In this study, high R2 values derived from linear regression analyses are shown to increase the likelihood of detecting significant curvature in a relation. While this curvature is unlikely to hinder the use of LSLR in predicting y from x, it is demonstrated that large variations in the slope...
regression coefficient- when the regression line is linear (y = ax + b) the regression coefficient is the constant (a) that represents the rate of change of one variable (y) as a function of changes in the other (x); it is the slope of the regression line ...
- 《Bmc Medical Research Methodology》 被引量: 9发表: 2017年 Machine Learning, vol.75, no.3, pp.249–274, 2009. 1 Pool-based Active Learning in Approximate Linear Regression The goal of pool-based active learning is to choose the best input points to gather output values from a 'pool ...
Multiple linear regression (MLR), principal component analysis (PCA), and GEP were used to determine the best method for predicting the FD. Geological setting The study area was the Mezősas field, located in the northern rim of the Békés Basin (Fig. 1), which is the largest and ...
The first aim of the research described in this paper was to assess in detail the implications for effect estimates (regression coefficients), and their precision (characterised by standard errors (SEs)), when a linear regression analysis exploring the relation of a continuous outcome variable to ...
2023, The Impact of AI Innovation on Financial Sectors in the Era of Industry 5.0 Identifying Spatiotemporal Clusters by Means of Agglomerative Hierarchical Clustering and Bayesian Regression Analysis with Spatiotemporally Varying Coefficients: Methodology and Application to Dengue Disease in Bandung, Indones...
The aim of this study is to propose Functional Linear Regression Models (FLRMs) to analyze the turbidity in the coastal zone of Guadalquivir estuary from satellite data. With this aim different types of FLRMs for scalar response have been used to predict the amount of Total Suspended Solids (...
We proposed an iterative methodology to handle this problem. The idea was, at each step, to test statistically whether the following point belonged to the same regression line. The methodology was then used to evaluate quantitatively the effect on linear range of a shift in detection wavelength ...
Developments in linear regression methodology 1959e 1982. Technometrics 25, 219e230.Hocking,R.Developments in linear regression methodology: 1959–1982. Technometrics . 1983HOCKING, R. R. (1983), “Developments in Linear Regression Methodology; 1959–1982,” Technometrics, 25 , 219–230 (with ...
The algorithm makes use of the iterative weighted least-squares method commonly used for maximum likelihood calculations, so that implementation is possible in standard regression software, such as GLIM. Comparison is made with a more ‘direct’ approach using Newton’s method, found to be easily ...