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 simple linear regression analyses LAD was significantly associated with U-tAs, 35-70 ng/mL (1.
This variable (organizational factors), in general was studied by simple linear regression, and following results were obtained:
What is coming below is accordingly the results of the simple linear regression test and the findings of power analysis.
This study establishes the relationship between GDP and FDI inflows, exports and FDI inflows, and imports and FDI inflows in India using Karl Pearson's Coefficient of Correlation and Simple Linear Regression.
This paper presents an activity that was used to introduce concepts related to the simple linear regression model using data from the National Basketball Association (NBA).
We will use this inexpensive hand-held calculator to demonstrate the use of simple linear regression in deriving market-based adjustments.
For simple linear regression, the confidence interval for an individual prediction is given by formula 1 (Neter, Wasserman and Kutner, 1990).
Review of research, revealed the widespread use of simple linear regression (Williams and Gregoire 1993; Williams and Schreuder 1996; Marshall et al.
Predictions of inventory investment by use of forecasting (i=1) and inventory control (i=2) as independent variables individually are defined in simple linear regression equation (1).
For example the CLSI (formerly NCCLS) guideline EP9-A2 Method Comparison and Bias Estimation Using Patient Samples (17) recommends the use of simple linear regression estimation if the correlation coefficient is >0.
Most simple linear regression (SLR) models assume the energy use as a function of outdoor temperature only, and can be used effectively at monthly or daily time scales, and thus do not require the use of a separate variable for occupancy.