# linear regression

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Related to linear regression: Multiple linear regression
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 Noun 1 linear regression - the relation between variables when the regression equation is linear: e.g., y = ax + brectilinear regressionstatistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parametersregression toward the mean, simple regression, statistical regression, regression - the relation between selected values of x and observed values of y (from which the most probable value of y can be predicted for any value of x)regression coefficient - when the regression line is linear (y = ax + b) the regression coefficient is the constant (a) that represents the rate of change of one variable (y) as a function of changes in the other (x); it is the slope of the regression line
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References in periodicals archive ?
Hence, the current study was carried out to predict live body weight from more than a few morphological traits at each of three different periods through factor scores in multiple linear regression models.
The relationships between all parameters were determined using simple and multiple linear regression analysis using MINITABA(r) statistical software version 13.1.1.
There are studies that stipulate the normalizing of the values of pulmonary blood pressure once the clinic eutyroidism is obtained.3,4 Other studies reside in the evaluation of the relationship of correlation and of determination between different parameters and pulmonary hypertension by using the linear regression method.3,4,6,8
Robust multivariate linear regression was performed using the robust package (8).
Put the 11 features into multiple linear regression models.
Table 4: Testing of Multiple Linear Regression Model ANOVA
Linear regression analysis was employed to process data (Tables II, III) and to establish a linear regression equation for individual age: Y=69.137-621.200X (X=PV/TV), P < 0.001, R=0.544.
* Linear regression is limited to predicting numeric output.
To address the research question and achieve the objective of the study, correlation and statistical linear regression analysis was done to predict the values of response variables (dependent variables: physico-chemical characteristics of SPM) through explanatory variables (independent variable: physico-chemical characteristics of inert matter/material/waste) [30].
The linear regression equation describing the behavior of the expression of the lodging percentage of oat plants by the increment in the doses of the growth regulator was fitted.
In the analysis model of multiple linear regression, variables Y, [X.sub.1], [X.sub.2], ..., [X.sub.m] are quantitative, measured in interval and relative scales, or dichotomous, of m values.

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