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.
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.
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.
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.
The new GAUSS Machine Learning (GML) library offers powerful and efficient machine learning techniques in an accessible and friendly environment. Whether you’re just getting familiar with machine learning or an experienced technician, you’ll be running models in no time with GML.
Logistic regression has been a long-standing popular tool for modeling categorical outcomes. It’s widely used across fields like epidemiology, finance, and econometrics.
In today’s blog we’ll look at the fundamentals of logistic regression. We’ll use a real-world survey data application and provide a step-by-step guide to implementing your own regularized logistic regression models using the GAUSS Machine Learning library, including:
If you’ve ever done empirical work, you know that real-world data rarely, if ever, arrives clean and ready for modeling. No data analysis project consists solely of fitting a model and making predictions.
In today’s blog, we walk through a machine learning project from start to finish. We’ll give you a foundation for completing your own machine learning project in GAUSS, working through:
