MIDTERM: Unbiasness – if expected value of estimator = population characteristic than its unbiased Consistency – if the sample size becomes large, the distribution of the estimator collapses to a spike located at the true value of the dist. Efficient – estimator has minimum variance R 2 – Greater Variation in Q but not P? Greater variation = R 2 should fall since TSS is larger compared to ESS Assumptions: 1) X causes Y, Y doesn’t Cause X 2) Correctly measured 3) Non-Redundant, Variation in X: ERROR Ui: T/F 1) If there’s no variation in Dep. V. you can’t estimate slope? FALSE, slope=0 2)If there’s no variation in Ind. V you can’t estimate slope? TRUE, undefined 0/x 3)The VAR of error term differs across all observations in OLS called independence? FALSE, called homoskedasticity or VAR of error term is constant 4)Supply f(x) is perf. Elastic (flat) & varies overtime. You can estimate model with P as depend. V. and Q as indp. V.? FALSE, Q is explained by P, not visaversa. TRUE, E(X+Y)=E(X)+E(Y)=10 Short Answer: 1) Assume that you estimated GPA=3 + .05*Hours. Your get BMW at least a 3.5. You hate study and use equation to study 10hrs/week. You get a 4.3. U think maybe you studied too much. You remember: 1)This is just a forecast 2)Error Term, actual GPA will vary upon variation of error term 3)coeffecient estimates differ for each peep 2)Suppose R.V. has PDFof f(x)=a for x’s b/w -10 & 6 & =0 otherwise.
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