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Question: the coefficients for the month of observation imonth2 imonth3 etc...

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i.month naturally coded Imonth 1 omitted) Source Number of obs- Mode ї Residual .110725879 005613569 13 .008517375 Prob > F 0.0000 0.9517 0.9384 01093 000119438R-squared Adj R-squared- Total .116339448 60 .001938991Root HSE nGas [95% Conf. Interval] -.0411666 . 529831 -.0779791 0358667 020317 0702461 0442586 084409 0901632 0047944 0521291 0121249 0439756 -1.740829 0121922 0944495 0069186 -1 0.001 0.000 0.000 0.000 0.007 0.000 0.000 0.000 0.000 0.504 0.000 0. 088 0.000 0.076 - . 0656942 - .016639 . 7198389 -.0640606 0493193 0347479 0846762 0584984 098483 .1042926 0191185 0664854 0261185 0579853 .1873903 lnInc .3398232 - . 0918976-. Imonth 3 onth 6 Imonth 7 Imonth 8 Imonth 9 Imonth 10 Imonth 11 006687 0071734 0071729 0070783 0069959 0070235 0071203 0071363 006956 006964 .9584831 0224141 005886 055816 0300189 07033!5 0760337 -,0095297 0377728 -,0018687 0299658 -3.669048 7.30 cons

The coefficients for the month of observation, _Imonth_2, _Imonth3, etc. are mean effects (dummy variables) that shift the intercept of our demand equation for each month of the sample. In terms of what we know about gasoline demand, why might it be important to model different baseline gasoline consumption by month?

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