Results 151 to 160 of about 1,566,040 (193)

Blocked Arteries and Multivariate Regression

Biometrics, 1992
Ultrasound blood flow waveforms may be used in the diagnosis of arterial occlusive disease in human legs. We develop a statistical model to predict disease severity, conditional on the ultrasound data and some training data. It belongs to the class of models known as seemingly unrelated regressions, for which the Bayesian predictive density function ...
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Multivariate Nonlinear Regression

2007
This chapter continues the previous chapter but now we can have two or more independent (predictor, explanatory) variables, the \(\overline{X}_i\). Another change from Chapter 12 is now we will work with fuzzy trapezoidal (shaped) fuzzy numbers instead of fuzzy triangular (shaped) fuzzy numbers.
James J. Buckley, Leonard J. Jowers
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Multivariate Linear Regression

2010
In this chapter we consider the multivariate multiple regression problem. The tests and estimates are again based on identity, spatial sign, and spatial rank scores. The estimates obtained in this way are then the regular LS estimate, the LAD estimate based on the mean deviation of the residuals (from the origin) and the estimate based on mean ...
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Multivariable Linear Regression

2015
INEAR REGRESSION is probably one of the most powerful and useful tools available to the applied statistician. This method uses one or more variables to explain the values of another. Statistics alone cannot prove a cause and effect relationship, but we can show how changes in one set of measurements are associated with changes of the average values in ...
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Multivariate Linear Regression

1998
Regression methods are perhaps the most widely used statistical tools in data analysis. When several response variables are studied simultaneously, we are in the sphere of multivariate regression. The usual description of the multivariate regression model, that relates the set of m multiple responses to a set of n predictor variables, assumes ...
Gregory C. Reinsel, Raja P. Velu
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Multivariate Regression Analysis

2018
This final chapter provides an introduction into multivariate regression modeling. We will cover the logic behind multiple regression modeling and explain the interpretation of a multivariate regression model. We will further cover the assumptions this type of model is based upon. Finally, and using our data, we will provide concrete examples on how to
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
exaly  

Multivariate Regression

2013
David Weisburd, Chester Britt
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Multivariable Linear Regression

2001
Abstract To examine the influence of maternal weight gained during pregnancy on the birth weight of a newborn infant while controlling for a variety of possibly important confounding factors. DATA The Department of Obstetrics, Gynecology, and Reproductive Science Peri­ natal Database maintained at the University of California, San ...
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Multivariate Regression

2021
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