example. 5 B. What is population regression function and sample regression function? Give example, 5 C. What do you mean by linearity of a regression model? Explain with example. 5 D. What is type-1-error and type-II-error? Explain with examples. 3 1. Model 1: 12 Source SS df MS Model Residual 1 .333987259 .00888526 2 7 16699363 001269323 Number of obs FC 2, 7) = Prob > F R-squared Adj R-squared Root MSE 10 131.56 0.0000 0.9741 0.9667 03563 Total 1 .342872519 9 .038096947 1nQd 1 Coef Std. Err. t P>It) [95% Conf. Interval] Inp! -1.406063 3228126 --4.36 0.003 Ini 1 0.4179501 4873427 -0.86 0.419 cons 1 12.71943 4.194799 3.03 0.019 2.800309 InQd log value of quantity demanded; price in a dollar and income in one hundred 22.63855 Interpret the coefficients and construct the 95% confidence intervals of them. Where. I = 2.856 and Fiel = 8.47 example. 5 B. What is population regression function and sample regression function? Give example, 5 C. What do you mean by linearity of a regression model? Explain with example. 5 D. What is type-1-error and type-II-error? Explain with examples. 3 1. Model 1: 12 Source SS df MS Model Residual 1 .333987259 .00888526 2 7 16699363 001269323 Number of obs FC 2, 7) = Prob > F R-squared Adj R-squared Root MSE 10 131.56 0.0000 0.9741 0.9667 03563 Total 1 .342872519 9 .038096947 1nQd 1 Coef Std. Err. t P>It) [95% Conf. Interval] Inp! -1.406063 3228126 --4.36 0.003 Ini 1 0.4179501 4873427 -0.86 0.419 cons 1 12.71943 4.194799 3.03 0.019 2.800309 InQd log value of quantity demanded; price in a dollar and income in one hundred 22.63855 Interpret the coefficients and construct the 95% confidence intervals of them. Where. I = 2.856 and Fiel = 8.47