 |
Analysis of Data from Designed Experiments |
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|
Resolvable Block Design |
Analysis Using SAS
For performing the analysis, following steps may be used:
Data
Input:
For
performing analysis, input the data in the following
format.
{Here
the strain codes (treatments) are termed as trt, Replication
as rep, block as blk and seed yield as syield. 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
toria; /*one can enter any other name for Data*/;
Input
rep blk trt syield;
Cards;
1
1
1
1555.6
1
1
5
1160.5
1
1
9
1308.6
1
1
13
1382.7
1
1
17
987.7
1
1
21
1135.8
1
2
2
1284.0
1
2
6
1086.4
1
2
10
1284.0
1
2
14
1111.1
1
2
18
938.3
1
2
22
1308.6
1
3
3
1234.6
1
3
7
419.8
1
3
11
1308.6
1
3
15
963.0
1
3
19
963.0
1
3
23
987.7
1
4
4
1234.6
1
4
8
987.7
1
4
12
1284.0
1
4
16
913.6
1
4
20
1160.5
1
4
24
790.1
2
1
1
1481.5
2
1
6
1086.4
2
1
11
1308.6
2
1
16
1284.0
2
1
19
1111.1
2
1
22
1185.2
2
2
2
987.7
2
2
7
308.6
2
2
12
1234.6
2
2
13
1308.6
2
2
20
765.4
2
2
23
938.3
2
3
3
1012.3
2
3
8
864.2
2
3
9
1234.6
2
3
14
938.3
2
3
17
913.6
2
3
24
864.2
2
4
4
1135.8
2
4
5
987.7
2
4
10
987.7
2
4
15
740.7
2
4
18
963.0
2
4
21
1135.8
3
1
1
1284.0
3
1
7
333.3
3
1
12
1135.8
3
1
15
839.5
3
1
18
814.8
3
1
24
888.9
3
2
2
1135.8
3
2
8
913.6
3
2
9
1456.8
3
2
16
1037.0
3
2
19
938.3
3
2
21
1037.0
3
3
3
963.0
3
3
5
1209.9
3
3
10
1259.3
3
3
13
1234.6
3
3
20
963.0
3
3
22
1111.1
3
4
4
1086.4
3
4
6
765.4
3
4
11
1111.1
3
4
14
1037.0
3
4
17
938.3
3
4
23
938.3
;
/*To test the equality of treatment effects, replication effects and replication within block effects, one can perform the analysis of variance of the data using the following statements.*/
Proc glm;
Class
rep blk trt;
Model
syield = rep blk(rep) trt ;
Means
trt;
Lsmeans
trt/PDIFF;
Run;
Here LSMEAN gives adjusted treatment means (least squares means) and PDIFF performs all possible pair wise treatment comparisons and gives the exact probability level of significance in a v` ×v matrix, where v is the number of treatments. Type III sum of squares may be used for inferring on the equality of treatment and block effects.
Here the treatment 20, 21 and 23 are checks. Using the adjusted means, identify the check with highest adjusted yield (lsmean) in kg/ha and list the treatments having more adjusted yield than the highest performing check. Now identify the treatments having significantly higher yield than the best performing check using the v` ×v matrix of probability level of significance.
Analysis Using SAS Analysis Using SPSS
Home Descriptive Statistics Tests of Significance Correlation and Regression Completely Randomised Design RCB Design
Incomplete Block Design Resolvable Block Design Augmented Design Latin Square Design Factorial RCB Design
Partially Confounded Design Factorial Experiment with Extra Treatments Split Plot Design Strip Plot Design
Response Surface Design Cross Over Design Analysis of Covariance Diagnostics and Remedial Measures
Principal Component Analysis Cluster Analysis Groups of Experiments Non-Linear Models
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(Under Development)
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)