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 ?
Table 7 also shows a summary of a simple linear regression for the effect of OJ on conscientiousness.
01 level (Table-1), (3) analysis of variance (ANOVA) of regression analysis (Table 2), (4) simple linear regression lines with R2 values (Fig.
961 Figure 1: Assumed Data Set: Simple Linear Regression Model A B C Monthly Total Vehicle Deliveries Expense 1 Month (XI) (Y) 2 1 5,882 $145,329 3 2 5,557 133,245 4 3 5,166 123,245 5 4 6,621 164,295 6 5 6,433 163,937 7 6 6,681 176,229 8 7 7,182 180,553 9 8 6,577 177,293 10 9 5,942 155,389 11 10 5,622 150,832 12 11 5,599 152,993 13 12 7,433 201,783 Figure 4: Output from Applying Regression Routine to Data SUMMARY OUTPUT Regression Statistics Multiple R 0.
True phosphorus digestibility and the endogenous phosphorus outputs associated with brown rice for weanling pigs measured by the simple linear regression analysis technique.
In simple linear regression analyses LAD was significantly associated with U-tAs, 35-70 ng/mL (1.
3] showed that there was a linear relationship between plant weight and fruit weight by using simple linear regression analysis.
25 in the simple linear regression models are shown in bold and were chosen for backward stepwise multiple linear regression analysis.
In a simple linear regression, GV was negatively associated with the number of days NPO without PN (p=0.
In this section we will define the simple linear regression in neutrosophic set.
The tool used to find the coefficients "m" and "b" is simple linear regression.
This variable (individual factors), in general was studied by simple linear regression, and following results were obtained: