? FINDER To fit the following simple linear regression model, Y=b+bX+U you regress Y on X using
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FINDER To fit the following simple linear regression model, Y=b₂+b₁X+U you regress Y on X using STATA and expect STATA to return you a nice table of statistics. However, for some unknown reason, the output table you get is incomplete. i.e. some important statistics are missing (see below). Source Model Residual Total Y SS 42.7887174 201.446631 244.235349 Coef. df 42.7887174 1 48 4.19680482 49 Std. Err. MS 5256942 .2990746 4.98439487 t 3.19 Number of obs F(1, 48) Prob > F R-squared P>|t| = = Adj R-squared = Root MSE = 0.002 0.000 = 50 10.20 0.0025 0.1580 2.0486 [95% Conf. Interval] 2.735545 X cons 1.890187 1.288857 You need to recover all missing values in the output table above based on the available information therein. Each value is worth 5 points. NOTE: All your calculations should be accurate to TWO decimal places. The estimated slope coefficient by on X is the t-statistic associated with the intercept bo is the missing lower bound for the 95% confidence interval (CI) of by is missing upper bound for the 95% Cl of bois and the R² of the regression mode is FINDER To fit the following simple linear regression model, Y=b₂+b₁X+U you regress Y on X using STATA and expect STATA to return you a nice table of statistics. However, for some unknown reason, the output table you get is incomplete. i.e. some important statistics are missing (see below). Source Model Residual Total Y SS 42.7887174 201.446631 244.235349 Coef. df 42.7887174 1 48 4.19680482 49 Std. Err. MS 5256942 .2990746 4.98439487 t 3.19 Number of obs F(1, 48) Prob > F R-squared P>|t| = = Adj R-squared = Root MSE = 0.002 0.000 = 50 10.20 0.0025 0.1580 2.0486 [95% Conf. Interval] 2.735545 X cons 1.890187 1.288857 You need to recover all missing values in the output table above based on the available information therein. Each value is worth 5 points. NOTE: All your calculations should be accurate to TWO decimal places. The estimated slope coefficient by on X is the t-statistic associated with the intercept bo is the missing lower bound for the 95% confidence interval (CI) of by is missing upper bound for the 95% Cl of bois and the R² of the regression mode is
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The estimated slope coefficient b on x is 319 x05 256 942 167696 the Astatistic asso... View the full answer
Related Book For
Probability & Statistics for Engineers & Scientists
ISBN: 978-0130415295
7th Edition
Authors: Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying
Posted Date:
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