Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...

Sankhyā: The Indian Journal of Statistics, Series B (1960-2002), Vol. 62, No. 3 (Dec., 2000), pp. 433-447 (15 pages) This paper discusses an econometric technique based on optimal control theory which ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

This course is compulsory on the BSc in Financial Mathematics and Statistics and BSc in Statistics with Finance. This course is available on the BSc in Actuarial Science, BSc in Business Mathematics ...

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 ...

Implementing LRR from scratch is harder than using a library like scikit-learn, but it helps you customize your code, makes it easier to integrate with other systems, and gives you a complete ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of-fit analyses. Census tracts. Fire ...

Major research questions in the field of social stratification and mobility deal with similarities and differences in the patterns of social mobility in space and time. Answers are typically given by ...