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 ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

"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 ...

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 ...

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 ...

Autoregressive models predict future values using past data patterns. Discover how these models work and their application in ...

Jim Chappelow is an independent consulting economist with over 13 years of experience in economic development, research, teaching, forecasting, and consulting. David Kindness is a Certified Public ...

When you use the statistical analysis features in Excel, you are leveraging one of the most powerful tools available for data manipulation and interpretation. Excel is not just a spreadsheet ...