The most valuable feature is its robust statistical analysis capabilities.

The most valuable features mainly include factor analysis, correlation analysis, and demographic analysis. I find the user interface to be intuitive and improved compared to earlier versions. IBM periodically updates their products, which enhances their usability and performance. Smoothness is crucial for researchers when using software, and in this regard, IBM SPSS is highly recommended.

One feature I found very valuable was the analysis of variance (ANOVA). Also, when it comes to result presentation, being able to present your results in graphs is important. Additionally, when discussing hypotheses and calculating probabilities, it's helpful to make a note of any significant differences in your tests. This information can improve your business processes for that team.

The same goes for full ANOVA and multiple ANOVA. Those are the features that I found very useful, especially when it comes to presenting demographic information.

IBM SPSS Statistics depends on AI. 

Most of the product features are good but I particularly like the linear regression analysis. I also benefit from the import and export of data abilities. 

The product’s most valuable capability is to handle large datasets and ensure robust analysis simultaneously. We can write, save, and reuse the scripts for further procedures. We do not have to run the program file again on the different datasets.

The analytics on offer are very good.

It offers very good visualization. 

The dashboards are excellent. 

It's stable. 

I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well.

The automated statistics testing and principle components analysis are very useful as well.

I've also found the decision tree elements pretty useful too.

In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions. I'm using most of the content of SPSS.

This product provides the opportunity to better explore data and provide complete data analysis because it works both with modeling and non-parametric analysis.  

The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful.

SPSS is the way to go if you have data amenable to quantitative analysis, and you need high-accuracy results. 

The solution is quite robust. I wouldn't pick one most valuable feature, but everything is quite handy. If you want to do a quick analysis, SPSS is quite robust and quicker in terms of providing the output. Python does take large sets of data, and it has more functionalities, but in terms of quick turnaround, I think SPSS is quite good.

The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it. It's a dashboard where they can get more information. And then, if they want to do a deeper dive into some things, they tell us, and we will work with the research department. We can either add or point to the field or fields and give them some more details. 

Say, for example, you wanted to see things like class registration broken down by area of study. So instead of just looking at enrollment, you can dive into specifics about which courses students are taking and what popular disciplines. We can do all that with SPSS. 

It's also customizable. You can add or remove features as needed to make it fit what you're looking for. Instructions for how to do this are all over our website. For example, on the research page, you see a default report, but you can pretty much create your own version that highlights what you want to show.

The solution has numerous valuable features. We particularly like custom tabs. It's very useful. We end up analyzing a lot of software data, so features related to custom tabs are really helpful.

All of the clustering for multi-related techniques including clustering, factoring, etc. is quite useful.

The cost integration features are very good.

The tests, videos, and tools that are the most useful depend on what objective the company has, but all of the features are quite good.

The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS.

The features that I find to be the most valuable are the anomaly detection and its statistics. The clustering statistics are important as well as the usual old-school statistical analysis called ANOVA and ANCOVA. They are critical.

There were some new approaches that I hadn't used before. 

The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important. Many people don't read these types of handbooks, but they should because it was very useful and I use it a lot.

Well, actually, in terms of the technique that we use, and also the implementation, I prefer to use SPSS Clementine right now. It can cover a big amount of data, instead of just basic SPSS where you have limitations, how many observations you can analyze. In terms of the features, I think that the modelling section in IBM SPSS Clementine is very good. it has a lot of modeling techniques compared to other tools.

The features that I have found most valuable are the Bayesian statistics and descriptive statistics. I use these more often because in pharma companies and clinical hospitals they make the medicines by taking the feedback from different patients. For example, there could be a neuro-disorder patient or a normal IT employee who may be an introvert, confined to his own space and doesn't want to mingle with people or more inclined towards reading books and stuff like that. You have a cross-section of the community. If a patient comes to the doctors with a little bit of mental depression because of the work pressure or anything else, we collect this data, put up a questionnaire to him about his personality traits, why he's feeling depressed, what are the factors affecting his mental attitude and those kinds of questions and we collect that kind of data. Depending on the data we draw a graph and we give a statistical analysis to the doctors, as well.

Then simultaneously, we give this feedback to the pharma companies, so that depending on the data, they determine which medicine they have and what medicine they could develop. That is the kind of work that we do.

I think that it's easy to use because of its variable inference. It has two variable ports and the variable experience is very nice because every variable has its own purpose. It has the ability to easily change any variable in our research.

Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things.

You can quickly build models because it does the work for you.

You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use.

Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful.

I like that the solution provides many built-in options for us. They have many existing algorithms that we can use. Using them effectively depends on us because we have to understand how to utilize any particular solution. But actually, the product is at a good level when it comes to ease-of-use. You have to have the data first to work with. You retrieve data you want to use from the database and you format the data and put it to the SPSS. Then you do analysis and from that, you can take better steps towards a business solution based on the results the analysis returns.  

The most valuable features are the small learning curve and its ability to hold a lot of data.

Custom tables and macros: They allow us to create useful reports quickly for a broad audience.

Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files.

The current features meet with the needs of our company. Our needs are not complex for the features offered.

• Merge, Aggregation, and Crosstabs functions are most valuable when I'm performing data preparation.
• Statistical modelling functions within this product, such as Regression and PCA function, are also useful. 
• The point-and-click interface is user-friendly.

The most valuable feature is the user interface because you don't need to write code. You can drag and drop selections and the menu is very helpful. You can adjust your parameters and other things, all through the interface.

The most valuable features are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial.

None are particularly valuable to me, because all its features are available in better condition elsewhere.