 4 4
0 16 32 48 64 0 16 32 48 64
respons LSMEAN
LSMEAN Number
96.0000000 90.0000000 90.0000000 72.0000000 51.3333333 92.0000000 91.3333333 78.0000000 -0.0000000 0.0000000
1 2 3 4 5 6 7 8 9 10
Least Squares Means for effect lama*dosis Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: respons i/j 1 2 3 4 5 6 7 8 9 10
1 0.2987 0.2987 <.0001 <.0001 0.7813 0.6158 <.0001 <.0001 <.0001
2
3
4
5
6
7
8
9
10
0.2987
0.2987 1.0000
<.0001 <.0001 <.0001
<.0001 <.0001 <.0001 <.0001
0.7813 0.9964 0.9964 <.0001 <.0001
0.6158 0.9998 0.9998 <.0001 <.0001 1.0000
<.0001 0.0017 0.0017 0.2987 <.0001 0.0003 0.0005
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 1.0000
1.0000 <.0001 <.0001 0.9964 0.9998 0.0017 <.0001 <.0001
<.0001 <.0001 0.9964 0.9998 0.0017 <.0001 <.0001
<.0001 <.0001 <.0001 0.2987 <.0001 <.0001
<.0001 <.0001 <.0001 <.0001 <.0001
1.0000 0.0003 <.0001 <.0001
0.0005 <.0001 <.0001
<.0001 <.0001
1.0000
13
FAKTORIAL RAKL DENGAN SAS PROGRAM data fakrakl; input y metode intensitas kelompok; label y='rata-rata nilai tes'; cards; 60 1 1 1 66 1 1 2 77 1 1 3 73 1 2 1 80 1 2 2 82 1 2 3 77 1 3 1 88 1 3 2 86 1 3 3 62 2 1 1 76 2 1 2 62 2 1 3 78 2 2 1 85 2 2 2 91 2 2 3 79 2 3 1 85 2 3 2 88 2 3 3 68 3 1 1 90 3 1 2 83 3 1 3 79 3 2 1 82 3 2 2 87 3 2 3 80 3 3 1 83 3 3 2 89 3 3 3 ; proc glm data=fakrakl; class metode intensitas kelompok; model y=metode intensitas metode*intensitas kelompok; lsmeans metode*intensitas/pdiff=all adjust=bon; run;
LOG NOTE: PROCEDURE GLM used (Total process time): real time 2:06.45 cpu time 1.18 seconds 106 107 108 109
data fakrakl; input y metode intensitas kelompok; label y='rata-rata nilai tes'; cards;
NOTE: The data set WORK.FAKRAKL has 27 observations and 4 variables. NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 137 138 139 140 141 142
; proc glm data=fakrakl; class metode intensitas kelompok; model y=metode intensitas metode*intensitas kelompok; lsmeans metode*intensitas/pdiff=all adjust=bon; run;
14
OUTPUT The GLM Procedure Class Level Information Class
Levels
metode intensitas kelompok
3 3 3
Values 1 2 3 1 2 3 1 2 3
Number of Observations Read Number of Observations Used
27 27
The GLM Procedure Dependent Variable: y
rata-rata nilai tes
Source
DF
Sum of Squares
Model Error Corrected Total
10 16 26
1728.222222 318.444444 2046.666667
Mean Square
F Value
Pr > F
172.822222 19.902778
8.68
<.0001
R-Square
Coeff Var
Root MSE
y Mean
0.844408
5.639224
4.461253
79.11111
Source metode intensitas metode*intensitas kelompok Source metode intensitas metode*intensitas kelompok
DF
Type I SS
Mean Square
F Value
Pr > F
2 2 4 2
156.2222222 788.6666667 255.1111111 528.2222222
78.1111111 394.3333333 63.7777778 264.1111111
3.92 19.81 3.20 13.27
0.0410 <.0001 0.0412 0.0004
DF
Type III SS
Mean Square
F Value
Pr > F
2 2 4 2
156.2222222 788.6666667 255.1111111 528.2222222
78.1111111 394.3333333 63.7777778 264.1111111
3.92 19.81 3.20 13.27
0.0410 <.0001 0.0412 0.0004
The GLM Procedure Least Squares Means Adjustment for Multiple Comparisons: Bonferroni metode
intensitas
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
y LSMEAN
LSMEAN Number
67.6666667 78.3333333 83.6666667 66.6666667 84.6666667 84.0000000 80.3333333 82.6666667 84.0000000
1 2 3 4 5 6 7 8 9
Least Squares Means for effect metode*intensitas Pr > |t| for H0: LSMean(i)=LSMean(j)
15
Dependent Variable: y i/j
1 1 2 3 4 5 6 7 8 9
0.3544 0.0164 1.0000 0.0093 0.0135 0.1119 0.0290 0.0135
2
3
4
5
6
7
8
9
0.3544
0.0164 1.0000
1.0000 0.1996 0.0093
