l estimate a multiple linear regression model with three explanatory variables and a sample size of 363 observations. The RSS of this model is 0.428217, the adjusted R squared is 0.453103. When I regress the depended variable on a constant only, I find that the RSS of this simple model is 0.789537 The value of the realized F stat for the test on the significance of the regression is: Select one: Ⓒa. 100.9721 and the null is that all betas except the intercept are simultaneously equal to zero O b. 145.056123 and the null is that the model is in the overall significant for the population statistically at 1% significance level O c. 145.056123 and the null is that all betas are simultaneously equal to zero O d. 145.056123 and the null is that all betas except the interecpt are simultaneously equal to zero O e. 100.9721 and the null is that all betas are simultaneously equal to zero Clear m My Mood my choice

l estimate a multiple linear regression model with three explanatory variables and a sample size of 363 observations. The RSS of this model is 0.428217, the adjusted R squared is 0.453103. When I regress the depended variable on a constant only, I find that the RSS of this simple model is 0.789537 The value of the realized F stat for the test on the significance of the regression is: Select one: Ⓒa. 100.9721 and the null is that all betas except the intercept are simultaneously equal to zero O b. 145.056123 and the null is that the model is in the overall significant for the population statistically at 1% significance level O c. 145.056123 and the null is that all betas are simultaneously equal to zero O d. 145.056123 and the null is that all betas except the interecpt are simultaneously equal to zero O e. 100.9721 and the null is that all betas are simultaneously equal to zero Clear m My Mood my choice

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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Water is being poured into a large, cone-shaped cistern. The volume of water, measured in cm³, is reported at different time intervals, measured in seconds. A regression analysis was completed and is displayed in the computer output. Regression Analysis: cuberoot (Volume) versus Time Predictor Coef SE Coef Constant -0.006 0.00017 -35.294 0.000 Time 0.640 0.000018 35512.6 0.000 s=0.030 R-Sq=1.000 R-sq (adj)=1.000 What is the equation of the least-squares regression line? Volume = 0.640 - 0.006(Time) Volume = 0.640 - 0.006(Time) Volume = -0.006 + 0.640(Time) Volume = - 0.006 + 0.640(Time?)
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