Detailed steps/answers would be greatly appreciated so I understand how you arri

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Detailed steps/answers would be greatly appreciated so I understand how you arrived at the answers. Thank you!

A simple linear regression equation is to see if the explanatory variable (x,) causes the dependent variable (y,). If the estimated slope coefficient beta is zero in a simple regression (y, = a + BetaX_i + uj), then R^2 = 0. The regression error Uj needs to be normally distributed for the Gauss-Markov Theorem (i.e., the OLS estimator is BLUE). When there are omitted variables in a multiple regression, which are determinants of the dependent variable, then the OLS estimators are always biased. Under near (or imperfect) multicollinearity, the standard errors of the OLS estimators become close to zero. In the following regression equation: we can obtain the LS estimates for all the parameters, alpha,beta_1ibeta_2,beta_3,beta_4,beta_5.

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Detailed steps/answers would be greatly appreciated so I understand how you arri

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