0.0093 1.0000 1.0000 0.0053
0.0135 1.0000 1.0000 0.0077 1.0000
0.1119 1.0000 1.0000 0.0627 1.0000 1.0000
0.0290 1.0000 1.0000 0.0164 1.0000 1.0000 1.0000
0.0135 1.0000 1.0000 0.0077 1.0000 1.0000 1.0000 1.0000
1.0000 0.1996 1.0000 1.0000 1.0000 1.0000 1.0000
0.0093 1.0000 1.0000 1.0000 1.0000 1.0000
0.0053 0.0077 0.0627 0.0164 0.0077
1.0000 1.0000 1.0000 1.0000
1.0000 1.0000 1.0000
1.0000 1.0000
1.0000
16
RANCANGAN SPLIT PLOT DENGAN RAL MENGGUNAKAN SAS PROGRAM data splitplot; input i respons tanaman jarak r; cards; 1 75.55 1 90 1 2 91.79 2 90 1 3 89.37 3 90 1 4 82.41 1 100 1 5 84.24 2 100 1 6 80.49 3 100 1 7 74.65 1 110 1 8 81.22 2 110 1 9 80.77 3 110 1 10 79.81 1 160 1 11 82.88 2 160 1 12 84.6 3 160 1 13 60.21 1 90 2 14 88.92 2 90 2 15 87.88 3 90 2 16 81.89 1 100 2 17 81.34 2 100 2 18 79.45 3 100 2 19 73.52 1 110 2 20 80.98 2 110 2 21 81.38 3 110 2 22 78.12 1 160 2 23 83.84 2 160 2 24 83.27 3 160 2 25 71.46 1 90 3 26 90.53 2 90 3 27 70.43 3 90 3 28 84.65 1 100 3 29 85.22 2 100 3 30 81.11 3 100 3 31 75.13 1 110 3 32 79.44 2 110 3 33 82.1 3 110 3 34 76.34 1 160 3 35 82.37 2 160 3 36 90.25 3 160 3 ; proc glm data=splitplot; class tanaman jarak r; model respons=tanaman r(tanaman) jarak tanaman*jarak ; test h=tanaman e=r(tanaman); lsmeans tanaman*jarak/pdiff=all adjust=tukey; run;
LOG NOTE: PROCEDURE GLM used (Total process time): real time 4:40.75 cpu time 1.28 seconds 280 281 282
data splitplot; input i respons tanaman jarak r; cards;
17
NOTE: The data set WORK.SPLITPLOT has 36 observations and 5 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds 319 320 321 322 323 324 325
; proc glm data=splitplot; class tanaman jarak r; model respons=tanaman r(tanaman) jarak tanaman*jarak ; test h=tanaman e=r(tanaman); lsmeans tanaman*jarak/pdiff=all adjust=tukey; run;
OUTPUT The GLM Procedure Class Level Information Class
Levels
tanaman jarak r
3 4 3
Values 1 2 3 90 100 110 160 1 2 3
Number of Observations Read Number of Observations Used
36 36
The GLM Procedure Dependent Variable: respons Source
DF
Sum of Squares
Model Error Corrected Total
17 18 35
1044.849764 337.500933 1382.350697
R-Square 0.755850 Source tanaman r(tanaman) jarak tanaman*jarak Source tanaman r(tanaman) jarak tanaman*jarak
Coeff Var 5.342893
Mean Square
F Value
Pr > F
61.461751 18.750052
3.28
0.0082
Root MSE 4.330133
respons Mean 81.04472
DF
Type I SS
Mean Square
F Value
Pr > F
2 6 3 6
451.6972056 67.2314667 77.2175639 448.7035278
225.8486028 11.2052444 25.7391880 74.7839213
12.05 0.60 1.37 3.99
0.0005 0.7285 0.2830 0.0103
DF
Type III SS
Mean Square
F Value
Pr > F
2 6 3 6
451.6972056 67.2314667 77.2175639 448.7035278
225.8486028 11.2052444 25.7391880 74.7839213
12.05 0.60 1.37 3.99
0.0005 0.7285 0.2830 0.0103
Tests of Hypotheses Using the Type III MS for r(tanaman) as an Error Term Source
DF
Type III SS
Mean Square
F Value
Pr > F
tanaman
2
451.6972056
225.8486028
20.16
0.0022
The GLM Procedure Least Squares Means Adjustment for Multiple Comparisons: Tukey
18
tanaman
jarak
1 1 1 1 2 2 2 2 3 3 3 3
90 100 110 160 90 100 110 160 90 100 110 160
respons LSMEAN
LSMEAN Number
69.0733333 82.9833333 74.4333333 78.0900000 90.4133333 83.6000000 80.5466667 83.0300000 82.5600000 80.3500000 81.4166667 86.0400000
1 2 3 4 5 6 7 8 9 10 11 12
Least Squares Means for effect tanaman*jarak Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: respons i/j 1 2 3 4 5 6 7 8 9 10 11 12
1 0.0331 0.9173 0.3707 0.0005 0.0234 0.1221 0.0323 0.0419 0.1347 0.0778 0.0058
2
3
4
5
6
0.0331
0.9173 0.4411
