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- In this exercise, you will investigate the relationship between earnings and height. These data are taken from the US National Health Interview Survey for 1994. Download the data from the table by clicking the download table icon . A detailed description of the variables used in the dataset is available here LOADING... . Use a statistical package of your choice to answer the following questions. Earnings and…14. Estimating the variance of the error term Suppose you are interested in looking at the determinants of a worker's wage, and use the following econometric model to do so: wage=β0+β1 educ+u wage=β0+β1 educ+u where educ = years of education u = error term You take a sample of 120 individuals and collect data on each person's wage and level of educational attainment. An unbiased, observable estimator of the variance of the error term (σ2?2) is σˆ2?^2Describe the Probabilistic Analysis?
- Comment on why there exists a trade-off between variance and bias of of OLS estimators when considering the choice of including a particular explanatory variable in your underlying model. Please keep your answer precise.Suppose we use the linear probability model to estimate the effect of household income on the willingness to avail a loan product. Given a regression equation: Willingness = 22.34 -0.04HouseholdIncome, with HouseholdIncome measured in hundreds of pesos. How is -0.04 interpreted?What is the difference between an unbiased estimator and a consistentestimator?
- Why does dependent variables must covariate in the multivariate analyses like MANOVA/MANCOVA? What happens if they don't covariate, will you be able to proceed in multivariate analyses?does the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?1. The following data represent the number of days absent and the final grade for a sample of college students in a general education courses at a large state university. 1 2 3 5 6 7 8 9 # of absences 0 Final Grade 89.2 86.4 83.5 81.1 78.2 73.9 64.3 71.8 65.5 66.2 a. Test whether there is a linear correlation between the number of absences and the final grade, use 0.01 level of significance. Assume requirements satisfied. b. Find the equation of least square regression line. c. Find the best predicted final grade for a student who misses five class periods.
- 13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True FalseWrite first-order model relating E(y) to four quantitative independent variables.In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..