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(a) In ordinary least squares estimation, less weight is given to observations with a lower error variance. (b) Whenever there is strong heteroskedasticity, it is preferable to use OLS rather than WLS, which may use a possibly misspecified variance function. (c) The variance of the slope estimator increases as the error variance de- creases. (d) The following simple model is used to determine the annual savings of an individual on the basis of his annual income and education. savings = a_0 + a_1edu + a_2inc + u The variable edu takes a value of 1 if the person is educated and the variable inc measures the income of the individual. We can conclude that the bench- mark group in this model is the group of uneducated people.

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If heteroskedasticity is present, the Ordinary Regression Analysis approximations are not the best linearly unbiased estimators.

The definition of heteroscedasticity:

Heteroskedastic describes a situation in which a regression model's residual term, or measurement error, variance fluctuates significantly. If this is the case, there may be a factor which can explain why it varies in a predictable manner.

Heteroskedasticity: What Is It?

When the variances of a predictor are heteroskedastic (or heteroscedastic), it signifies that the variability of the errors is indeed not constant across data. Particularly, estimated coefficients may influence the variability of the mistakes. Non-constant measures are those that are tracked over a range of independent variable values or in relation to earlier time periods.

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