9. The (incomplete) table below shows the x and y values for three data points. It also showsthe estimated coefficients, predicted y values and residuals for two potential regression lines(A and B). Fill in the missing values in the table below.obs1W23231009080706050403020100y602001X328Using the values you just calculated, plot the three data points and the two estimatedregression equations in the graph below. Be sure to label everything clearlyVA40503aAABARegression AUA4-30-15510YB64060BRegression BAВв7UB25-2015820910

Here for the given data on x & y variables. We have derived 2 estimated lines A & B which…

9. The (incomplete) table below shows the x and y values for three data points. It also shows the estimated coefficients, predicted y values and residuals for two potential regression lines (A and B). Fill in the missing values in the table below. obs 123 100 90 80 y 60 20 70 X 3 8 VA 40 50 UA aA A ВА -30 -15 Regression A 10 VB Using the values you just calculated, plot the three data points and the two estimated regression equations in the graph below. Be sure to label everything clearly 40 60 UB 25 -20 15 Regression B αB 20 A Вв

9. The (incomplete) table below shows the x and y values for three data points. It also shows the estimated coefficients, predicted y values and residuals for two potential regression lines (A and B). Fill in the missing values in the table below. obs 123 100 90 80 y 60 20 70 X 3 8 VA 40 50 UA aA A ВА -30 -15 Regression A 10 VB Using the values you just calculated, plot the three data points and the two estimated regression equations in the graph below. Be sure to label everything clearly 40 60 UB 25 -20 15 Regression B αB 20 A Вв

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression you want to control for high school standing and so you run the following regression: GPA = 3.4 0.03 * HrsWrk - 0.7 * Frosh - 0.3 * Soph +0.1 * Junior (1.1) (0.013) (0.23) (0.14) (0.08) where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing. a) If you include a dummy variable for seniors, that would cause a Hint: type one word in each blank. For the rest of questions, type a number in one decimal place. b) The expected GPA of a Sophomore who works 10 hours per week is c) The expected GPA of a Senior who works 10 hours per week is d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a higher GPA than Sarah. e) Suppose you rewrite the regression as: problem. GPA = ₁HrsWrk + ß2Frosh + B2Soph +…
(Don't accept answers from Chat-GPT)You are estimating the following simple linear regression model: Edui = B0 + B1 MomEdu + ui. Where Edu is the years of schooling of an individual and MomEdu is the years of education of the individual's mother (Note: We might estimate this sort of regression to learn about intergenerational transmission of economic success.) a. Suppose you restrict your sample to individuals with MomEdui = 10 What happens to the OLS estimates? b. Suppose you have two random samples of size 100, both with the same In the first sample, half of the mothers have 12 yearsof education and half have 14 years of education. In the second sample, one quarter of of the mothers have each of 10, 12, 14. and 16 years of education. Does the variance of the OLS estimator differ between the two samples? Explain why or why not. C. Suppose you estimate the above regression using a random sample of 100 observations. Then you find another random sample of 100 with the same as the…
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You estimated a regression with the following output. Source | SS df MS Number of obs = 411 -------------+---------------------------------- F(1, 409) = 4098.54 Model | 22574040.7 1 22574040.7 Prob > F = 0.0000 Residual | 2252702.97 409 5507.83122 R-squared = 0.9093 -------------+---------------------------------- Adj R-squared = 0.9090 Total | 24826743.7 410 60553.0334 Root MSE = 74.215 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.727341 .1050822 64.02 0.000 6.520772 6.933909 _cons | -.7552724 9.26027 -0.08 0.935 -18.95894 17.44839…
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