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26 lip 2021 · The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, ...
Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed.
Regression models can be used to make predictions of kinetic data of a series of chemically similar compounds. The molecular structure of a chemical compound determines its properties. A set of numerical descriptors are calculated to encode information about each of the molecular structures.
3 mar 2018 · The utilization of physical organic molecular descriptors for the quantitative description of reaction outcomes in multivariate linear regression models is demonstrated as an effective tool for a priori prediction and mechanistic interrogation.
The structural equation model (SEM) generalizes the linear regression model to include multiple dependent variables, reciprocal effects, indirect effects, and the estimation and removal of measurement error through the inclusion of latent variables.
10.8 Sample Partial Correlations 266 11 Multiple Regression: Bayesian Inference 277 11.1 Elements of Bayesian Statistical Inference 277 11.2 A Bayesian Multiple Linear Regression Model 279 11.2.1 A Bayesian Multiple Regression Model with a Conjugate Prior 280 11.2.2 Marginal Posterior Density of b 282 11.2.3 Marginal Posterior Densities of tand ...
10 sty 2024 · To address this challenge, we introduce a quantum-mechanical (QM) descriptor dataset, called QMex, and an interactive linear regression (ILR), which incorporates interaction terms between QM...
