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About 10,000 papers for “multiple linear regression”

Multiple Linear Regression

Benjamin E. Schlegel · Springer texts in political science and international relations · 2026

Cited by 0

Multiple linear regression.

Jorge Montero-Cámara (18516069), Francisco José Ferrer-Sargues (9915056), David Cuesta Peredo (18516078) et al. · Figshare · 2025

<div> Introduction Cardiovascular diseases may be amenable to surgical intervention. To mitigate post-surgical complications, diverse strategies are employed, including pre-habilitation programs. This study examines the effect of an unsupervised pre-surgical respiratory physiotherapy program on both sexes in terms of hospital and intensive care unit (ICU) stay lengths, as well as the incidence of post-surgical complications, their severity, and mortality risk. Methodology Retrospective observational study of 418 adults who underwent open-heart surgery between 2018 and 2022. The subjects were divided into two sex-based groups based on attendance at individual pre-surgical physiotherapy sessions. A Mann-Whitney U test was employed to evaluate the impact of the pre-surgical respiratory physiotherapy program and a Kruskal-Wallis rank-sum test to assess its influence on both sexes. Additionally, a multiple linear regression analysis was conducted to evaluate the impact of various variables on overall length of stay. Results The mean age of women was higher (p = 0.002), and they exhibited longer mean lengths of ICU (p = 0.004) and hospital stays (p = 0.031). In both sexes, a statistically significant reduction in LOS was observed among those undergoing a respiratory physiotherapy program. The linear regression analysis indicated that male sex was associated with a reduction in hospital and ICU stay lengths (p < 0.001). Although women experienced a higher number of complications (p = 0.043), no differences in severity levels or mortality risk were observed between sexes. Conclusion An unsupervised home pre-surgical physiotherapy program based on ventilatory exercises can reduce hospital and ICU stay for both men and women. Notwithstanding the higher incidence of complications in women, no differences in severity or mortality risk were observed between sexes. </div>

Cited by 0Open access

Multiple linear regression.

Frederik Ricken (13924646), Ahu Damla Can (13924649), Steffen Gräber (8681373) et al. · Figshare · 2022

<p>Predictors for the CSF fetuin-A/serum fetuin-A quotient.</p> <p>(PDF)</p>

Cited by 0Open access

Multiple Linear Regression

Peter C. Bruce, Peter Gedeck, Janet Dobbins · 2024

This chapter helps the data science instructors to fit a multiple linear regression model and to test the statistical significance of coefficients. Multiple linear regression models work best when certain assumptions about the data are met. These assumptions, as well as methods for checking them, are listed. The standard regression evaluation metrics computed in most statistical software, including statsmodels are for the data that the model was fit with. Typically, coefficients in regressions are reported along with confidence intervals. The chapter helps the data science instructors to distinguish traditional explanatory purposes from predictive modeling purposes, and identify the model performance metrics appropriate in each. Standard regression metrics typically apply to the data used to fit the model. The chapter also includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves.

Cited by 4Open access

Multiple Linear Regression

John H. Maindonald, W. John Braun, Jeffrey L. Andrews · Cambridge University Press eBooks · 2024

Multiple linear regression generalizes straight line regression to allow multiple explanatory (or predictor) variables, in this chapter under the normal errors assumption. The focus may be on accurate prediction. Or it may, alternatively or additionally, be on the regression coefficients themselves. Simplistic interpretations of coefficients can be grossly misleading. Later chapters elaborate on the ideas and methods developed in this chapter, applying them in new contexts. The attaching of causal interpretations to model coefficients must be justified both by reference to subject area knowledge and by careful checks to ensure that they are not artefacts of the correlation structure. There is attention to regression diagnostics, to assessment, and comparison of models. Variable selection strategies can readily over-fit. Hence the importance of training/test approaches and cross-validation. The potential is demonstrated for errors in x to seriously bias regression coefficients. Strong multicollinearity leads to large variance inflation factors.

