The following figure and table provides details of two regression models of student achievement (district average)...

     

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The following figure and table provides details of two regression models of student achievement (district average) on average district income. Income Income Constant Test Score 740 B 700 680 640 420 600 Observations R² Adjusted R² Residual Std. Error F Statistic Note: 10 50 Average District Income (Thousands of $) Table 1: OLS Results (1) 1.879*** (0.091) Dependent variable: Test Score 625.384*** (1.532) 420 0.508 3.851*** (0.304) 0.506 13.387 (df = 418) 430.830*** (df = 1: 418) -0.042*** (0.006) 607.302*** (3.046) 420 0.556 0.554 12.724 (df = 417) 261.278 (df = 2; 417) *p<0.1; **p<0.05; ***p<0.01 a) Match the regression functions (in figure) with the models in the table. (5 points) b) Do you think both models suffers from heteroscedasticity problem? What is your solution to this problem? (5 points) c) Do you think absence of "Squared-Income" in the first model causes omitted variable bias? (5 points) d) Suppose that while coding the model 2 in R, I made a mistake and wrote "Income 2" instead of "Income 2". What is the implication of this mistake? (5 points) The following figure and table provides details of two regression models of student achievement (district average) on average district income. Income Income Constant Test Score 740 B 700 680 640 420 600 Observations R² Adjusted R² Residual Std. Error F Statistic Note: 10 50 Average District Income (Thousands of $) Table 1: OLS Results (1) 1.879*** (0.091) Dependent variable: Test Score 625.384*** (1.532) 420 0.508 3.851*** (0.304) 0.506 13.387 (df = 418) 430.830*** (df = 1: 418) -0.042*** (0.006) 607.302*** (3.046) 420 0.556 0.554 12.724 (df = 417) 261.278 (df = 2; 417) *p<0.1; **p<0.05; ***p<0.01 a) Match the regression functions (in figure) with the models in the table. (5 points) b) Do you think both models suffers from heteroscedasticity problem? What is your solution to this problem? (5 points) c) Do you think absence of "Squared-Income" in the first model causes omitted variable bias? (5 points) d) Suppose that while coding the model 2 in R, I made a mistake and wrote "Income 2" instead of "Income 2". What is the implication of this mistake? (5 points)