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 Randomized Complete Block Design

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Analysis Using SPSS 

  Analysis  Using  SAS 

To answer the question whether the performance of check varieties is significantly different from strains, treatment contrast analysis is to be performed  Therefore, for ease of understanding, recode the varieties from 1 to 24 as follows:

Strain

Code

Number Code

RK-04-3

MCN-04-110

1

RK-04-4

MCN-04-111

2

RGN-124

MCN-04-112

3

HYT-27

MCN-04-113

4

PBR-275

MCN-04-114

5

HUJM-03-03

MCN-04-115

6

RGN-123

MCN-04-116

7

BIO-13-01

MCN-04-117

8

RH-0115

MCN-04-118

9

RH-0213

MCN-04-119

10

NRCDR-05

MCN-04-120

11

NRC-323-1

MCN-04-121

12

RRN-596

MCN-04-122

13

RRN-597

MCN-04-123

14

CS-234-2

MCN-04-124

15

RM-109

MCN-04-125

16

BAUSM-2000

MCN-04-126

17

NPJ-99

MCN-04-127

18

SWARNA JYOTI(ZC)

MCN-04-128

19

VARDAN (NC)

MCN-04-129

20

PR-2003-27

MCN-04-130

21

VARUNA (NC)

MCN-04-131

22

PR-2003-30

MCN-04-132

23

KRANTI-(NC)

MCN-04-133

24

 To test equality of treatment effects, analysis using SAS may be performed using the following steps:

 Data Input:
For performing analysis, input the data in the following format. 

{Here we call the strain codes as treatments: TRT, strain number as Treatment Number: TRTN, Replication: REP. It may, however, be noted that one can retain the same name or can code in any other fashion}.

 Prepare a SAS data file using

 Data rbd;     /*one can enter any other name for Data*/;
Input TRT $ 11. TRTN REP YIELD;
*here 11. represents that the value of the variable trt is upto 11 columns;
Cards;                           

MCN-04-110  1     1     1539.69
MCN-04-111  2     1     1261.85
MCN-04-112  3     1     1389.19
MCN-04-113  4     1     1192.39
MCN-04-114  5     1     1250.27
MCN-04-115  6     1     1296.58
MCN-04-116  7     1     1227.12
MCN-04-117  8     1     1273.43
MCN-04-118  9     1     1180.82
MCN-04-119  10    1     1296.58
MCN-04-120  11    1     1122.93
MCN-04-121  12    1     1250.27
MCN-04-122  13    1     1180.82
MCN-04-123  14    1     1146.09
MCN-04-124  15    1     1574.42
MCN-04-125  16    1     914.55
MCN-04-126  17    1     891.4
MCN-04-127  18    1     1227.12
MCN-04-128  19    1     1389.19
MCN-04-129  20    1     1331.31
MCN-04-130  21    1     1250.27
MCN-04-131  22    1     717.75
MCN-04-132  23    1     1169.24
MCN-04-133  24    1     1203.97
MCN-04-110  1     2     1412.35
MCN-04-111  2     2     1065.05
MCN-04-112  3     2     1516.54
MCN-04-113  4     2     1215.55
MCN-04-114  5     2     1203.97
MCN-04-115  6     2     1273.43
MCN-04-116  7     2     1018.74
MCN-04-117  8     2     1157.66
MCN-04-118  9     2     1203.97
MCN-04-119  10    2     1458.65
MCN-04-120  11    2     1065.05
MCN-04-121  12    2     926.13
MCN-04-122  13    2     1053.47
MCN-04-123  14    2     1180.82
MCN-04-124  15    2     1412.35
MCN-04-125  16    2     972.44
MCN-04-126  17    2     937.71
MCN-04-127  18    2     1203.97
MCN-04-128  19    2     1180.82
MCN-04-129  20    2     1157.66
MCN-04-130  21    2     1250.27
MCN-04-131  22    2     740.9
MCN-04-132  23    2     1157.66
MCN-04-133  24    2     1296.58
MCN-04-110  1     3     1319.73
MCN-04-111  2     3     1111.36
MCN-04-112  3     3     1203.97
MCN-04-113  4     3     1157.66
MCN-04-114  5     3     1366.04
MCN-04-115  6     3     1308.16
MCN-04-116  7     3     937.71
MCN-04-117  8     3     1088.2
MCN-04-118  9     3     1041.9
MCN-04-119  10    3     1250.27
MCN-04-120  11    3     1018.74
MCN-04-121  12    3     1030.32
MCN-04-122  13    3     717.75
MCN-04-123  14    3     856.67
MCN-04-124  15    3     1597.57
MCN-04-125  16    3     659.87
MCN-04-126  17    3     798.79
MCN-04-127  18    3     1389.19
MCN-04-128  19    3     1273.43
MCN-04-129  20    3     1180.82
MCN-04-130  21    3     1296.58
MCN-04-131  22    3     578.83
MCN-04-132  23    3     1111.36
MCN-04-133  24    3     1250.27
; 

*To test the equality of treatment effects, one can perform the analysis of variance using the following statements;

PROC ANOVA;
Class REP TRTN;
Model    YIELD  = REP TRTN;
Run;

/*One can obtain means of treatments and all possible pair wise treatment comparisons can be performed using any of the multiple comparison procedures such as least significant difference, Duncanís New Multiple range test, Tukey's Honest Significant difference test by using  the following statements:*/

PROC ANOVA;
Class REP TRTN;
Model    Yield  = REP TRTN;
Means TRTN/LSD;   /*performs all possible pairwise treatment comparisons using LSD*/
Means  TRTN/DUNCAN;   /*performs all possible pairwise treatment comparisons using Duncan's Multiple Range Test*/
Means TRTN/Tukey;  /*performs all possible pairwise treatment comparisons using Tukey's HSD. For controlling the family error rate, it is advisable to perform these comparisons using Tukey's HSD*/;
Run;

/*One may use a host of multiple comparison procedures under the options in MEANS statement viz.  Least Significant Difference (LSD), Duncanís New multiple - range test (DUNCAN), Waller - Duncan (WALLER) test, Tukeyís Honest Significant Difference (TUKEY).  The LSD, DUNCAN and TUKEY options takes level of significance ALPHA = 5% unless ALPHA = options is specified.  Only ALPHA = 1%, 5% and 10% are allowed with the Duncanís test.  95% Confidence intervals about means can be obtained using CLM option under MEANS statement. 

Contrast analysis can not be performed using PROC ANOVA,  therefore, one has to use PROC GLM. This can be done using the following steps.

Note: Here we want to test

*/

* The following SAS statements can be made use of ;

 PROC GLM;
Class REP TRTN;
Model    YIELD  = REP TRTN;
Contrast 'check vs varieties' TRTN
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 -20 -20 4 -20 4 -20;
Run;

Data File

Result File

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