 |
Analysis of Data from Designed Experiments |
 |
|
Analysis of Covariance |
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
variety number
is termed as trt, replication as rep, tolerance score of
each variety as Y and
score of the corresponding susceptible check as X. 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
ancova_rbd; /*one
can enter any other name for data*/
input
trt rep x y;
cards;
1
1
5
2
2
1
6
4
3
1
5
4
4
1
6
3
5
1
7
7
6
1
6
4
7
1
6
3
8
1
6
6
9
1
7
4
10
1
7
7
11
1
6
5
12
1
6
5
13
1
5
4
14
1
5
5
15
1
5
4
1
2
6
3
2
2
5
3
3
2
5
4
4
2
5
3
5
2
7
6
6
2
5
3
7
2
5
3
8
2
7
7
9
2
5
3
10
2
7
7
11
2
5
4
12
2
5
3
13
2
5
4
14
2
5
4
15
2
5
5
1
3
6
4
2
3
5
3
3
3
5
3
4
3
5
3
5
3
6
6
6
3
5
3
7
3
6
3
8
3
6
6
9
3
5
4
10
3
5
6
11
3
5
5
12
3
5
3
13
3
6
5
14
3
5
3
15
3
6
6
;
-
To perform analysis of covariance by taking tolerance score of each variety (Y) as dependent variable and score of the corresponding susceptible check (X) as covariate and
-
To perform all possible pair wise variety comparisons and to identify the best variety one can follow the following steps.
proc
glm;
class
trt rep;
model
y = rep trt x;
lsmeans
trt/pdiff
;
run;
Here
LSMEANS gives adjusted treatment means (least squares means)
and pdiff performs all possible pair wise treatment comparisons
give the exact probability level of significance in a
v ´
v
matrix. Hear v denotes the number of treatments.
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
Copyright Disclaimer How to Quote this page Report Error Comments/suggestions
(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)