Which statistical package is best

Both methods are important and give different insights. They simplify large quantities of data for easy interpretation, without making conclusions beyond the analysis or answering any hypotheses. Instead of proceeding data in its raw form, descriptive statistics allows us to present and interpret data more easily.

In contrast, inferential statistics allows analysts to test a hypothesis based on a sample of data from which they can make inferences and generalizations about the greater whole. Inferential statistics tries to make conclusions about future outcomes beyond the data available. For descriptive statistics, we choose a group to study, measure all the subjects in that group and describe the group in exact numbers. Descriptive statistics can be helpful in looking into such things as the spread and center of the data, but because descriptive statistics are stated in exact numbers, they cannot be used to make broader generalizations or conclusions.

For inferential statistics, we instead start by defining the target population and then plan how to obtain a representative sample. After analyzing the sample and testing hypotheses based on the sample data, the result will be expressed in confidence intervals and margins of errors, based on the uncertainty of using a sample that cannot perfectly represent the population.

Both kinds of statistics are at the heart of the statistical analysis that powers statistical software, used hand in hand to solve business problems with intelligence. Statistical software can help with business intelligence in many different ways. Statistical analysis can give insight into how effectively your business is operating, and help you think ahead with predictive analytics models based on historical data.

So what are the benefits of using a statistical analysis tool for business intelligence? There are many factors to consider when choosing statistics software. Using a complicated advanced tool like statistical software for simple data sets is impractical; statistical analysis tools work best with complicated sets of quantitative data.

If your analysis needs are less demanding, a business analytics tool may be more suitable for you. Products tend to offer different ranges of statistical theorems and algorithms, but some users may only need to use a small percentage of these functions. If you have a massive amount of data to analyze, you may want to invest in a tool built to handle large data sets with speed.

You should look for a tool that performs exactly the kind of data analyses you need it to. Who will use the tool? Will your analysts be experts, amateurs, or somewhere in between? Will they analyze data continuously in real-time, or will they do more statistical analysis on an ad-hoc self-service basis? Are they primarily data analysts or scientists? Your statistical analysis software should meet the needs of the person using it, so make sure to choose a package that does exactly what your user needs it to.

Statistical analysis is by no means easy, and many statistical software platforms can be confusing and downright unintelligible to the average user. Some tools also have a higher learning curve than others, making them more difficult to master.

After considering who will be using the tool, determine what their level of experience with statistics is. Expert data scientists will feel at home crunching numbers with equations and programming languages, but novice users may feel overwhelmed with a software presented in that format and prefer using a more familiar menu-based interface.

Do your engineers need a robust statistical analysis platform with powerful coding capabilities, or do your analysts need a simpler statistical tool that can display basic models, or do you need something in between? Considering the interoperability and integration capabilities of prospective statistics software is an important step in the vetting process.

While statistical software helps businesses derive deeper insights from their data, they are often just a cog in the machine of their technology ecosystems. More frequently than not, your business may need more than just one solution to address its analytical needs. Will the new solution play well with others? If your business currently uses any other programs, it can be helpful to get a statistical analysis tool that supports the databases, file formats and frameworks of your existing solutions.

Some statistical packages are feature-packed with data visualization options, while others generate graphics that are much more bare-bones, with less customization available. Do you prefer interactive or static visualizations? Will you need your statistical analysis software to produce visually appealing graphics outright?

Statistical software packages range in price from free for open-source tools like Python and R, to thousands of dollars per license for more robust offerings. Will you need just one license, or several?

If someone like me has to buy a license, then to me, Stata is a no-brainer, given all the stats you can do with it. I actually agree with you about Stata. Depends on which social scientists you are talking about. I doubt you will find many economists, for example, who do most if any of their analyses in SPSS. If you absolutely must have a gui JMP is clearly the superior platform, since its scripting language can interface with R, and you can do whatever you please. I have used it for repeated measures data by mixed model when a colleague wanted help doing it himself, where the posthoc tests where flexible and accessible, compared to his version of Stata or in R.

Thanks, Dave. Yes, I agree. Good advice, all around. The overlap is just too great to make either a good complement to the other. A factor to consider in choosing between the Big Two is your preferred user interface. SEMs are confusing enough without worrying about converting from your preferred expression of the models into the expression your software wants. Stata users say it has some very slick programming facilities.

The S dialects are killers for simulation studies. R is built entirely around an object-oriented programming interface. Language extensions are a snap. In my opinion bootstrap estimation is easier in R than in other languages. High resolution graphics are native to R, and despite a lot of improvement from versions 6 to 7 to 9. I believe the above comment is spam. I figured it was a language difficulty, and they meant S-Plus, on which R was based.

Your email address will not be published. Skip to primary navigation Skip to main content Skip to primary sidebar In addition to the five listed in this title, there are quite a few other options, so how do you choose which statistical software to use?

And here are my observations: 1. Karen will introduce you to how SPSS is set up, some hidden features to make it easier to use, and some practical tips. Take Me to The Video! Comments Rguroo is a newly developed software for teaching statistics that is becoming increasingly popular. Hello,am an actuarial science student. Kindly help me choose the best software to learn. I want to pursue MSC in ecology. Thanks Cisima. It is more rich than SPSS for policy research which is in your area as an economist.

Hey Paul and Lisa, You can also check out our list of Stata resources , which includes 2 free webinars. Hi Karen, nice suggestions backed with arguments! Hi Ragnar, Thanks! MatLab is an analytical platform and programming language that is widely used by engineers and scientists.

As with R, the learning path is steep, and you will be required to create your own code at some point. While MatLab can be difficult to use for novices, it offers a massive amount of flexibility in terms of what you want to do — as long as you can code it or at least operate the toolbox you require. While not a cutting-edge solution for statistical analysis, MS Excel does offer a wide variety of tools for data visualization and simple statistics.

As many individuals and companies both own and know how to use Excel, it also makes it an accessible option for those looking to get started with statistics. SAS is a statistical analysis platform that offers options to use either the GUI, or to create scripts for more advanced analyses.

It is a premium solution that is widely used in business, healthcare, and human behavior research alike. GraphPad Prism is premium software primarily used within statistics related to biology, but offers a range of capabilities that can be used across various fields.

Similar to SPSS, scripting options are available to automate analyses, or carry out more complex statistical calculations, but the majority of the work can be completed through the GUI. The Minitab software offers a range of both basic and fairly advanced statistical tools for data analysis. SPSS is commonly used in universities, particularly in the social sciences and psychology.

In more recent versions, the software is developed by IBM, strongly in the direction of a tool which accomplishes evaluations that can be largely automated and do not require special method knowledge from the user. In addition, the short release cycle has negatively impacted stability in the past. While SPSS comes with some more specific modules e. STATA is a commercial statistical software that is particularly favored by econometricians.

Considerable discounts are available when purchasing multiple licenses, and special conditions apply for the education sector. Conclusion Although STATA is a mature, very stable, and powerful software, its distribution — especially in companies — is low. For users who value a broad spectrum of methods, stability, a mature operating concept including scripting language and a fair price, STATA is superior to the more expensive commercial competition. The five programs discussed above are the undisputed market leaders in the field of universally-applicable statistics programs, and they cover almost the entire spectrum of statistical methods.

There are also several programs that have specialized in certain methods, and have thus been able to establish themselves for certain applications. Some of these programs are mentioned — briefly — below:. In addition, there are a number of commercial and open source programs that specialize in data mining methods.

Want to share your content on R-bloggers? R R is a popular, open-source statistics environment that can be extended by packages almost at will.