_ The term μ ˆ y | x serves as the point estimate for estimating both the mean and individual prediction of y for a given x. _ The error or residual sum of squares is the numerator portion of the formula for the variance of y about the regression line. _ The x values must be randomly selected in order to use a regression analysis. ![]() _ If the true regression of y on x is curvilinear, a linear regression still provides a good approximation to that relationship. _ If x and y are uncorrelated in the population, the expected value of the estimated linear regression coefficient (slope) is zero. ![]() _ In linear regression we may extrapolate without danger. _ Rejecting the null hypothesis of no linear regression implies that changes in x cause changes in y. _ To conduct a valid regression analysis, both x and y must be approximately normally distributed. _ The correlation coefficient indicates the change in y associated with a unit change in x. _ A plot of the residuals versus the dependent variable provides a good graphical check of whether a nonlinear model is needed.
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