Suppose you estimated a simple linear regression model involving log hourly wage rate and experience for a sample of 1000 people. The estimated results are given below: Dependant Variable: log(w) Coefficients Standard Error t-Statistic 10.0508 intercept experience 0.17 2.56 0.3546 0.1986 12.4673 Based on the above estimated results, which of the following represents the fitted line, i.e. the sample regression function? a) log (w)i =2.56 + 0.17experience; + u; b) log (w)i =2.56 + 0.17experience; + û¡ c) log (w)j =0.17 + 2.56experience; d) log (w)i =2.56 + 0.17experience; e) none of the above

Suppose you estimated a simple linear regression model involving log hourly wage rate and experience for a sample of 1000 people. The estimated results are given below: Dependant Variable: log(w) Coefficients Standard Error t-Statistic 10.0508 intercept experience 0.17 2.56 0.3546 0.1986 12.4673 Based on the above estimated results, which of the following represents the fitted line, i.e. the sample regression function? a) log (w)i =2.56 + 0.17experience; + u; b) log (w)i =2.56 + 0.17experience; + û¡ c) log (w)j =0.17 + 2.56experience; d) log (w)i =2.56 + 0.17experience; e) none of the above

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

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 4E

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True or False For a linear regression model including only an intercept, the OLS estimator of that intercept is equal to the sample mean of the independent variable.
Question 15 When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.
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The regression equation to predict sales based on temperature is: Predicted sales = -2419.01+ 98.02 (temperature). A correct interpretation of the slope would be that 1. as temperature goes up by 1 degree, sales are predicted to go down by 2419.01. 2. as temperature goes down by 1 degree, sales are predicted to go up by 2419.01. 3. as temperature goes up by 1 degree, sales are predicted to go down by 98.02. 4. as temperature goes up by 1 degree, sales are predicted to go up by 98.02. 5. None of the answer choices provides a correct interpretation of the slope.
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