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Analysis of Data from Designed Experiments |
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|
Factorial RCB Design |
Analysis Using SPSS
Analysis Using SAS
To
test the significance of treatment combinations,
identification of best treatment combination, to compare
treatment combinations with and without farmyard manure(FYM)
and also to compare the treatments with and without
phosphorus solublizing bacteria (PSB),the analysis has to
be performed on treatment combinations.Therefore the three factor treatment
combinations are recoded as:
|
FYM |
P |
PSB |
TREATMENT NUMBER |
|
1 |
1 |
1 |
1 |
|
1 |
1 |
2 |
2 |
|
1 |
2 |
1 |
3 |
|
1 |
2 |
2 |
4 |
|
1 |
3 |
1 |
5 |
|
1 |
3 |
2 |
6 |
|
2 |
1 |
1 |
7 |
|
2 |
1 |
2 |
8 |
|
2 |
2 |
1 |
9 |
|
2 |
2 |
2 |
10 |
|
2 |
3 |
1 |
11 |
|
2 |
3 |
2 |
12 |
The analysis of the data is performed using PROC GLM of SAS. The SAS commands are given in the sequel.
Data
Input:
For performing analysis, input the data in the following format.
{Here Replication is termed as REP, three factors as FYM, P and PSB and treatment as TRT. 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 fact; /*one can enter any other name for data*/
INPUT REP FYM P PSB TRT Yield;
CARDS;
1
1
1
1
1
0.70
1
1
1
2
2
1.13
1
1
2
1
3
1.23
1
1
2
2
4
1.25
1
1
3
1
5
1.25
1
1
3
2
6
1.25
1
2
1
1
7
0.83
1
2
1
2
8
1.23
1
2
2
1
9
1.18
1
2
2
2
10
0.88
1
2
3
1
11
1.63
1
2
3
2
12
1.48
2
1
1
1
1
0.98
2
1
1
2
2
1.13
2
1
2
1
3
1.18
2
1
2
2
4
1.13
2
1
3
1
5
1.26
2
1
3
2
6
1.25
2
2
1
1
7
0.93
2
2
1
2
8
0.88
2
2
2
1
9
1.50
2
2
2
2
10
1.30
2
2
3
1
11
1.38
2
2
3
2
12
1.43
3
1
1
1
1
0.90
3
1
1
2
2
1.10
3
1
2
1
3
1.10
3
1
2
2
4
0.88
3
1
3
1
5
1.35
3
1
3
2
6
1.35
3
2
1
1
7
1.10
3
2
1
2
8
1.03
3
2
2
1
9
1.30
3
2
2
2
10
0.88
3
2
3
1
11
1.38
3
2
3
2
12
1.43
4
1
1
1
1
0.73
4
1
1
2
2
1.25
4
1
2
1
3
1.43
4
1
2
2
4
1.25
4
1
3
1
5
1.10
4
1
3
2
6
1.75
4
2
1
1
7
0.98
4
2
1
2
8
1.38
4
2
2
1
9
1.35
4
2
2
2
10
1.43
4
2
3
1
11
1.30
4
2
3
2
12
1.50
;
/*If
one is interested in answering first two questions, then
there is no need of recoding the treatment combinations and
adding the variable TRT in input.*/
*
Perform
the analysis of the main effects and the interactions of the
data using the following statements.
proc
glm;
class rep fym p psb;
model yield=rep fym|p|psb;
means fym p psb fym*p fym*psb p*psb fym*p*psb/lsd;
lsmeans fym p psb fym*p fym*psb p*psb fym*p*psb /pdiff;
run;
/*
LSD
option under means statement gives LSD value only for
comparing single factor level means and not for the means of
the level combination of 2 or 3 factors. For performing such
comparisons LSMEANS statement with PDIFF option may be used.
One
may use a host of multiple comparison procedures under the
options in MEANS statement viz. Least Significant
Difference (LSD), Duncan’s New multiple - range test (
For testing the significance of treatment combination, identification of best treatment combination and for contrast analysis use the following: */
proc
glm;
class
rep trt;
model
yield=rep trt;
means trt/tukey;
contrast
'1
2 3 4 5 6 vs 7 8 9 10 11 12' trt 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1;
contrast
'1
3 5 7 9 11 vs 2 4 6 8 10 12' trt 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1;
run;
Analysis Using SAS Analysis Using SPSS
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For exposure on SAS, SPSS,
MINITAB, SYSTAT and
MS-EXCEL for analysis of
data from designed experiments:
Please see Module I of Electronic Book II: Advances in Data Analytical Techniques
available at Design Resource Server (www.iasri.res.in/design)