In our analysis using

simple linear regression, we found that similar to prior studies, hypertension, hyperlipidemia, BP, and TG were positively correlated with CAC score and with risk for coronary as well as other cardiovascular events [18, 19].

Parameter

Simple linear regression Multiple linear Pearson's correlation regression P r [r.sup.2] P Age (years) -0.0195 0.0004 0.8796 >0.05 Sex/gender N/a N/a N/a >0.05 WBC (G/L) 0.2867 0.0822 0.0227 >0.05 CRP (mg/L) 0.1816 0.0330 0.1544 >0.05 PCT (ng/mL) -0.0992 0.0098 0.4393 >0.05 HBP (ng/mL) 0.2915 0.0850 0.0205 >0.05 PLT (G/L) 0.0609 0.0037 0.6354 >0.05 D-dimers (ng/mL) 0.4783 0.2287 0.00007 r = 0.4783; [r.sup.2] = 0.2287; p = 0.00007 Table 3: The sensitivities, specificities, positive predictive values (PPV), and negative predictive values (NPV) for D-dimers as a marker of spontaneous bacterial peritonitis.

Table 7 also shows a summary of a

simple linear regression for the effect of OJ on conscientiousness.

2), (2) two tailed Pearson correlations of physico-chemical characteristics of inert waste to the corresponding physico-chemical characteristics of suspended particulate matter at 0.01 level (Table-1), (3) analysis of variance (ANOVA) of regression analysis (Table 2), (4)

simple linear regression lines with R2 values (Fig.

Summary of correlation and

simple linear regression models are presented in Table 2.

Dashed lined = fitted

simple linear regression line.

Simple Linear Regression. In the simplest approach, the AOD recorded on day d (i.e., the current day) is used as the independent variable in a

simple linear regression model.

The

simple linear regression model between sucrose content (Suc) and the apparent purity (Ap), polarization (Pol) and brix (Bx) is as follows:

The relationship between the parameters, with a positive correlation between the F-PEF, S-PEF, and the FEV1, has been studied in

simple linear regression. The parameters that have a significant correlation in

simple linear regression were analyzed in multiple linear regression to retain the influential parameters for F-PEF, S-PEF, and FEV1 in a statistically significant way.

The relationship between mean monthly temperatures and mean monthly 2-h glucose concentrations was evaluated by

simple linear regression.

In each model, a

simple linear regression ([[??].sub.i] = [[beta].sub.0] + [[beta].sub.1][X.sub.i]) of the leaf area estimated by the model (dependent variable) as a function of the observed leaf area (independent variable) was initially adjusted.

Option #4: Use the FORECAST function to generate cost estimates based on a

simple linear regression model.