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How to use Genstat for mean
comparision?
Prepared By
Krishna Dhakal
M.Sc.Ag
Genetics and Plant Breeding
Faculty of Agriculture
Agriculture and Forestry University
Rampur, Chitwan, Nepal
Final work: March 13, 2017
A simple guideline
First of all you make sure that your data is correctly entered in excel
sheet
 Select and copy the data from the excel sheet (make sure you select
and copy all the data)
Bring the icon of Genstat on your desktop
Now, open your Genstat desktop icon
Go to “tools”---”Option”---”menu”---choose “show multiple mean
comparison on ANOVA menu” and click “OK”
Click on spread…..New….. then “from clipboard”
A small window of new spreadsheet will appear, Click “yes” on
“column name on first row” , that will show the heading of your data
table
Data on excel sheet
View of window on opening
Loading data
To load data with its headings click on
“yes” of the “column names in first row”
Continued….
 Now, in the left side of your Screen data will be seen and in right side
small window will appear where you have to fix which one is factor
and which one is the variables and click “OK”
 Replication, block, varieties etc are factors and plant height, yield, etc
are variables
 you can denote it by double clicking on the shown options
 When the work is done, a red colored exclamation mark (!) appears on
the concerned subject, if you wrongly fixed the factors, you can again
change it by clicking in the concerned subject and correct it
Data loaded
Red exclamation sign
Correcting the mistake
For single factor comparision
 You have now fixed the factors, its time for mean comparison
 Go to “stat” click on “analysis of variance” choose “General” and
choose the design of your experiment, load “Y” variate and treatment
and click “Run”
 To view the results, go to “window” click on “output” and see the
results
For one factor ANOVA with no
blocking
Choosing one way ANOVA no
blocking according to the field design
Click “run” after you fill the boxes
To view the output
Output
For two factor mean comparison
(RCBD)
 The process is similar to one factor mean comparison
 The difference occurs while choosing Design of experiment, and other
options according to your interest
 When you choose two way RCBD then the small window of analysis of
variance would appear as follows
Data used in the example
Typical window for two factor RCBD
Continue…..
 In the above slide you can see “option” in the right side of “run”, you
can use that for desirable setting of your analysis
 For example: you would like to obtain CV, standard errors of means
and other too based on the requirement for your analysis
 Also you can go to “multiple comparison” option that is available in
ANOVA options and set the desirable changes, the ANOVA options is
shown in the below slide
ANOVA options
Multiple comparison option
Continued……
 From multiple comparison options you can choose either variety or
environment or variety and environment interaction as a treatment to
compare the means.
 Also you can choose whether you want the results of means comparison
in ascending or descending order.
 You can also choose the type of test you wanted to choose.
 After choosing, click on “OK” in both multiple comparison and ANOVA
options mini window
 Click “Run” and then observe the output clicking “window” and
selecting “output”
Test of your choices
Split plot design
 The similar is the procedure like in two factorial RCBD
 The only difference occurs while choosing the Design of experiment also
the desired treatment interaction should be given that is interaction may
be main plot treatment Vs sub plot treatment or subplot Vs main plot.
 Here in example either environment or treatment can be taken as main
plot and the variety as sub plot treatment or variety as main plot or
environment as subplot treatment.
 Accordingly the treatment interaction will be env × var or var × env
 Click “Run” and then observe the output clicking “window” and
selecting “output”
Analysis of variance in split plot
Strip plot design
 The similar is the procedure like in split plot design
 The only difference occurs while choosing the Design of experiment
and the interaction between row and column should be designed
 That means interaction treatment may be between row and column or
column and row
 Click “Run” and then observe the output clicking “window” and
selecting “output”
Strip plot design
How to use genstat

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How to use genstat

  • 1. How to use Genstat for mean comparision? Prepared By Krishna Dhakal M.Sc.Ag Genetics and Plant Breeding Faculty of Agriculture Agriculture and Forestry University Rampur, Chitwan, Nepal Final work: March 13, 2017
  • 2. A simple guideline First of all you make sure that your data is correctly entered in excel sheet  Select and copy the data from the excel sheet (make sure you select and copy all the data) Bring the icon of Genstat on your desktop Now, open your Genstat desktop icon Go to “tools”---”Option”---”menu”---choose “show multiple mean comparison on ANOVA menu” and click “OK” Click on spread…..New….. then “from clipboard” A small window of new spreadsheet will appear, Click “yes” on “column name on first row” , that will show the heading of your data table
  • 4. View of window on opening
  • 5.
  • 7. To load data with its headings click on “yes” of the “column names in first row”
  • 8. Continued….  Now, in the left side of your Screen data will be seen and in right side small window will appear where you have to fix which one is factor and which one is the variables and click “OK”  Replication, block, varieties etc are factors and plant height, yield, etc are variables  you can denote it by double clicking on the shown options  When the work is done, a red colored exclamation mark (!) appears on the concerned subject, if you wrongly fixed the factors, you can again change it by clicking in the concerned subject and correct it
  • 12. For single factor comparision  You have now fixed the factors, its time for mean comparison  Go to “stat” click on “analysis of variance” choose “General” and choose the design of your experiment, load “Y” variate and treatment and click “Run”  To view the results, go to “window” click on “output” and see the results
  • 13. For one factor ANOVA with no blocking
  • 14. Choosing one way ANOVA no blocking according to the field design
  • 15. Click “run” after you fill the boxes
  • 16. To view the output
  • 18. For two factor mean comparison (RCBD)  The process is similar to one factor mean comparison  The difference occurs while choosing Design of experiment, and other options according to your interest  When you choose two way RCBD then the small window of analysis of variance would appear as follows
  • 19. Data used in the example
  • 20. Typical window for two factor RCBD
  • 21. Continue…..  In the above slide you can see “option” in the right side of “run”, you can use that for desirable setting of your analysis  For example: you would like to obtain CV, standard errors of means and other too based on the requirement for your analysis  Also you can go to “multiple comparison” option that is available in ANOVA options and set the desirable changes, the ANOVA options is shown in the below slide
  • 24. Continued……  From multiple comparison options you can choose either variety or environment or variety and environment interaction as a treatment to compare the means.  Also you can choose whether you want the results of means comparison in ascending or descending order.  You can also choose the type of test you wanted to choose.  After choosing, click on “OK” in both multiple comparison and ANOVA options mini window  Click “Run” and then observe the output clicking “window” and selecting “output”
  • 25. Test of your choices
  • 26. Split plot design  The similar is the procedure like in two factorial RCBD  The only difference occurs while choosing the Design of experiment also the desired treatment interaction should be given that is interaction may be main plot treatment Vs sub plot treatment or subplot Vs main plot.  Here in example either environment or treatment can be taken as main plot and the variety as sub plot treatment or variety as main plot or environment as subplot treatment.  Accordingly the treatment interaction will be env × var or var × env  Click “Run” and then observe the output clicking “window” and selecting “output”
  • 27. Analysis of variance in split plot
  • 28. Strip plot design  The similar is the procedure like in split plot design  The only difference occurs while choosing the Design of experiment and the interaction between row and column should be designed  That means interaction treatment may be between row and column or column and row  Click “Run” and then observe the output clicking “window” and selecting “output”