Linear regression was invented at the beginning of the 19th century and today, after more than 200 years, it is still used extensively in practical applications for description and prediction purposes ...

Duration: 12h. In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial ...

Exploring Insights/Inferences by performing EDA on the given project data (50_Startups and Toyota Corolla data) . Model fitting via linear regression by Importing sklearn package. Selecting the best ...

Specialization: Statistical Modeling for Data Science Applications Instructor: Brian Zaharatos, Director, Professional Master’s Degree in Applied Mathematics Prior knowledge needed: Basic calculus ...

Abstract: When we use linear programming in possibilistic regression analysis, some coefficients tend to become crisp because of the characteristic of linear programming. On the other hand, a ...

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear ...