Eric has been working to build, distribute, and strengthen the GAUSS universe since 2012. He is an economist skilled in data analysis and software development. He has earned a B.A. and MSc in economics and engineering and has over 18 years of combined industry and academic experience in data analysis and research.
If you’re an applied researcher, odds are (no pun intended) you’ve used hypothesis testing. Hypothesis testing is an essential part of practical applications, from validating economic models, to assessing policy impacts, to making informed business and financial decisions.
The usefulness of hypothesis is its ability to provide a structured framework for making objective decisions based on data rather than intuition or anecdotal evidence. It provides us a data-driven method to check the validity of our assumptions and models. The intuition is simple — by formulating null and alternative hypotheses, we can determine whether observed relationships between variables are statistically significant or simply due to chance.
In today’s blog we’ll look more closely at the statistical intuition of hypothesis testing using the Wald Test and provide a step-by-step guide for implementing hypothesis testing in GAUSS.
Panel data offers a unique opportunity to examine both individual-specific and time-specific effects. However, as anyone who has worked with panel data knows, these same features that make panel data so useful can also make exploration and cleaning particularly challenging.
GAUSS 25 was designed with these challenges in mind. It introduces a comprehensive new suite of panel data tools, tailored to make working with panel data in GAUSS easier, faster, and more intuitive.
In today’s blog, we’ll look at these new tools and demonstrate how they can simplify everyday panel data tasks, including:
GAUSS 25 will transform your workflow with intuitive tools for data exploration, advanced diagnostics, and seamless model comparison.
Learn more about our new features including:
In this video, you’ll learn the basics of panel data analysis in GAUSS. We demonstrate panel data modeling start to finish, from loading data to running a group specific intercept model.
In this video, you’ll learn the basics of choice data analysis in GAUSS. Our video demonstration shows just how quick and easy it is to get started with everything from data loading to discrete data modeling.
If you’ve worked with real-world data, you know that data cleaning and management can eat up your time. Efficiently tackling tedious data cleaning, organization, and management tasks can have a huge impact on productivity.
We created the GAUSS Data Management Guide with that exact goal in mind. It’s aimed to help you save time and make the most of your data.
Today’s blog looks at what the GAUSS Data Management Guide offers and how to best use the guide.
Data analysis in reality is rarely as clean and tidy as it is presented in the textbooks. Consider linear regression — data rarely meets the stringent assumptions required for OLS. Failing to recognize this and incorrectly implementing OLS can lead to embarrassing, inaccurate conclusions.
In today’s blog, we’ll look at how to use feasible generalized least squares to deal with data that does not meet the OLS assumption of Independent and Identically Distributed (IID) error terms.
Survey data is a powerful analysis tool, providing a window into people’s thoughts, behaviors, and experiences. By collecting responses from a diverse sample of responders on a range of topics, surveys offer invaluable insights. These can help researchers, businesses, and policymakers make informed decisions and understand diverse perspectives.
In today’s blog we’ll look more closely at survey data including:
Fundamental characteristics of survey data.
Data cleaning considerations.
Data exploration using frequency tables and data visualizations.
Anyone who works with panel data knows that pivoting between long and wide form, though commonly necessary, can still be painstakingly tedious, at best. It can lead to frustrating errors, unexpected results, and lengthy troubleshooting, at worst.
The new dfLonger and dfWider procedures introduced in GAUSS 24 make great strides towards fixing that. Extensive planning has gone into each procedure, resulting in comprehensive but intuitive functions.
In today’s blog, we will walk through all you need to know about the dfLonger procedure to tackle even the most complex cases of transforming wide form panel data to long form.
We’re happy to announce the release of GAUSS 24, with new features for everything from everyday data management to refined statistical modeling.
GAUSS 24 features a robust suite of tools designed to elevate your research. With these advancements, GAUSS 24 continues our commitment to helping you conduct insightful analysis and achieve your goals.
