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Analysis Using SPSS Main
Procedure is: ·
Following are
the brief description of the steps along with screen shots.
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. ·
Enter
data in SPSS Data editor. There are two views in SPSS Data
Editor. In variable view, one can define the name of
variables and variable types string or numeric and data view
gives the spreadsheet in which data pertaining to variables
may be entered in respective columns.
·
Once the data
entry is complete, Choose Analyze from the Menu Bar. Now
select
· This selection displays the following screen
·
Select Y and
send it to the Dependent Variable box; TRT and REP may be
selected for Fixed Factor(s) box and select X and send
it to the Covariate(s): box. After doing this the dialog box
should be like this.
One gets the following screen
2. To perform all possible pair wise variety
comparisons and to identify the best variety one can follow
the following steps. ·
Click Continue
to return back to the Univariate dialog box, ·
Now select
Option.. and check the box for Compare main effects(LSD is
default) for the multiple
pairwise comparison procedure . ·
The following
screen appears:
·
Click on Continue
to return back to the Univariate dialog box.
·
Click on OK to
get the output. After
creating the data file following syntax may be used in the
syntax editor mode to get the output. UNIANOVA
y BY rep
trt WITH x
/METHOD = SSTYPE(3)
/INTERCEPT = INCLUDE
/EMMEANS = TABLES(trt) WITH(x=MEAN) COMPARE ADJ(LSD)
/CRITERIA = ALPHA(.05)
/DESIGN = rep trt x .
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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