The researcher will have questions about his model similar to a simple linear regression model. X 3 = amount of understory herbaceous matterĪ researcher would collect data on these variables and use the sample data to construct a regression equation relating these three variables to the response. For example, scatterplots, correlation, and least squares method are still essential components for a multiple regression.įor example, a habitat suitability index (used to evaluate the impact on wildlife habitat from land use changes) for ruffed grouse might be related to three factors: Multiple linear regression is an extension of simple linear regression and many of the ideas we examined in simple linear regression carry over to the multiple regression setting. Regressions based on more than one independent variable are called multiple regressions. If this relationship can be estimated, it may enable us to make more precise predictions of the dependent variable than would be possible by a simple linear regression.
It frequently happens that a dependent variable ( y) in which we are interested is related to more than one independent variable.
0 kommentar(er)
