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Using a random sample of 670 individuals for the population of people in the workforce in 1976, we want to estimate the impact of education on wages. Let wage denote hourly wage in 1976 U.S. dollars and let educ denote years of schooling. We obtain the following OLS regression line: wage = -0.54 + 0.54educ. How do you interpret the slope of this regression line? What is the expected difference in the hourly wage between a worker that finished four years of college and a worker with finished high school? What is the predicted wage for a person with one year of education? Does that make sense? If it is not, what is the name of this problem in econometrics? How do we deal with it?
Suppose you are interested in the effect of skipping classes on college GPA, and collect a sample of economic variables from 400 college students to analyze the problem. Included in your data are college GPA on a four-point scale (COLGPA), high school GPA on a four-point scale (HSGPA), achievement test score (ATS), and the average number of Economics 122B lectures missed per week (SKIP). Running a regression of the dependent variable COLGPA on the other explanatory variables including a constant (and homoskedastic errors) yields:
The total number of overtime hours (in 1000s) worked in a large steel mill was recorded for 16 quarters, as shown below. Year Quarter Overtime hour
Question 1 Suppose that you have 150 observations on production (yt) and investment (it), and you have estimated the following ADL(3,2) model: (1 – 0.5L – 0.1L2 – 0.05L3)yt = 0.7
How vital statistics are affects on our human life
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applications of normal probability distribution
The State Department of Taxation wishes to investigate the effect of experience, x, on the amount of time, y, required to fill out Form ST 1040AVG, the state income-averaging form.
Analysis of covariance (ANCOVA) It is initially used for an expansion of the analysis of variance which permits to the possible effects of continuous concomitant variables (suc
Old Faithful Geyser in Yellowstone National Park derives its names and fame from the regularity (and beauty) of its eruptions. Rangers usually post the predicted times of eruptions
The 4 assumptions of regression: 1. Variables are normally distributed 2. Linear relationship between the independent and dependent variables 3. Homosced
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