dear all,
I need to know about the recent/advanced softwares for statistical analysis specially for plant tissue culture and diversity studies. if you suggest me some softwares or available sites,so that i can download if it is available for free, otherwise you can send me the *exe files too if possible.
thank you all.
If free is the key word, then R is your best choice. I think the correct site is https://www.r-project.org/
There is a long list of non-free choices. SAS is my favorite, but someone else pays for it. Many people use SPSS. Other options include Minitab, Mathematica, Matlab, Statistica, JMP, SYSTat, and a very long list of old programs like BMDP.
Your best option depends a great deal upon what you want to do and you level of skill both as a statistician and a programmer. If all you need is a t-test, then something like Microsoft Excel could work. If you need awesome statistical power because you are developing awesome new methods and applications then your best bet might be R.
1) SAS is expensive. However, it has awesome documentation, good support (at least in the USA), and is stable. The IO has changed a great deal, but the program that I wrote in 1980 will still run. SAS is actively updated. Thus while my old program still runs, I have a whole range of new choices. Also, I really like that the 4,000++ pages that used to be the SAS documentation are now online and searchable.
2) R is free. However, the documentation is hard to use (at least for a beginner). There are a large number of friendly people willing to help. However, the R platform is flexible. So much so, that a graduate student had to rewrite his code when one of the R packages changed (or stopped getting support). So a large number of people are producing a large amount of computer code, but that diversity can be confusing and not all of it is supported by the core R product. That said, this platform enables very rapid change.
3) Products like JMP that are more point-and-click are great for performing standard analyses. However, you are stuck with whatever standard the software company thinks that its user's want.
4) Products like Matlab and Mathematica can (I think) do any of the analyses you like if you are sufficiently skilled to program them correctly.
5) Minitab has changed a great deal over the last few decades. Originally I liked it as a tool to do matrix manipulation. It is great to see all the steps without having to invert a 10 x 10 matrix, or transposing the matrix without making an error.
6) Of course the most powerful stats package of all is doing the programming yourself. Fortran used to be the main platform, but I think C (or newer C++) is the more common platform. However, this is time consuming and risks a great deal of error.
7) The biodiversity analysis can be done using EstimateS. http://viceroy.eeb.uconn.edu/estimates/
Hey....u can use graphpad prism software ...its easy..
And many people use DMRT for comparison but u can use Tukey's test or Dunnet's test also...it's there in Graph pad prism...
U can download trial version from net.
The following links may help you .
https://www.researchgate.net/file.PostFileLoader.html?id=56e7c7d3cbd5c20b300c1947&assetKey=AS%3A339818895691786%401458030546495
https://www.researchgate.net/deref/http%3A%2F%2Fdocuments.tips%2Fdocuments%2Fstatistical-analysis-for-plant-tissue-culture-data-5626514a004cf.html
If free is the key word, then R is your best choice. I think the correct site is https://www.r-project.org/
There is a long list of non-free choices. SAS is my favorite, but someone else pays for it. Many people use SPSS. Other options include Minitab, Mathematica, Matlab, Statistica, JMP, SYSTat, and a very long list of old programs like BMDP.
Your best option depends a great deal upon what you want to do and you level of skill both as a statistician and a programmer. If all you need is a t-test, then something like Microsoft Excel could work. If you need awesome statistical power because you are developing awesome new methods and applications then your best bet might be R.
1) SAS is expensive. However, it has awesome documentation, good support (at least in the USA), and is stable. The IO has changed a great deal, but the program that I wrote in 1980 will still run. SAS is actively updated. Thus while my old program still runs, I have a whole range of new choices. Also, I really like that the 4,000++ pages that used to be the SAS documentation are now online and searchable.
2) R is free. However, the documentation is hard to use (at least for a beginner). There are a large number of friendly people willing to help. However, the R platform is flexible. So much so, that a graduate student had to rewrite his code when one of the R packages changed (or stopped getting support). So a large number of people are producing a large amount of computer code, but that diversity can be confusing and not all of it is supported by the core R product. That said, this platform enables very rapid change.
3) Products like JMP that are more point-and-click are great for performing standard analyses. However, you are stuck with whatever standard the software company thinks that its user's want.
4) Products like Matlab and Mathematica can (I think) do any of the analyses you like if you are sufficiently skilled to program them correctly.
5) Minitab has changed a great deal over the last few decades. Originally I liked it as a tool to do matrix manipulation. It is great to see all the steps without having to invert a 10 x 10 matrix, or transposing the matrix without making an error.
6) Of course the most powerful stats package of all is doing the programming yourself. Fortran used to be the main platform, but I think C (or newer C++) is the more common platform. However, this is time consuming and risks a great deal of error.
7) The biodiversity analysis can be done using EstimateS. http://viceroy.eeb.uconn.edu/estimates/
PAST is simple in use and free. And it allows to estimate several diversity indexes.
http://folk.uio.no/ohammer/past/
I recommend R in combination with R-Studio www.rstudio.com/ - you can find some good introductions on Stats with R @ www.statmethods.net - Enjoy!
I personally enjoy working with PAST (which has already been suggested by Tatiana Chuzhekova), NSDstat (which, unfortunately, is not free) and PSPP, which aims to be a free alternative to SPSS. PSPP can be used for most common statistical procedures such as t-tests, ANOVA, linear regression, logistic regression, cluster analysis, reliability analysis, factor analysis etc. pp. If you don't know the software yet, it might be worth a try (see attached link).
https://www.gnu.org/software/pspp/
U can try graph pad or biodiversity pro also for statistical analysis
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