Categorical data plays a key role in data analysis, offering a structured way to capture qualitative relationships. Before running any models, simply examining the distribution of categorical data can provide valuable insights into underlying patterns.
In GAUSS 25, these functions received significant enhancements, making them more powerful and user-friendly. In this post, we’ll explore these improvements and demonstrate their practical applications.
Whether summarizing survey responses or exploring demographic trends, fundamental statistical tools, such as frequency counts and tabulations, help reveal these patterns.
In data analysis, a well-designed graph can help clarify your insights but a poorly annotated one can confuse and distract your audience. That’s why proper annotation, including legends, is essential to creating effective graphs.
Legends play a crucial role in making graphs more readable by distinguishing between different groups, categories, or data series. A well-placed legend helps ensure that your message comes across clearly.
In this blog, we’ll walk through how to add and customize legends in GAUSS graphics.
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.
