ii.Interpret both slope coefficients of the estimated OLS regression of wage (inrupees) on education (in years) and experience (in years). What is the overallpredicted wage change if education and experience change by one year each?=Wage 30000+1500 education + 500 experienceWhat can you infer about the shape (U-shape/inverse U-shape) of therelationship between job performance (the number of tasks completed per day)and hours of sleep a person gets from the following estimated OLS regression?At what level of sleep does the job performance peak?Job Performance 20+5 hours of sleep-0.25 (hours of sleep)2

The objective of the question is to interpret the coefficients of two different Ordinary Least…

Answered: ii. Interpret both slope coefficients…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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3. Rubax, a U.S. manufacturer of athletic shoes, estimates the following linear trend model for shoe sales: Qt = a+bt+c1D1+c2D2+c3D3Where, Qt = sales of athletic shoes in the t-th quartert = 1, 2, . . . . , 28 [2011(I), 2011(II), . . . . , 2017(IV)]D1 = 1 if t is quarter I (winter); 0 otherwiseD2 = 1 if t is quarter II (spring); 0 otherwiseD3 = 1 if t is quarter III (summer); 0 otherwiseThe regression analysis produces the following results: Is there sufficient statistical evidence of an upward trend in shoe sales? explain Do these data indicate a statistically significant seasonal pattern of sales for Rubax shoes? If so, what is the seasonal pattern exhibited by the data? Using the estimated forecast equation, forecast sales of Rubax shoes for 2018(III) and 2019(II).
When running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?
J 4
Chegg Home Expert Q&A My solutions Student question Time Left: 00:09:16 Use the data set Home Morloage Disdosure Act Data (HMDA) from the AER package. Subtract 1 from the factor "Was the mortage denied?". Then, eatimate the Inear regression model of paymentsto - income rato on the factor "Was the mortage denied?" ising Ols. Based on these results, the prebabity a mertougo will be denled if the payment - to - income ratio is equal to 2 is a. 1.13 b. 0.107 c. None of these d. 0.52 e. 1.00 Use the data set Home Mortgage Disclosure Act Data (HMDA) from the AER package. Subtract 1 from the factor "Was the mortage denied?". Then, estimate the linear regression model of payments-to-income ratio on the factor "Was the mortage denied?" using OLS. Based on these results, the probability a mortgage will be denied if the payment-to-income ratio is equal to 2 is a. 1.13 Ob.0.87 Oc None of these d.0.52 1.00
Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.
7. The Stata data set "college_gpa" has data on students' college GPAS, high school GPAS, ACT scores, lectures skipped during the academic year, and other characteristics. We wish to examine the predictors of college GPAS. You must submit your do-file (commands). a) Regress college GPA on high school GPA and write the estimated regression line. b) Interpret the intercept of your regression in a sentence. Does this coefficient make sense?n Explain c) Interpret the coefficient on high school GPA in a sentence. Does this coefficient make sense? Explain. reg colGPA hsGPA Source df MS Number of obs 141 %3D F(1, 139) 33.80 Model 5.58478164 1 5.58478164 Prob > F e.0000 Residual 22.9668501 139 .165229138 R-squared Adj R-squared 0.1956 %3D 0.1898 %3D Total 28.5516318 140 .203940227 Root MSE .40648 colGPA Coef. Std. Err. t P>|t| [95% Conf. Interval] hsGPA .6242948 .1073816 5.81 0.000 .4119822 .8366073 _cons 890254 3669263 2.43 0.017 647755
Suppose the researcher considers the following model : Wage = Bo+B,Female + u, and runs OLS, using wage data on 250 randomly selected male workers and 280 female workers. The researcher has obtained the estimated equation as Wage = 15 (1.00) 3 Female, R = 0.05. (0.5) In the equation, Wage is measured in dollars per hour, Female is a binary variable that is equal to 1 if the person is a female and O if the person is a male. The numbers in the parentheses are the standard errors of the coefficients. Which Statement is NOT correct? The coefficient of Female, -3.00, is statistically significant at 5%. O The p-value for the test that Ho: B = 0, H : B 0 is less than 0.05. This regression may suffer omitted variable bias. R Since insignificant. is too low, the wage difference between men and women is The researcher can increase R- by including more regressors in the model.
QUESTION 7 Which is NOT true about the coefficient of determination? As you add more variables, the R-square generally rises. As you add more variables, the adjusted R-square can fall. If the R-square is above 50%, the regression is considered significant. The R-square gives the percent of the variation in the dependent variable that is explained by the independent variables. The higher is the R-square, the better is the fit.
3. Boyoung is writing a paper about the effect of Sunday liquor sales on drunk driving. She has panel data on which states allow liquor to be sold on Sunday in which years and wishes to estimate a difference-in-differences model. She writes the following regression equation: Year DUIRate;; = atate +a? + BTreatmentt +yControl;t + Eit i Which of the following changes does Boyoung need to make? a) She should include a constant in the regression equation. b) She should not include controls because they're already accounted for by fixed effects. c) She should include a time trend instead of time-fixed effects. d) None of the above are changes she needs to make.
1. Suppose output (Q) is related to labor (L) and capital (K) in the following nonlinear way: Q = albKc When taking log to this equation, it is transformed into a linear LnQ = Ina + b In(L) + c Ln (K) One hundred twenty-three observations are used to obtain the following regression results: Dependant Variable: Observations: Variable Intercept L K Q 123 5.5215 Parameter Standard Estimate error 0.650 R-square 0.350 0.7547 0.9750 0.2950 0.1450 F-ratio 184.56 t-ratio 5.66 2.20 2.41 p-value on F 0.00001 p-value 0.0001 0.0295 0.0173 a. Write the regression equation based on the output either in the transformed linear form or the original non-linear form.
number 1 please
Consider a regression in which a coefficient suffers from downward omitted variable bias. If you add a regressor that controls for the omitted variable bias: Group of answer choices 1. Can’t determine 2. The new estimate will be smaller. 3. The new estimate will remain unchanged. 4. The new estimate will be larger

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