0.3707 0.9525 0.9943
0.0005 0.6280 0.0102 0.0787
0.0234 1.0000 0.3495 0.9033 0.7306
0.4411 0.9525 0.6280 1.0000 0.9998 1.0000 1.0000 0.9997 1.0000 0.9987
0.9943 0.0102 0.3495 0.8336 0.4338 0.5098 0.8586 0.7030 0.1141
0.0787 0.9033 0.9998 0.9496 0.9740 0.9999 0.9974 0.5394
0.7306 0.2609 0.6360 0.5558 0.2392 0.3736 0.9777
0.9987 1.0000 1.0000 0.9978 0.9999 0.9998
Least Squares Means for effect tanaman*jarak Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: respons i/j 1 2 3 4 5 6 7 8 9 10 11 12
7
8
9
10
11
12
0.1221 0.9998 0.8336 0.9998 0.2609 0.9987
0.0323 1.0000 0.4338 0.9496 0.6360 1.0000 0.9998
0.0419 1.0000 0.5098 0.9740 0.5558 1.0000 1.0000 1.0000
0.1347 0.9997 0.8586 0.9999 0.2392 0.9978 1.0000 0.9996 0.9999
0.0778 1.0000 0.7030 0.9974 0.3736 0.9999 1.0000 1.0000 1.0000 1.0000
0.0058 0.9987 0.1141 0.5394 0.9777 0.9998 0.9049 0.9989 0.9962 0.8847 0.9673
0.9998 1.0000 1.0000 1.0000 0.9049
1.0000 0.9996 1.0000 0.9989
0.9999 1.0000 0.9962
1.0000 0.8847
0.9673
19
RANCANGAN STRIP PLOT DENGAN RAK MENGGUNAKAN SAS PROGRAM data stripplot; input respons r varietas dosis; cards; 2.05 1 1 1 2.5 1 2 1 2.15 1 3 1 2.6 1 4 1 3.3 1 1 2 3.25 1 2 2 3.25 1 3 2 2.95 1 4 2 2.95 1 1 3 2.85 1 2 3 2.75 1 3 3 3 1 4 3 2.25 2 1 1 2.6 2 2 1 2.5 2 3 1 2.5 2 4 1 3.5 2 1 2 2.95 2 2 2 3.45 2 3 2 3.05 2 4 2 2.9 2 1 3 3 2 2 3 3 2 3 3 2.85 2 4 3 2.45 3 1 1 2.45 3 2 1 2.35 3 3 1 2.25 3 4 1 3.45 3 1 2 3 3 2 2 3.5 3 3 2 3.15 3 4 2 2.95 3 1 3 3.15 3 2 3 3 3 3 3 2.95 3 4 3 ; proc glm data=stripplot; class r varietas dosis; model respons=r varietas r(varietas) dosis r(dosis) varietas*dosis r(varietas*dosis); test h=varietas*dosis e=r(varietas*dosis); run;
LOG NOTE: PROCEDURE GLM used (Total process time): real time 15.68 seconds cpu time 0.62 seconds
421 422 423
data stripplot; input respons r varietas dosis; cards;
NOTE: SAS went to a new line when INPUT statement reached past the end of a line. NOTE: The data set WORK.STRIPPLOT has 36 observations and 4 variables.
20
NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 463 464 465 466 467 468 469
; proc glm data=stripplot; class r varietas dosis; model respons=r varietas r(varietas) dosis r(dosis) varietas*dosis r(varietas*dosis); test h=varietas*dosis e=r(varietas*dosis); run;
OUTPUT The GLM Procedure Class Level Information Class
Levels
r varietas dosis
3 4 3
Values 1 2 3 1 2 3 4 1 2 3
Number of Observations Read Number of Observations Used
36 36
The GLM Procedure Dependent Variable: respons Source
DF
Sum of Squares
Model Error Corrected Total
35 0 35
5.39388889 0.00000000 5.39388889
R-Square 1.000000
Coeff Var .
Mean Square
F Value
0.15411111 .
Root MSE .
.
Pr > F .
respons Mean 2.855556
Source
DF
Type I SS
Mean Square
r varietas r(varietas) dosis r(dosis) varietas*dosis r(varietas*dosis)
2 3 6 2 4 6 12
0.05597222 0.02611111 0.13013889 4.43930556 0.02986111 0.48180556 0.23069444
0.02798611 0.00870370 0.02168981 2.21965278 0.00746528 0.08030093 0.01922454
Source
DF
Type III SS
Mean Square
r varietas r(varietas) dosis r(dosis) varietas*dosis r(varietas*dosis)
2 3 6 2 4 6 12
0.05597222 0.02611111 0.13013889 4.43930556 0.02986111 0.48180556 0.23069444
0.02798611 0.00870370 0.02168981 2.21965278 0.00746528 0.08030093 0.01922454
F Value . . . . . . . F Value . . . . . . .
Pr > F . . . . . . . Pr > F . . . . . . .
Tests of Hypotheses Using the Type III MS for r(varietas*dosis) as an Error Term Source varietas*dosis
DF
Type III SS
Mean Square
F Value
Pr > F
6
0.48180556
0.08030093
4.18
0.0169
21