Results 261 to 270 of about 1,688,491 (311)

Linear regression

Wiley Interdisciplinary Reviews: Computational Statistics, 2012
Xiaogang Su, Xin Yan, Chih-Ling Tsai
exaly   +2 more sources

(Non) linear regression modeling [PDF]

, 2004
We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or predict the dependent variables by means of the model. Such
Čížek, Pavel
openaire   +5 more sources

Grouping and linear regression

Journal of Chronic Diseases, 1982
With a large number of observations, the method of grouping is often employed to provide simpler graphs or tables. When one investigates the relationship between two variables, one usually groups based on the magnitude of the independent variable, and then plots the dependent variable averages against independent variable averages to get a clearer ...
K K, Lan, M, Halperin, G T, Waldman
openaire   +2 more sources

Regression: simple linear

International Journal of Injury Control and Safety Promotion, 2018
Regression is a statistical term used for describing models that estimate the relationships among variables.
openaire   +2 more sources

Linearized Ridge Regression Estimator in Linear Regression

Communications in Statistics - Theory and Methods, 2011
In this article, we aim to study the linearized ridge regression (LRR) estimator in a linear regression model motivated by the work of Liu (1993). The LRR estimator and the two types of generalized Liu estimators are investigated under the PRESS criterion.
Xu-Qing Liu, Feng Gao
openaire   +1 more source

Linear Regression and Logistic Regression

2023
Supervised learning is a machine learning task of mapping the input to the output on the basis of labeled input-output example pairs. Supervised learning may be of two types: classification and regression. In this chapter, we will discuss linear regression in one variable, linear regression in multiple variables, gradient descent, and polynomial ...
Deepti Chopra, Roopal Khurana
openaire   +1 more source

Multiple Linear Regression

2007
This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit.
openaire   +2 more sources

Regression: multiple linear

International Journal of Injury Control and Safety Promotion, 2018
Simple linear regression models study the relationship between a single continuous dependent variable Y and one independent variable X (Bangdiwala, 2018).
openaire   +2 more sources