Data Analysis & Simulation

Archive for the ‘Monte Carlo Simulation’ 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.

EasyFit Used to Improve the Forecasting of Software Project Status

Monday, November 29th, 2010

The software development community struggles with a way to identify if their projects are on-schedule given the inherent risks of constant invention that inevitably has elements of uncertainty and risk. Current practice is for developers to estimate a software project, and attempt to consider (up-front) all variations to get a viable estimate of time and cost. This process is laborious, and even with due rigor, project slip when the realization that estimates versus actual times fail to match. This leads to costly project overruns and lack of trust in future estimates.

As part of the Agile movement for software development, we think there is a better way and are championing the use of Monte-Carlo simulation as a ways of assessing likely progress and dealing with delays as early as possible… read the full case study

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