None The variables in our dataset include the college gradepoint average (colGPA), high school GPA (hsGPA), andachievement test score (ACT) for a sample of 141students from a large university; both college and highschool GPAs are on a four-point scale. We estimate thefollowing model by OLS:colGPA = \beta, + \beta 2hsGPA+\beta 3ACT + u,and obtain the following parameter estimates \beta1= 0.3, \beta 2 = 0.38, and \beta 3 = 0.037. Predict thecollege GPA of a student with high school GPA of 3.5and ACT score of 30.

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Answered: The variables in our dataset include…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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This exercise refers to the drunk driving panel data regression, summarizedin Regression analysis of Drunk Driving (see attachment)a. New Jersey has a population of 8.85 million people. Suppose that NewJersey increased the tax on a case of beer by $2 (in 1988 dollars). Use theresults in column (5) to predict the number of lives that would be savedover the next year. Construct a 99% confidence interval for your answer. b. The drinking age in New Jersey is 21. Suppose that New Jersey loweredits drinking age to 19. Use the results in column (5) to predict the changein the number of traffic fatalities in the next year. Construct a 95% confidence interval for your answer.c. Suppose that real income per capita in New Jersey increases by 3% inthe next year. Use the results in column (6) to predict the change in thenumber of traffic fatalities in the next year. Construct a 95% confidenceinterval for your answer.d. How should standard errors be clustered in the regressions in columns(2) through…
Suppose you have the following data: X Y 3 9 7 45 5 25 2 8 Using OLS methods: estimate the following regression model: Sq root of Yi = Beta0 + Beta1(Xi) + error
Show the graphical form of the econometric error using sample regression line (SRL) and the population regression line(PRL).
Sir Francis Galton, a cousin of James Darwin, examined the relationship between the height of children and their parents towards the end of the 19th century. It is from this study that the name "regression" originated. You decide to update his findings by collecting data from 110 college students, and estimate the following relationship: Studen th = 19.6 + 0.73 × Midparh, R2 = 0.45, SER = 2.0 (7.2) (0.10) where Studenth is the height of students in inches, and Midparh is the average of the parental heights. Values in parentheses are heteroskedasticity robust standard errors. Interpret the estimated slope coefficient and intercept coefficient. What is the meaning of the regression R2 ? What is the prediction for the height of a child whose parents have an average height of 70.06 inches? a. b. с. d. What is the interpretation of the SER here? Is the slope coefficient statistically significantly different from zero (at the 5% significance level)? e.
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Let ŷ represent the profit (or loss) for a certain company z years after 1965. Based on the data shown below, a statistician calculates a linear model ŷ = - 2.76x + 54.33. 2 50.05 3 46.3 4 42.25 5 41.4 6 36.35 7 34.5 31.85 30.5 10 26.45 11 23.1 12 20.85 13 19.9 Use the model to estimate the profit in 1978. Round to the nearest cent. The estimated profit in 1978 is $
A variety of summary statistics were collected for a small sample (13) of bivariate data, where the dependent variable was y and an independent variable was x. Thex variable is the number of doctorates awarded in sociology in the United States, while the y variable is the total number of space launches per year. We call this a spurious regression where the results show a relationship but the relationship is silly or utter nonsense. EX, = 7.615 ΣΥ- 583 n-13 SSE - 125.9 Estimate the least squares estimate of bo. ECY - D(X, -8) = 1.609.5 E(X, -8) = 12.466.3 E(Y - P) = 333.7 Answer:
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You have noticed that paying higher wages attracts more productive employees. However, you are concerned that there may be a limit to this relationship. Some experimentation has convinced you that the relationship between wages paid (x) and profits can be modelled by the equation Profit = 25x − 0.1x2 + 200. Use a Nonlinear Solver tool to determine the level of wages that will maximize profits.
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