Simple linear regression


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Simple linear regression

A regression analysis between only two variables, one dependent and the other explanatory.

Simple Linear Regression

In statistics, the analysis of variables that are dependent on only one other variable. Regression analysis uses regression equations, which shows the value of a dependent variable as a function of an independent variable. For example, a simple regression equation could take the form:

y = a + bx

where y is the dependent variable and x is the independent variable. In this case, the slope is equal to b and a is the intercept. When plotted on a graph, y is determined by the value of x. Regression equations are charted as a line and are important in calculating economic data and stock prices. See also: Multiple regression.
References in periodicals archive ?
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.
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.
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