In a regression of wages on years of education we find thatTSS = 4, 367ESS= 349.46SSR = 4, 017.64The R? is: Consider the linear regression model Y; = Bo + B1X; + U; for each i = 1, ..., n, with n large. Let B1.n bethe OLS estimate of B1. Let c be such that Pr(Bn > c) = 0.005. The 99% confidence interval for B1 is•••)CO (B1,n – SE(Ŝ1,n) × c, B1,n + SE(Ŝ1,m) × c).O IB1,n – 2 x SE(Î1,n) × c, B1,n + 2 × SE(Ŝ1,n) × c).O l31,n – c, Bì,n + c].O B1,n – 2c, B1,n + 2c].-

We have given that Total sum of square TSS =4367 Explained sum of square ESS=349.46 Residual sum of…

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In a regression of wages on years of education we find that TSS = 4, 367 ESS = 349.46 SSR = 4,017.64 The R2 is:

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 2E

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In regression model: Yi = B1+ B2Xi+ Ei, where Ei is a random variable independent of Xi, with mean and variance constant and different from zero. Ei ~(media, varianza) Obtain the OLS estimator of ẞ2. Show that the properties of finite samples hold.
(e) Suppose you have been given the following ordinary least squares (OLS) regression result. Estimated Long Run Coefficients using the ARDL Approach ARDL (1,2,2,2,0,2) selected based on Akaike Information Criterion Dependent variable is LY 33 observations used for estimation from 1987 to 2019 T-Ratio [Prob.] 4.6671[0.000] 4.6678[0.051] 7.9897[0.043] -4.802[0.009] 2.3898[0.028] 1.0498[0.308] Regressor Coefficient Standard Error 0.36068 0.45447 LK 0.077280 LM 0.097363 0.48751 -0.41208 0.19057 0.52521 LE 0.061017 LF 0.085800 LT 0.079744 C 0.500320 where, Y = Economic growth K = Capital M = Employment E = Electricity consumption F = Foreign direct investment T= Technology (i) Write the regression equation. Interpret the estimated coefficients. (ii) Which explanatory variables are significant at the 1%, 5% and 10% level? Which variables are insignificant? Briefly explain.
When the regression error is heteroskedastic, all of the following statements are false, with the exception of: a. the conditional variance of the error term is not constant. b. the OLS estimator is unbiased but not consistent. C. the OLS estimator is still BLUE.
Exercises 5.1 Suppose that a rescarcher, using data on class size (CS) and average test scores from 100 third-grade classes, estimates the OLS regression TestScore- 520.4 - 5.82 x CS, R² =0.08, SER = 11.5. (20.4) (2.21) a. Construct a 95% confidence interval for B, the regression slope coef- ficient. b. Calculate the p-value for the two-sided test of the null hypothesis Hs B1 -0. Do you reject the null hypothesis at the 5% level? At the 1% level?
The sum of squared residuals for the first group (SSR1) is 15.3 and for the second group (SSR2) is 25.7. Each group contains 12 observations after removing the central observations and this model has only one independent variable. Calculate the F-statistic (rounded to two dp) (a) 2.57 (b) 2.41 (c) 1.92 (d) 1.68 (e) 1.67
A large school district is reevaluating its teachers' salaries. They have decided to use regression analysis to predict mean teacher salaries at each elementary school. The research has come up with the following prediction equation: Y = $18012.24 + 1432.37X1 - 4.07 X2 where X1 = Yrs Exp and X2 = Yrs Exp2 (a) If a teacher has 7 years of experience, what is the expected salary? (b) If teacher has 10 years of experience, what is the expected salary?
Consider a data set with 15 observations and consider a multiple linear regression model with 7 in-dependent variables. Assume you have estimated the model and you find that SST = 1,325 and SSR = 794.
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1. A study published in 1993 used US state panel data to investigate the relationshipbetween minimum wage and the employment of teenagers. The sample period was1977 to 1989 for all 50 states. The author estimated a model of the following type:ln(Eit) = β0 + β1ln(Mit/Wit) + γ2D2i + ... + γnD50i + δ2B2t + ... + δTB13t + uitwhere E is the employment to population ratio of teenagers, M is the nominalminimum wage, and W is average hourly earnings in manufacturing. In addition,other explanatory variables, such as the adult unemployment rate, the teenagepopulation share, and the teenage enrollment rate in school, were included.(a) Name some of the factors that might be picked up by time and state fixedeffects(b) The author decided to use eight regional dummy variables instead of the 49state dummy variables. What is the implicit assumption made by the author?Could you test for its validity? How?(c) The results, using time and region fixed effects only, were as follows:ˆ ln(EIT ) =…
File Home Insert Draw Design Layout References Mailings Review View Help 7. Given the following estimated regression line Y = 10.0 + 6.0 x X. R2 = 0.40, SER = 2.0, n = 500 (8.0) (4.0) The standard errors are in the parentheses (1) Construct a 99% two-sided confidence interval for the slope coefficient (B,) Alpha=1-(99/100) =0.01 Critical probability = 1-(alpha/2) =1-0.005-0.995 df = n-2 = 500 – 2 =498 (2) Construct a 95% two-sided confidence interval for the intercept coefficient (Bo). Page 8 of 10 920 words English (United States) D'Focus 館 100%
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Consider the estimated equation from Example 4.3, which can be used to study the effects of skipping class on college GPA: colGPA = 1.39.412 hsGPA+.015 ACT- .083 skipped (.33) (.094) (.011) (.026) n = 141, R² = .234. (ii) (i) Using the standard normal approximation, find the 95% confidence interval for BhsGPA- Can you reject the hypothesis Ho: ßhsGPA .4 against the two-sided alternative at the 5% level? Can you reject the hypothesis Ho: PhsGPA = 1 against the two-sided alternative at the 5% level? - (iii)