MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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Explain how we can handle heteroskedasticity by using Weighted Least Squares?
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In regression analysis, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values. It violates the assumption made by Ordinary least square that all the residuals are drawn from a population that has a constant variance (homoscedasticity).
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- 5 How does the dynamic CCE estimator solve the estimation problems of the PMG technique?arrow_forwardPhoebe gathers data and estimates many di¤erent regression models. All of them suggest that children who have more books at home have fewer cavities. Phoebe doesnt think this result is important because: a.) There is clearly selection bias, kids who have lots of books at come from higher income and better educated families.b.) The data on cavities is certainly heteroskedasticc.) Phoebe only used ordinary least squares in her model, should have used weighted least squares d.) Phoebe didnt use a high enough con dence levelarrow_forwardIn a White test for heteroskedasticity, what is the degrees of freedom? The number of explanatory variables in the auxiliary regression + 1 The number of explanatory variables in the initial model + 1 The number of explanatory variables in the auxiliary regression The number of explanatory variables in the initial modelarrow_forward
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