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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.


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2, the third edition adds chapters on simple linear regression, tire wear, computerized delivery of cognitive behavioral therapy, and treating toenail infection.
Simply put, in logistic regression, rather than analyzing the relationship between x and y using simple linear regression, as we did when we analyzed the predictive relationship between travel distance and ambulance response time, we analyze, using regression, the predictive relationship between x and the logit(y).
Using simple linear regression analysis, we formally explored whether a relation between country-specific antimicrobial drug use and antimicrobial resistance can be inferred at national aggregation levels and found that the association between antimicrobial drug use and resistance was specific and robust for 2 of the 3 compound pathogen combinations under study, stable over time, but not sensitive enough to explain all of the observed variation.
 
 
 
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