Provide step by step solutions

You may need to use the appropriate appendix table or technology to answer this question. Suppose In 2018, RAND Corporation researchers found that 77% of all individuals ages 66 to 65 are adequately prepared financially for retirement, Many financial planners have expressed concern that a smaller percentage of those in this age group who did not complete high school are adequately prepared financially for retirement. (a) Develop appropriate hypotheses such that rejection of N, will support the conclusion that the proportion of those who are adequately prepared financially for retirement is smaller for people in the 66-69 age group who did not complete high school than it is for the population of the 66-69 year old. (Enter In for a as needed.} (b) In a random sample of 300 people from the 65-69 age group who did not complete high school, 159 were not prepared financially for retirement. What is the p- value for your hypothesis test? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to four decimal places.) p-value = (c] At a = 0.01, what is your conclusion? Do not reject He. We conclude that the percentage of 66- to 69-year-old individuals who are adequately prepared financially for retirement is smaller for those who did not complete high school. Reject Ho. We conclude that the percentage of 66- to 69-year-old individuals who are adequately prepared financially for retirement is smaller for those who did not complete high school. Reject Ho. We cannot conclude that the percentage of 66- to 69-year-old individuals who are adequately prepared financially for retirement is smaller for those who did not complete high school. Do not reject H,. We cannot conclude that the percentage of 66- to 69-vear-old individuals who are adequately pranarad financially far vatican1. (15 points) For the exercises below, transform the regression equation Yi = 60 +81Xli +$2X2i +pi so that you can use a t-statistic to test for the following restrictions. Show all your steps for full credit. a. (5 points) B1 = 1262 b. (5 points) B1 - 82 = 2 a) If we assume normality of u, then we know the exact distribution of B1, a t student b) If we do not assume normality of u but n is large, then we approximate the sampling distribution 3 2. (15 points) Consider the following model to explain CEO salaries in terms of various factors: salary = 30 + Blsales + $2mktval + 83ceoten + u, where: salary = 1990 compensation, $1000s; sales = 1990 firm sales, millions; mktval = market value, end 1990, mills; ceoten = years as ceo with company Next you perform the following regression: * R2 = 0.2013, SER = 529.67 a) (5 pts) What does each estimated coefficient on the individual variables (and constant term) mean quantitatively (do not worry about standard errors, as they are not given, or the measures of fit)? b) (5 pts) What is the forecasted salary of a CEO working in a firm with sales equal to 5,000 millions, market value equal to 10,000 millions, and 10 years of tenure? c) (5 pts) Eliminating the variable sales from your regression, the estimation regression becomes:" salary = 613.436 + .019sales + .023mktval + 12.703ceoten + u salary = 641.059 + .0369mktval + 11.525ceoten + u R2 = 0.184, SER = 533.58 Why do you think that the effect of mktval has changed now over part b. (very briefly describe)?1. Draw and properly label an AD-AS model to show Keynesian, intermediate, and neoclassical zones (4%). Then, briefly explain what are rates (whether low, high or medium) in each zone of unemployment, inflation, and real GDP. (6%) 2. Draw and properly label the AD-AS model to show recessionary and inflationary gaps (5%). Then, discuss Keynesians' solutions to closing recessionary and inflationary gaps. (10%) 3. Draw and properly label AD-AS models to show recessionary and inflationary gaps (5%). Then, discuss neoclassicals' solutions to closing recessionary and inflationary gaps. (10%)Grad - A dummy variable equal to 1 if the CEO attended a post-graduate program (e.g. MBA), 0 if not Computer - A dummy variable equal to 1 if the firm is in the computer industry, 0 if not Financial - A dummy variable equal to 1 if the firm is in the financial industry, 0 if not Here are the results of a regression of Logsalbon on the covariates: Log Salbon = 3.65+ .31 LogSales+ .0016 Fiveret+ .014 Age-.037Grad-.0037Computer+ .156 Financial ,R = .32 (.25) (.019) (.0012) (.003) (.039) (.064) (.049) Here are some tests of a few joint hypotheses: Ho : B2 = B4 = 0, F-statistic = 1.423 Ho : B2 = 35 = 0, F-statistic = 1.026 Ho : B4 = 35 = 0, F-statistic = 0.473 Ho : B2 = B4 = 35 = 0, F-statistic = 0.959 a. People frequently complain about the high salaries and bonuses earned by CEOs. Some suggest that their compensation is almost totally disconnected from the performance of their firms. Using the company's five year return (Fiveret) as a measure of a firm's performance, do you find evidence that CEO's are rewarded for good performance? What effect do you find? Justify your answer. b. Do CEO's at larger firms earn higher salaries? If so, how much? Justify your answer using Log Sales as your measure of firm size. c. Do the data provide any evidence that CEO's receive a premium (i.e. higher earnings) for having attended a post-graduate program? What effect do you find? Explain. d. Suppose I re-run the regression using Salbon instead of Log Salbon as the dependent variable and find that R" = .34. On the basis of this evidence, should I conclude that it's better to run this new regression instead of the earlier one? Explain