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Essay question. In this regression analysis, you are interested in the effect of age on the amount of gasoline used by a car, measured in gallons. Suppose you analyze your dataset and find the following results. Based on the Stata output, how can you improve the analysis? State 3 issues in the figure and table, and possible solution for each problem you can apply based on what you have learned in class. gasoline_use 40 20 100 .reg gasoline_use age Source SS df MS Model Residual 28.1117271 9969.5248 1 28.1117271 23 433.4576 Number of obs F(1, 23) Prob > F R-squared 25 0.06 0.8012 0.0028 a Total 9997.63653 24 416.568189 Adj R-squared Root MSE -0.0405 20.82 gasoline_use Coef. Std. Err. t P>|t| [95% Conf. Interval] age cons -.0688645 .2704114 62.37748 13.86839 -0.25 0.801 4.50 0.000 -.6282532 .4905242 33.68853 91.06643 20 40 60 80 age

Essay question. In this regression analysis, you are interested in the effect of age on the amount of gasoline used by a car, measured in gallons. Suppose you analyze your dataset and find the following results. Based on the Stata output, how can you improve the analysis? State 3 issues in the figure and table, and possible solution for each problem you can apply based on what you have learned in class. gasoline_use 40 20 100 .reg gasoline_use age Source SS df MS Model Residual 28.1117271 9969.5248 1 28.1117271 23 433.4576 Number of obs F(1, 23) Prob > F R-squared 25 0.06 0.8012 0.0028 a Total 9997.63653 24 416.568189 Adj R-squared Root MSE -0.0405 20.82 gasoline_use Coef. Std. Err. t P>|t| [95% Conf. Interval] age cons -.0688645 .2704114 62.37748 13.86839 -0.25 0.801 4.50 0.000 -.6282532 .4905242 33.68853 91.06643 20 40 60 80 age

MACROECONOMICS FOR TODAY
MACROECONOMICS FOR TODAY
10th Edition
ISBN:9781337613057
Author:Tucker
Publisher:Tucker
Chapter1: Introducing The Economic Way Of Thinking
Section1.A: Applying Graphics To Economics
Problem 1SQ
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Essay question. In this regression analysis, you are interested in the effect of age on the amount of gasoline used by a car, measured in gallons. Suppose you analyze your dataset and find the following results. Based on the Stata output, how can you improve the analysis? State 3 issues in the figure and table, and possible solution for each problem you can apply based on what you have learned in class. gasoline_use 40 20 100 .reg gasoline_use age Source SS df MS Model Residual 28.1117271 9969.5248 1 28.1117271 23 433.4576 Number of obs F(1, 23) Prob > F R-squared 25 0.06 0.8012 0.0028 a Total 9997.63653 24 416.568189 Adj R-squared Root MSE -0.0405 20.82 gasoline_use Coef. Std. Err. t P>|t| [95% Conf. Interval] age cons -.0688645 .2704114 62.37748 13.86839 -0.25 0.801 4.50 0.000 -.6282532 .4905242 33.68853 91.06643 20 40 60 80 age
Transcribed Image Text:Essay question. In this regression analysis, you are interested in the effect of age on the amount of gasoline used by a car, measured in gallons. Suppose you analyze your dataset and find the following results. Based on the Stata output, how can you improve the analysis? State 3 issues in the figure and table, and possible solution for each problem you can apply based on what you have learned in class. gasoline_use 40 20 100 .reg gasoline_use age Source SS df MS Model Residual 28.1117271 9969.5248 1 28.1117271 23 433.4576 Number of obs F(1, 23) Prob > F R-squared 25 0.06 0.8012 0.0028 a Total 9997.63653 24 416.568189 Adj R-squared Root MSE -0.0405 20.82 gasoline_use Coef. Std. Err. t P>|t| [95% Conf. Interval] age cons -.0688645 .2704114 62.37748 13.86839 -0.25 0.801 4.50 0.000 -.6282532 .4905242 33.68853 91.06643 20 40 60 80 age
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