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Analysis
Using SAS 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 one can
call the row numbers as row, column number as col and
treatments 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
latin; input row col trt
yield; cards; 1
1
3
3.10 1
2
6
5.95 1
3
1
1.75 1
4
5
6.40 1
5
2
3.85 1
6
4
5.30 2
1
2
4.80 2
2
1
2.70 2
3
3
3.30 2
4
6
5.95 2
5
4
3.70 2
6
5
5.40 3
1
1
3.00 3
2
2
2.95 3
3
5
6.70 3
4
4
5.95 3
5
6
7.75 3
6
3
7.10 4
1
5
6.40 4
2
4
5.80 4
3
2
3.80 4
4
3
6.55 4
5
1
4.80 4
6
6
9.40 5
1
6
5.20 5
2
3
4.85 5
3
4
6.60 5
4
2
4.60 5
5
5
7.00 5
6
1
5.00 6
1
4
4.25 6
2
5
6.65 6
3
6
9.30 6
4
1
4.95 6
5
3
9.30 6
6
2
8.40 ; proc glm; class row col trt; model yield = row col trt; means trt/tukey; contrast 'T3 T4 vs T5 T6'
trt 0
0 1 1
-1 -1; run;
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
(DUNCAN), Waller - Duncan (WALLER) test, Tukey’s 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 Duncan’s test. 95%
Confidence intervals about means can be obtained using CLM
option under MEANS statement.
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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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 | |||||
Contact Us | |||||
Other
Designed Experiments (Under Development) |
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For
exposure on SAS, SPSS, Please see Module I of Electronic Book II: Advances in Data Analytical Techniques available at Design Resource Server (www.iasri.res.in/design) |
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