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Madhur L.
(a) What are the assumptions for the simple linear regression model? Name at least two diagnostics that are commonly used to check these assumptions. (b) What does it mean for a point to be an outlier? For simple linear regression, what rule is commonly used to classify points as outliers? (c) What does it mean for a point to have high leverage? For simple linear regression, what rule is commonly used to classify points of high leverage? (d) For simple linear regression, what are the formulas for the error, ei, residual, êi, and standardized residual, ri? What is Var(ei) and Var(êi) (just write down the formulas, no derivation necessary)? Describe 2-3 reasons why it is useful to look at a plot of the standardized residuals versus the fitted values.
2. Address the following questions about regression analysis: Define simple linear regression. When is it most appropriate to rely on raw parameter coefficient and when is it most appropriate to rely on standardized parameter coefficients? Why is the Y-intercept estimate equal to 0 for standardized estimates? What are the steps in interpreting a regression model? Please answer all parts.
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