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 +…
Assignment-log linear model with dummy variable regressor A least squares regression model explaining the sample variation of LOGEARN,, the logarithm of reported weekly earnings for individual i, yields the estimated Bivariate Regression Model LOGEARN = 5.99+ .383 gender, + U₁ where gender, is a dummy variable which is, in this case, one for females and zero for males. The issue of how to evaluate the precision of the parameter estimates in a model such as this will be discussed in Chapters 6 and 7: you may ignore these issues in answering this question. a. What interpretation can be given to this estimated coefficient on the variable gender,? In particular, what can one say about how the expected value of the logarithm of weekly earnings depends on gender? b. What can one say about how the expected value of household earnings itself depends on gender? c. Suppose that it becomes apparent that females are, on average, better educated than males. Presuming that well-educated individuals…
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Please make a Data set and regression equation based on Student Debt and inflation with the following variables: Current student debt,interest rates, inflation rates ( will give thumbs up ) thank you so much.
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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