STATISTICA Blogs

Now that I'm officially 39 years old...again...I've been around long enough to hear more than a few phrases become popular and then disappear after a few months or years. Most of them have been used in popular culture, but there have also been terms used professionally as well that are no longer groovy. One such popular term that I've been hearing a lot lately is Big Data. It seems that the term Big Data may mean different things to different people, but will we be talking about a new popular term in a few more years?

You have put in a lot of hard work generating a scorecard model.  Just imagine, you have looked through possibly hundreds of predictor variables and selected those that were most important to your model.  You’ve discretized them, looking at weight of evidence to verify that they have been properly prepared for use in developing your model.  All of your discretization scripts have been used to prepare your predictors for use in building a logistic regression model which is in turn used to create your scorecard.  Now that you have your model, how do you determine if your model performs as you expect?

There is a host of statistics and graphs that you can use to help you determine if your model is performing at the level you expect.  There is the Kolmogorov-Smirnov statistic which is a measure of how much...

One of my focuses at StatSoft has been Scorecard, STATISTICA's dedicated module for developing, evaluating and monitoring scorecard models. Being the statistical minded individual that I am, I tend to gravitate to the complexities of the module and how each individual component of Scorecard works.  As I reflect on my past year here at StatSoft, I find myself wanting to look at the bigger picture of Scorecard to see what the benefits are of STATISTICA as a credit scoring tool.

The credit industry is under intense scrutiny in the media due to the mortgage lending crisis and accompanying economic downturn that has been experienced within the United States and also globally.  This has made companies large and small, as well as individuals, pay more attention to how decisions...

If you are new to analyzing large data sets, specifically those with many variables, you may have never heard of the Curse of Dimensionality. I know the first time I heard the term, I was wondering how they were going to bring this low-budget horror film topic into a discussion on predictive analytics. I now have a better understanding of the Curse of Dimensionality and the difficulties that arise when trying to analyze data that has hundreds if not thousands of predictors. While it may feel like you are in a horror film when dealing with some of these data sets, it doesn't have to be a film with a bad ending. A better understanding about the term is often the first step in working with this type of data. Hopefully this blog post will help give you a bit of insight into the Curse of Dimensionality.

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Have you ever wondered how banks make their lending decisions? Working with the Scorecard module in STATISTICA has given me quite a bit of insight into the decision-making process regarding lending in some banks. One new feature coming out in the next version of STATISTICA is Calibration Tests. This tool makes it possible for banks to test whether or not the forecast probability of default (PD) has been the PD that has actually occurred. If it has, then all is well and good. However, if the forecast PD and the realized PD are different to a large degree, a banker somewhere may need to reevaluate their lending model.

I've been at StatSoft since March, less than six months. I am more in awe of the solutions we offer and the people who create those solutions, my colleagues, on a daily basis. After more than ten years in school, if all goes as planned, I will graduate with a Ph.D. in the Research, Evaluation, Measurement and Statistics program in Educational Psychology at Oklahoma State University this Fall.  Coming out of that program led me to believe that I'd be swimming pretty well alongside all the other statistical athletes