Suppose you regress the natural log of total family expenditures on household size using observations of households from the 201
3 Consumer Expenditure Survey:
. reg Intotexppq fam_size
Source SS df MS Number of obs = 6,766
Model F(1, 6764) = 634.72
Residual Prob > F = 0.0000
R-squared = 0.0858
Adj R-squared = 0.0857
Total | 5540.26944 6,765 818960744 Root MSE = .86534
Intotexppp | Coef. Std. Err. t P>I t I |[95% Conf. Interval]
fam_size | .1767089 .007014 25.19 0.000
cons | .0203324 404.71 0.000
From the information given, you should be able to answer the following questions about the regression:
• (4) Interpret your estimate of the coefficient on family size.
• (4) Compute the 95% confidence interval for the coefficient on family size.
• (6) Compute the estimate of the intercept of the regression line.
• (5) Compute the Explained Sum of Squares and Residual Sum of Squares of the model.
• (6) How would you assess whether the error terms in your model were homoskedastic or heteroskedastic? Include a picture if you need to.
. reg Intotexppq fam_size
Source SS df MS Number of obs = 6,766
Model F(1, 6764) = 634.72
Residual Prob > F = 0.0000
R-squared = 0.0858
Adj R-squared = 0.0857
Total | 5540.26944 6,765 818960744 Root MSE = .86534
Intotexppp | Coef. Std. Err. t P>I t I |[95% Conf. Interval]
fam_size | .1767089 .007014 25.19 0.000
cons | .0203324 404.71 0.000
From the information given, you should be able to answer the following questions about the regression:
• (4) Interpret your estimate of the coefficient on family size.
• (4) Compute the 95% confidence interval for the coefficient on family size.
• (6) Compute the estimate of the intercept of the regression line.
• (5) Compute the Explained Sum of Squares and Residual Sum of Squares of the model.
• (6) How would you assess whether the error terms in your model were homoskedastic or heteroskedastic? Include a picture if you need to.
1 answer:
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Plugging in values, we get that (1,-4) works.