Fuzzy regression models extend traditional statistical regression by integrating fuzzy set theory to better handle imprecision and uncertainty inherent in many real-world data sets. These models ...

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 ...

Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile ...

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 ...

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 ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

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 ...

The study by researchers at Benha University shows that cost overruns in Egyptian road projects are mainly caused by unclear ...