Suppose the population regression model, which shows the relationship between the explanatory variables (x and x2)...

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Suppose the population regression model, which shows the relationship between the explanatory variables (x₁ and x2) and the dependent variable y, is given by: log (y) = Bo + B1 x1 + B₂ x₂ + u Suppose the model has been estimated, and the results are as follows: log (y) = 3.2 +0.4 x1 +0.45x2 From the coefficient estimate of B2, holding x₁ constant, when x₂ increases by 1 unit, y becomes more inaccurate as the change in log (y) increases. However, when x₂ increases by one unit, > %. by exactly by %, which is only an approximation that %. When x₂ decreases by one unit, by exactly Which of the following are potential benefits of measuring the units of the dependent variable y in log form? Check all that apply. Logged dependent variables provide better estimates for values of the dependent variable that are less than 1. Using the logged form of the dependent variable reduces issues associated with heteroskedasticity. Logged dependent variables provide better estimates for values of the dependent variable that are less than 0. Logged variables can make OLS estimates less sensitive to outliers. Suppose the population regression model, which shows the relationship between the explanatory variables (x₁ and x2) and the dependent variable y, is given by: log (y) = Bo + B1 x1 + B₂ x₂ + u Suppose the model has been estimated, and the results are as follows: log (y) = 3.2 +0.4 x1 +0.45x2 From the coefficient estimate of B2, holding x₁ constant, when x₂ increases by 1 unit, y becomes more inaccurate as the change in log (y) increases. However, when x₂ increases by one unit, > %. by exactly by %, which is only an approximation that %. When x₂ decreases by one unit, by exactly Which of the following are potential benefits of measuring the units of the dependent variable y in log form? Check all that apply. Logged dependent variables provide better estimates for values of the dependent variable that are less than 1. Using the logged form of the dependent variable reduces issues associated with heteroskedasticity. Logged dependent variables provide better estimates for values of the dependent variable that are less than 0. Logged variables can make OLS estimates less sensitive to outliers.