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Analysis Using SPSS
Main Procedure is: Start →All Programs → SPSS for Windows → SPSS 15.0/ SPSS13.0/ SPSS10.0 (based on the version available on your machine) → Enter data in Data Editor → Analyze → GLM → Univariate → yield → [puts yield under Dependent list: ] → rep → [put rep under Fixed Factor(s): ] ms → [put ms under Fixed Factor(s):] mt → [put mt under Fixed Factor(s):] Continue → Model... [Opens Model dialogue box] → Custom → Build Term(s) → Main effects →[puts rep, ms, mt under Model: ] Interaction→rep → var → spt → [puts rep*ms and ms*mt under Model:] → Run All. Data
Input: For performing
analysis, input the data in the following format.
{Here one can call the replication as rep, main-plot treatments as ms (method
of sowing) and sub-plot treatments as mt (manorial treatment). (It
may, however, be noted that one can retain the same name or can code in any
other fashion). Following
are the brief description of the steps along with screen shots. ·
Open Data editor: Start → All Programs →
SPSS for Windows → SPSS 15.0/ SPSS13.0/
SPSS10.0
· 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. In the present case, entered data is in numeric format.
·
Once the data entry is complete, Choose Analyze from the Menu
Bar. Now select Analyze → General
linear Model → Univariate.
This selection displays the following screen.
·
Click Continue to return to the Univariate dialog box Syntax for
testing mainplot with error(a). Click Paste on the Univariate dialog box to get the commands in syntax editor. Now define model as per design adopted to analyze the data. Here it is /Test ms vs rep*ms.
·
Click Run → All. To
perform the analysis, the following syntax may be used after creating the data
file. UNIANOVA
YIELD BY REP MS MT
/METHOD = SSTYPE(3)
/INTERCEPT = INCLUDE
/CRITERIA = ALPHA(.05)
/DESIGN = REP MS MT MS*REP MS*MT /TEST
MS VS REP*MS. We could not find the syntax for pairwise
comparisons by selecting the appropriate error term. For making all
possible pairwise comparisons, one
may, however, compute only the means from the software and compute the minimum
significant differences using the given formulae
on the click of mouse.
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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