# Data Analysis & Simulation

## Archive for the ‘Simulation & Probabilistic Analysis SDK’ Category

### Using Probability Distributions in Excel VBA

Monday, August 27th, 2012

Some time ago, we covered the use of probability distributions and related Excel worksheet functions available in EasyFitXL. When dealing with probability data in Excel, most of the time, you would use those functions to set up your calculations to be performed directly within your workbooks. This approach works well for applications where you need to perform typical probability analysis based on different input data: you modify the data, and Excel recalculates the entire worksheet and updates the associated results.

However, for more advanced applications, you might need to implement some complex logic requiring the use of IF statements, which will make your worksheets too complicated. Of course, you can still use the IF worksheet function, but in reality, you would want to keep your workbooks as simple as possible, which is a good idea if you want to easily get back to your analysis in a month. And that is where the built-in Visual Basic for Application programming language comes in handy: with little programming knowledge, using the VBA functions available in EasyFitXL as well as in the EasyFit SDK, you can create feature-rich probability analysis and Monte Carlo simulation applications implementing the logic of any degree of complexity.

Even though both EasyFitXL and the SDK include a variety of VBA functions, these software packages differ in the feature sets they offer. Initially, EasyFitXL was designed as an Excel add-in that brings the visual distribution fitting feature of EasyFit to Excel. Of course, we could not ignore the integration and data analysis automation capabilities of Excel, so we came up with the following ideology for EasyFitXL: visually fit distributions to data in Excel, and use the results in the most convenient way – either visually, in your worksheets, or in your VBA applications. That is why the VBA functions offered by EasyFitXL allow you to evaluate most common distribution functions (PDF, CDF etc.), calculate distribution statistics (mean, variance…), and generate random numbers from any probability distribution you choose as the model for your data.

On the other hand, the Simulation & Probabilistic Analysis SDK was designed from the ground up as the package targeting software developers and offering a complete range of functions covering the entire feature set of EasyFit. Apart from evaluating distribution functions, calculating statistics and generating random numbers, you can do distribution fitting, perform goodness of fit tests, and even create distribution graphs – all directly from your VBA applications.

Another huge difference is that technically, the SDK offers its functionality through a set of Objects, enabling you to use the object-oriented approach to software development, making your work with large projects more efficient. On the contrary, EasyFitXL employs the functional programming model, offering a separate VBA function for each kind of distribution function and each probability distribution, which is good for short and simple programs.

Overall, depending on your needs, you can use either EasyFitXL or the SDK to implement any kind of data analysis application, ranging from simple probability calculation programs to complex automated data analysis and Monte Carlo simulation systems.

### Simulation & Probabilistic Analysis SDK 1.2 Released

Monday, September 5th, 2011

Recently we have released a new version of our SDK. In this update, we have added a new property that lets you obtain the current licensing status of the SDK – for instance, you can determine whether the SDK is currently running in trial mode (using the Evaluation License), and if so, how many days are left until the evaluation period expires.

Consider the following scenario: you are building an application with a modular structure that, apart from its core feature set, provides some additional functionality through a number of modules, or add-ins, which can be installed and enabled on an optional basis. Now, suppose one of these modules uses the simulation or distribution fitting features of the SDK, and you want to give your users an ability to evaluate it prior to making a purchase decision. The new version of the SDK lets you easily integrate this logic into your applications, allowing you to create more flexible solutions that better meet your customers’ needs.

### Simulation & Probabilistic Analysis SDK Released

Thursday, December 3rd, 2009

The beta testing of our new product, the Simulation & Probabilistic Analysis SDK, is now over, and we want to thank our beta testers for their effort and valuable feedback. One of the most exciting things is that during the beta testing phase, we have not detected any bugs in the SDK, indicating the initial high quality of the product.

The production version of the SDK is now available for public download, so if you are a software developer and need to add distribution fitting or simulation features to your software with no hassle, feel free to download the fully functional version of the SDK and try it free for 30 days.

Another good news is that the Christmas is coming, so we decided to make a gift to software developers who are considering to purchase the SDK: until the end of December, the SDK Developer License can be ordered at a \$500 discount – click here details.

### Simulation & Probabilistic Analysis SDK Available for Public Beta Testing

Monday, October 12th, 2009

The new Software Development Kit enabling you to easily add Monte Carlo simulation and distribution analysis features to your applications is now available for public beta testing – please download the free beta version and take a look at the code examples (available in several languages, including C#, VB.NET, C++, and Visual Basic for Applications).

We would be glad to receive any feedback, questions or suggestions from you, so please feel free to drop us a line and let us know what you think about this product and how we can make it better.

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