Suppose you have the following wage and education data for a sample with 3 observations: obs. no. 1 2 3 wage educ ($/hr) (years) 10.73 11 13.44 12 18.05 16 Calculate the coefficient for the regression of wage on education through the origin (i.e. 1 in the model wage = Bieduc). Express your answer out to two (2) decimal points.
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- How do you find the MP of X1 and X2? I need these to find overall TRS and I'm struggling to follow along with no actual figures.MP believe that there is a causal relationship between earnings and years of schooling. They show you an estimate of the impact of years of schooling on annual earnings: There are two groups of individuals – group 1 and group 2. Group 1 on average earns £60,000 per year with the average level of post-compulsory education of 6 years, while group 2 earns on average £40,000 with the average post-compulsory education of 4 years. The MPs conclude that the annual return to an additional year of education is 25% using the following equation [(£60,000-£40,000)/40,000]/(6-4)=25%. Ignoring time preferences and discounting, and if both groups face the same cost of schooling, you need to explain to the MPs why this is not a good estimate of the annual return to an additional year of education. Propose methods to the MPs to deal with the problemSuppose you examined blood of 36 patients with the aim to study the relation between sugar level in blood (in mg/dL) and the amount of artificial sweetener (measured in grams). Your regression shows: blood=7.1 + 0.4*sweatener - 0.2*female. What is the most precide interpretation of the estimated coefficient for sweetener?
- How should be the following given be treated while estimating national income (1) bonus paid to employeesWe know that discrimination exists. It influences wages, but also many other dimensions over the life cycle which affect wages indirectly. I run OLS regression with variables wage, age, female and degree. The dependent variable is log(wage) and we replace the variables female and degree with the interaction term. However, discrimination is not included among the observed regressors. Given that omitting confounding variables from regression model can bias the coefficient estimates, omitting discrimination would lead to biased results. Could you please help me provide an example of how unobserved gender discrimination can affect my OLS estimates. [Hint: think about ways in which discrimination can invalidate OLS assumptions.].Suppose you are asked to compare the variation in housing rental prices in the Eastern Caribbean measured in Eastern Caribbean dollars to housing rental prices in Jamaica measured in Jamaican dollars, explain the most appropriate statistic to use making sure to say why
- Some MPs believe that there is a causal relationship between earnings and years of schooling. They show you an estimate of the impact of years of schooling on annual earnings: There are two groups of individuals – group 1 and group 2. Group 1 on average earns £60,000 per year with the average level of post-compulsory education of 6 years, while group 2 earns on average £40,000 with the average post-compulsory education of 4 years. The MPs conclude that the annual return to an additional year of education is 25% using the following equation [(£60,000-£40,000)/40,000]/(6-4)=25%. Ignoring time preferences and discounting, and if both groups face the same cost of schooling, you need to explain to the MPs why this is not a good estimate of the annual return to an additional year of education. Propose methods to the MPs to deal with the problem. (300 words)et R2unrestricted and R2restricted be 0.54 and 0.30 respectively. The difference between the unrestricted and the restricted model is that you have imposed two restrictions. The number of regressors in the unrestricted model is 5. There are 500 observations. The F-statistic (under the assumption of homoskedasticity) in this case is 1. 4.61 2. 8.01 3. 98.5 4. 128.9A researcher estimates a model of CEO salary. She regresses this variable on CEO tenure and CEO tenure squared. The coefficient on those two variables are 20.29 and -1.14, respectively. Calculate the value of CEO tenure at which the impact of this variable on CEO salary is optimised..
- The following regression was run using a sample of 587 women living in Kenya. The variable goats is the number of goats the woman owns and grant is whether the woman received $ 500 in cash (in local currency equivalent) two years ago from the charity GiveDirectly, or not. The charity gives grants randomly to poor people in Kenya, of $ 500. goats = 6.42 +0.80 grant R2=0.35 1. What is the estimated coefficient for grant? What does it mean, in words? (Be specific, referring to numbers.) 2. Suppose one woman in the sample, Wangari, did not receive a grant. What is her predicted number of goats? Please do fast ASAP fastGNP: PLUSES AND MINUSES The gross national product in recent years has basked in the warm glow of nearly universal praise. Econo mists, quite naturally, have been freest with the encomiums. Where else but the GNP could they get in one tidy number a measure of the growth in the mammoth U.S. economy. Businessmen, though not entirely persuaded of the value of statistics, especially esoteric statistics, nevertheless have been known to quote the GNP—possibly because of its powers to impress audiences with the speaker's grasp of the "big picture." Politicians, too, have been extravagant in their admiration of GNP, most especially when it was rising at a brisk pace. Even the ubiquitous cocktail party has paid homage to the GNP as assorted junior executives, research assistants, housewives, and dancing instructors have pronounced on the latest GNP numbers with a solemn and knowing air. Now all that seems to be changing. GNP increasingly is coming under attack. Dr. Arthur F.…How should I interpret the coefficients on a regression with a naural log of a dependent variable? Ex: Ln(wage)=B1+B2Experience+B3Male...+Ui