linear regression

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Related to multiple regression: Multiple linear regression

linear regression

[′lin·ē·ər ri′gresh·ən]
(statistics)
The straight line running among the points of a scatter diagram about which the amount of scatter is smallest, as defined, for example, by the least squares method.
References in periodicals archive ?
Table-4: PFT prediction equations using simple and multiple regression.
Descriptive Statistics and Multiple Regression Results Descriptive Statistics M SD I usually do well in science 2.
Figure 4 presents rows 1-20 of the multiple regression results for monthly supplies expense (Y) with sales (in units) per month ([X.
1, then the predictor is used in the multiple regression.
Key words: Infant mortality, punjab, contour plot, spatial autocorrelation, moran's I, geary c, multiple regression model, conditional autoregressive model.
The present research adopted general approach of using simple and multiple regression analysis against the backdrop of Baron and Kenny (1986) assumptions of mediation analysis.
In this research for the zoning landslide in the study area, in addition to modeling using the multiple regression.
Shieh examined the accuracy of Equation 1 for 27 additional conditions and concluded that Equation 1 is "not recommended for precise interval estimation of squared multiple correlation coefficient in multiple regression analysis" (p.
Multiple regression models are used to estimate the rainfall amount for monsoon season for four cities of Sindh Sukkur Larkana Hyderabad and Karachi using previous data of 25 years (1987-2011).
As a result, the present results confirmed that instead of multiple and stepwise regression, principal use of factor and principal component scores in multiple regression analysis might offer a good opportunity without multicollinearity problem for predicting body weight of indigenous goat.
The first part of the study conducted during fall 2005 to spring 2007 semesters by D'Souza and Maheshwari (2009) developed a multiple regression model that included four independent variables as a predictor of student performance.

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