How can I find the exact p-value for each coefficient (p>t )column if the number of observations
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How can I find the exact p-value for each coefficient (p>t )column if the number of observations = 52, df numerator is 3, df denominator is 48, t static is there in the third column.
I need to know the exact number for p>t each rows.
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eq 1 The following is STATA output from a two variable OLS regression on gas consumption that we demonstrated in class: Source SS df MS Model 2.7723 Residual 0.136253108 Total 2.90855 3. N 00283 0.924108071 48 D 50 Std. Err. t Ka N-K Number Number of obs F(?,?) Prob > F R-squared Adj R-squared = Root MSE =1 .9531 053 27 2.0423 4 XXN Log gas Post Coef. dung after 19737 log_gasp Zlog_gasp_post-1.854321 cons eq 2 5.76518 0.3765519 15.31 1.967916 0.1191667 16.51 0.1228435 -15.09 -18.33233 0.3545327 -51.71 Selb 325.55 P>t [95% Conf. .65 Interval] 5.008071 6.522288 .019030754 Number of obs F(1,50) Prob > F R-squared Adj R-squared- Root MSE Source SS S5 df MS 1 Model Residual .951537676 50 Total 51 Coef. Std. Err. t X P Log gas post cons dummy 197737 0.395377 -12.4807 0.038989 10.14 0.030104 -414.59 0 0 symmetric vcov[4,4] eq 1 = " " " "I 1.728315 2.207517 -2.101314 -1.607328 -19.04517 -17.6195 b Exselby) .13795 P>t [95% Conf. Interval) 0.317065 0.473688 -12.5412 -12.4203 post @g_gasp log_gasp_post _cons Post 1418 log_gasp 0.0422 0.0142 log gasp post -0.0460 -0.0142 0.0151 cons -0.1257 -0.0422 0.0422 0.1257 eq 1 The following is STATA output from a two variable OLS regression on gas consumption that we demonstrated in class: Source SS df MS Model 2.7723 Residual 0.136253108 Total 2.90855 3. N 00283 0.924108071 48 D 50 Std. Err. t Ka N-K Number Number of obs F(?,?) Prob > F R-squared Adj R-squared = Root MSE =1 .9531 053 27 2.0423 4 XXN Log gas Post Coef. dung after 19737 log_gasp Zlog_gasp_post-1.854321 cons eq 2 5.76518 0.3765519 15.31 1.967916 0.1191667 16.51 0.1228435 -15.09 -18.33233 0.3545327 -51.71 Selb 325.55 P>t [95% Conf. .65 Interval] 5.008071 6.522288 .019030754 Number of obs F(1,50) Prob > F R-squared Adj R-squared- Root MSE Source SS S5 df MS 1 Model Residual .951537676 50 Total 51 Coef. Std. Err. t X P Log gas post cons dummy 197737 0.395377 -12.4807 0.038989 10.14 0.030104 -414.59 0 0 symmetric vcov[4,4] eq 1 = " " " "I 1.728315 2.207517 -2.101314 -1.607328 -19.04517 -17.6195 b Exselby) .13795 P>t [95% Conf. Interval) 0.317065 0.473688 -12.5412 -12.4203 post @g_gasp log_gasp_post _cons Post 1418 log_gasp 0.0422 0.0142 log gasp post -0.0460 -0.0142 0.0151 cons -0.1257 -0.0422 0.0422 0.1257
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