linear regression analysis


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linear regression analysis

a statistical method that aims to define the relationship between two variables, producing a value b, the regression coefficient. There are several assumptions that have to be made in carrying out the analysis, particularly
  1. that there is an independent variable x, e.g. time, which can be measured exactly, and also a dependent variable y, e.g. metabolic rate,
  2. that for every value of x there is a ‘true’ value of y . Linear regression analysis enables the fitting of a straight line to a scatter graph, using the equation y = a + bx, the a value being the point at which the regression line crosses the y -axis (the intercept).
References in periodicals archive ?
Regression tree analysis based on the algorithms is employable instead of multiple linear regression, ridge regression, use of factor analysis scores or principal component analysis scores in multiple linear regression analysis.
The next step of the study consisted of correlation and simple linear regression analysis of the data in order to identify the relationship between the dependent variable Y (physico-chemical characteristics of suspended particulate matter) and the independent variables X (physico-chemical characteristics of suspended particulate matter) [30].
Linear regression analysis was performed separately for FEV1, FVC and FEV1/FVC ratio in order to determine the association of percentage predicted lung volumes with respiratory symptoms.
In multiple linear regression analysis, the adjusted multiple regression certainty factor, adjusted R Square, [r.
Table 2] demonstrates the univariate linear regression analysis for the association between clinical variables and Ln_miRNA-145.
For example, the linear regression analysis [1] assumes that
Study on the determination of endogenous outputs and true digestibility of calcium and phosphorus with soybean meal for growing pigs by linear regression analysis technique.
The summary output, analysis of variance, parameter values and comparative four variable linear regression analysis for maximal axial drilling force and torque and tapping torque are presented in Tables 5 and 6.
Table 3 shows the weighted mean values obtained from linear regression analysis based on classification into daytime-weekdays, daytime-weekends, nighttime-weekdays, and nighttime-weekends by adding the classification into weekdays and weekends to the existing classification into daytime and nighttime.
Topics and discussions will cover the following: Conceptualization, Framing and Formulation of Research Problems and Objectives, Hypothesis and Conceptual/Theoretical Framework, Review of Related Literature, Data Management, Description of Data, Inference about Two Populations: Interval and Ratio Data; Inference about Two Populations: Two or More Populations: Ordinal Data, Inference about Two Populations: Two or More Populations: Nominal Data, Analysis of Variance (One-way and Two-way) and Linear Regression Analysis.