Cited by 0

Multiple Linear Regression

Ronald D. Yockey · 2023

Cited by 1

Multiple Linear Regression

Cheng-Few Lee, Hongyi Chen, John Lee · 2019

Cited by 2

Multiple Linear Regression

Ronald L. Moy, Li-Shya Chen, Lie Jane Kao · 2014

Cited by 7

Multiple Linear Regression

2014

The purpose of a multiple linear regression is to estimate the dependence of the dependent variable on more than two explanatory variables. The same method for estimating the parameters in a simple linear regression model is used in estimating the parameters for a multiple linear regression. When estimating a multiple linear regression there are three basic assumptions regarding the error terms that are made. Violations of any of these assumptions may require that more sophisticated regression models be used. In this chapter, we describe the multiple linear regression model and then focus on diagnosing the quality of a regression model. Applications in finance that are illustrated in this chapter are the measurement of the interest-rate sensitivity of a financial asset, predicting the 10-year U.S. Treasury yield, benchmark selection in asset management, return-based style analysis, rich/cheap analysis, and using the multiple linear regression model to test for market efficiency and the capital asset pricing model.

Cited by 4

Multiple Linear Regression

The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation · 2018

Cited by 4

Multiple Linear Regression

2006

Cited by 0

Multiple Linear Regression

Samprit Chatterjee, Ali S. Hadi · Wiley series in probability and statistics · 2006

This chapter contains sections titled: Introduction Description of the Data and Model Example: Supervisor Performance Data Parameter Estimation Interpretations of Regression Coefficients Properties of the Least Squares Estimators Multiple Correlation Coefficient Inference for Individual Regression Coefficients Tests of Hypotheses in a Linear Model Predictions Summary Exercises Appendix: Multiple Regression in Matrix Notation

Cited by 9

Multiple Linear Regression.

George Humfeld · Defense Technical Information Center (DTIC) · 1978

Multiple linear regression theory provides an estimated covariance structure for the estimates of the parameters of the linear function based on given data. However, when the deviation form is used to calculate these parameter estimates, the portions of this matrix which involve the constant term are generally missing. This report presents equations which can be used to calculate these missing covariances from quantities which are generally available. Most standard regression references discuss calculation of confidence limits for point estimates of the dependent variable when these point estimates are calculated from the regression equation. This report presents equations for similar limits for the independent variables, again from quantities generally available when a deviation-form routine is used. A different interpretation is suggested for these limits than is seen in the references. A numerical example is provided. (Author)

Cited by 0

Multiple linear regression.

Stephen Gorard · Durham Research Online (Durham University) · 2017

This chapter provides a brief and basic introduction to regression techniques and to the use of several variables in one consolidated analysis, focusing here on multiple linear regression. There are, of course, many other kinds of regression and even more ways of conducting multiple variable analysis, and some of these are referenced later in the chapter. However, most of the advice given in this chapter, about interpretation and judgement for example, would apply equally to all of these other techniques of analysis as well. For the benefit of some readers the chapter starts with a reprise of the ideas of correlation and two-variable regression (see examples in Chapter 16). The next section describes a basic multiple linear regression, and ensuing sections look at the interpretation of results and some of the potential dangers of being misled.

Cited by 0

Multiple Linear Regression

2011

Cited by 5

Multiple Linear Regression

Barry J. Everitt, Sophia Rabe‐Hesketh · Statistics in the health sciences · 2001

Cited by 3

Multiple Linear Regression

2012

This chapter describes the basic structure of the linear regression model, including the assumptions that justify the use of least squares estimation. A regression analysis is used for one (or more) of three purposes: modeling the relationship between x and y; prediction of the target variable (forecasting); and testing of hypotheses. The chapter introduces the basic multiple linear regression model, and discusses how this model can be used for these three purposes. Specifically, it discusses the interpretations of the estimates of different regression parameters, the assumptions underlying the model, measures of the strength of the relationship between the target and predictor variables, the construction of tests of hypotheses and intervals related to regression parameters, and the checking of assumptions using diagnostic plots. Controlled Vocabulary Terms confidence interval; hypothesis testing; multiple regression; partial residual plot

Cited by 7

Cited by 2Open access