QUESTION 1: (a) Consider the simple linear regression model: Yi = ?1 + ?2 Xi + ei, ??2 = ?xi yi / ?xi^2 = ?xi Yi / ?xi^2 = ? ki Yi where ki = xi / (?xi^2) (i) Give a full description of the process of maximum likelihood estimation of the parameters ?1, ?2 and ?^2. (4) (ii) Explain the importance of the normality assumption in regression analysis. (3) (b) Give a brief discussion of the following concepts. (i) The Gauss Markov Theorem (ii) The standard error of an estimator. (iii) A t-test for significance. (iv) r^2 (v) Multiple regression (vi) The relationship between r and ??2 in a simple regression. (vii) Elasticity (viii) Partial correlation (ix) F test
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Maximum Likelihood Estimation (MLE) of the parameters $B_1$, $B_2$, and $\sigma^2$: The maximum likelihood estimation is a method used to estimate the parameters of a statistical model by maximizing the likelihood function. In the context of simple linear Show more…
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