Linear regression

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Linear regression

A statistical technique for fitting a straight line to a set of data points.
Copyright © 2012, Campbell R. Harvey. All Rights Reserved.

Linear Regression

A statistical technique in which one takes a set of data points and plots them on a line. Linear regression is used to determine trends in economic data. For example, one may take different figures of GDP growth over time and plot them on a line in order to determine whether the general trend is upward or downward.
Farlex Financial Dictionary. © 2012 Farlex, Inc. All Rights Reserved
References in periodicals archive ?
Use of factor analysis scores in multiple regression model for estimation of body weight from some body measurement in Muscovy duck.
The regression model based on SPICE simulations has the following advantages.
The models include three typical single to multiple regression models, two proposed regression models, and an artificial neural network model with recommended classifications.
Estimates of (co)variance function for growth to yearling in Horro sheep of Ethiopia using random regression model. Arch.
Based on the changes in abdominal measurement, a regression model was developed for estimating weight of pregnant mares from the fifth month of gestation until parturition.
In this paper, the performance of the recently introduced stochastic restricted estimators, namely, the Stochastic Restricted Ridge Estimator (SRRE) proposed by Li and Yang [14], Stochastic Restricted Almost Unbiased Ridge Estimator (SRAURE), and Stochastic Restricted Almost Unbiased Liu Estimator (SRAULE) proposedbyWu and Yang [15], Stochastic Restricted Principal Component Regression Estimator (SRPCRE) proposed by He and Wu [16], Stochastic Restricted r-k (SRrk) class estimator, and Stochastic Restricted r-d (SRrd) class estimator proposed by Wu [17], was examined in the misspecified regression model when multicollinearity exists among explanatory variables.
According to the results of the model for the repaired curve segments (Table 2), like the previous regression model, [G.sub.t-1] and [T.sub.S] are significant at a 95 percent confidence level (p value is less than 0.05) to estimate the current gauge.
To verify the rationality of the final regression model, residual test is an indispensable step.
Furthermore, the regression model tests of GRDP at CMV, GRDP at CP and population (life) to per capita income with F distribution.
The idea of modeling the expected value of Be (p, q) distribution was already under discussion for some time in the works of Jorgensen (1997), Paolino (2001) and Kieschnick and McCullough (2003), e.g., however, the regression model exposed by Ferrari and Cribari-Neto (2004) became popular for formulating more carefully the modeling of the expected value in Be (p, q) distribution, based on Be ([mu], [sigma]) distribution parameterization, and to establish an association with GLM theory, a class well described in the literature by Nelder and Wedderburn (1972).