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Review the Concepts 253

3. The coefficients in multiple regression can be estimated by OLS. When the
four least squares assumptions in Key Concept 6.4 are satisfied, the OLS esti-
mators are unbiased, consistent, and normally distributed in large samples.

4. Perfect multicollinearity, which occurs when one regressor is an exact linear
function of the other regressors, usually arises from a mistake in choosing
which regressors to include in a multiple regression. Solving perfect multi-
collinearity requires changing the set of regressors.

5. The standard error of the regression, the R2, and the R 2 are measures of fit
for the multiple regression model.

Key Terms

omitted variable bias (229) constant regressor (237)
constant term (237)
multiple regression model (235) homoskedastic (237)
heteroskedastic (237)
population regression line (235) ordinary least squares (OLS)

population regression function (235) estimators of b0, b1, c, bk (239)
OLS regression line (239)
intercept (235) predicted value (239)
OLS residual (239)
slope coefficient of X1i (235) R2 (242)
coefficient on X1i (235) adjusted R21R 22 (243)
slope coefficient of X2i (235) perfect multicollinearity (246)
coefficient on X2i (235) dummy variable trap (250)
holding X2 constant (236) imperfect multicollinearity (251)
controlling for X2 (236)
partial effect (236)

population multiple regression model

(237)

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Review the Concepts

6.1 A researcher is estimating the effect of studying on the test scores of stu-
dent’s from a private school. She is concerned, however, that she does not