![]() ![]() Basic ideas of linear regression: The two-variable model. In other words, the relationship between the two variables only goes one way). That is, they measure different things-see the section in this chapter on Multiple Linear Regression for additional details), (4) homoscedasticity (the error term is the same across all values of the independent variable), and (5), exogeneity, which is only necessary when regression is being used for causal inference (independent variables are not dependent on the dependent variable-that is, in this example, instructional expenditures are not dependent on international student enrollment. 1 for more information about sampling strategies and why randomization is important), (3) non-collinearity (when we use more than one independent variable to predict a dependent variable, these two independent variables are not perfectly correlated with one another. In more technical terms, we assume that the relationship depicted in the regression line is linear in its parameters), (2) random sampling, meaning especially that units in the dataset are not related to one another in some way (e.g., students who study abroad in the same program might be related to each other in ways not captured in your dataset), that is, there is no autocorrelation among units (see Chap. These assumptions include: (1) correct functional form (we assume that the relationship between the two variables is linear in some way-but note that this line does not have to be straight. ![]()
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