Subjects were grouped according to sex, and analyzed using

linear regression analysis, deriving the inferred function of male age: Y=64.

therefore,

linear regression analysis was applied to test the hypotheses.

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.