The first step in conducting a regression-based study is to specify a model. In real applications, this is usually the most challenging step - deciding which variables “belong” in the model and which ...

Discover the importance of homoskedasticity in regression models, where error variance is constant, and explore examples that illustrate this key concept.

Have you ever found yourself staring at a spreadsheet, trying to make sense of all those numbers? Many face the challenge of transforming raw data into actionable insights, especially when it comes to ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

When you perform regression analysis in Microsoft Excel, you are engaging in a statistical process that helps you understand the relationship between variables. This technique is particularly useful ...

"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count data. The most ...

The short course will illustrate how to use JMP in linear regression analysis. The three main topics will be: Exploratory data analysis, simple liner regression and polynomial regression How to fit a ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...