6 In the multiple regression model, the assumption of no perfect collinearity is best described as:Select one:a. The explanatory variables will not be correlated at all.b. The explanatory variables will have correlation coefficients close to one.C. None of the explanatory variables will be an exact linear combination of the other explanatory variables.d. The dependent variable will not be correlated with the explanatory variables. The following equation reports a regression of the natural logarithm of the wages (lwage) of a sample of workers on the years of schooling(edu), years of work experience (exp), its square (exp²), and a dummy variable male which equals 1 if a person is male and 0 otherwise:Alwage=-0.305 +0.082exp – 0.032male * exp = 0.0025exp² +0.002male * exp² +0.153edu - 0.563male +0.012edu * maleAt what point another year of experience reduce lwage of a man? (round to the closest decimal)Input only a number, no explanation required.Answer:

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In the multiple regression model, the assumption of no perfect collinearity is best described as: Select one: a. The explanatory variables will not be correlated at all. O b. The explanatory variables will have correlation coefficients close to one. C C. None of the explanatory variables will be an exact linear combination of the other explanatory variables. Od. The dependent variable will not be correlated with the explanatory variables.

In the multiple regression model, the assumption of no perfect collinearity is best described as: Select one: a. The explanatory variables will not be correlated at all. O b. The explanatory variables will have correlation coefficients close to one. C C. None of the explanatory variables will be an exact linear combination of the other explanatory variables. Od. The dependent variable will not be correlated with the explanatory variables.

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

Chapter7: Production Economics

Section: Chapter Questions

Problem 1.3CE

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Question

In the multiple regression model, the assumption of no perfect collinearity is best described as:
Select one:
a. The explanatory variables will not be correlated at all.
b. The explanatory variables will have correlation coefficients close to one.
C. None of the explanatory variables will be an exact linear combination of the other explanatory variables.
d. The dependent variable will not be correlated with the explanatory variables.

The following equation reports a regression of the natural logarithm of the wages (lwage) of a sample of workers on the years of schooling
(edu), years of work experience (exp), its square (exp²), and a dummy variable male which equals 1 if a person is male and 0 otherwise:
A
lwage
=
-
0.305 +0.082exp – 0.032male * exp = 0.0025exp² +0.002male * exp² +0.153edu - 0.563male +0.012edu * male
At what point another year of experience reduce lwage of a man? (round to the closest decimal)
Input only a number, no explanation required.
Answer:

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Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed actress/actor ages in various years, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years. Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? Best Actress 27 30 30 61 30 32 46 28 61 22 43 56 D Best Actor 42 39 38 45 51 49 59 51 38 57 45 34 Find the equation of the regression line. y = + (Round the constant to one decimal place as needed. Round the coefficient to three decimal places as needed.) The best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years is years old. (Round to the nearest whole number as needed.) Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? the predicted age is the actual winner's age.
17 The fraction of the variations in WAGE explained by EDUC is ________. a 0.1478 b 0.1848 c 0.2310 d 0.2888
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Consider the following regression estimates (ID6) Source Model Residual Total wage educ exper exper2 _cons inverse u-shaped SS C. linear d. exponential 1927.87673 5232.53756 7160.41429 df -.0046123 -3.96489 642.625576 3 522 10.0240183 525 Coef. Std. Err. .5953429 .0530251 .268287 .0368969 .000822 .7521526 MS 13.6388844 Number of obs F(3, 522) Prob > F R-squared Adj R-squared Root MSE t P>|t| 0.000 11.23 7.27 0.000 -5.61 0.000 -5.27 0.000 = .4911741 .1958023 -.006227 -5.442508 = = = 526 64.11 0.0000 0.2692 0.2650 3.1661 [95% Conf. Interval] .6995118 .3407717 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant? a. u-shaped b.i -.0029975 -2.487272