Model A Model c . regress SBP i.village Source MS MS 381 0.6577 .0133 Adj R-squa

Question

Model A Model c . regress SBP i.village Source MS MS 381 0.6577 .0133 Adj R-squared-0.0052 25.783 3336.32719 7 476.618171 Prob > F Mode22980.3184 Residual 228313.671 373 664.765851 R-squared 379 602.18742 R-squared 251293.99 380 661.299972 Root MSE 0.0891 251293.99 380 661.299972 Root MSE 195% conf. Interval] t P>It] [95% Conf. Interval 94358 14.35152 14.30208 17.08335 14.02417 10.03007 4893 e.326 5.984756 6.081754 3.375 5.582195 5.66849 5.733491 5.772061 2 6.114988 -1.16886 5.695304 9.089286 6.306127 5.974867 -7.601517 7.958863 -5.616368 -10.02417 -12.36779 -3.310732 6784493 cons 133.0096 1.257432 105.78 0.0 130.5372 135.482 5146198 .0833211 6.18 0.000 3507904 Juan Sanchez Altagrac Los Gueneos San Antonio 838 129.625 4.557843 0e0 120 138.5873 cons . regress SBP age i.sex df regress sB age 1.sex 1.vittage 6948 Residua 225795.895 2 12749.0474 Prob F 378 597.343637 R-sqared . 1025 0.0967 MS Adj R-squared = 27598.1374 223695.852 9 3066.45971 Prob >F 371 602.953779 R-squared 380 661.299972 Root MSE 0.1098 51293.99 38 661.299972 Root MSE 24.555 SBP t P> | t I 195% Conf. Interval] 195% conf. Interval 5642883 0864251 3943542 7342224 5S2506 0871658 3811049 723907 sex -5.727008 2.789532 .05 0.041 -11.21195 242064 99.55401 115.9942 -5.585589 2.846715 -11.18331 0121314 107.7741 4.180562 25.78 0.000 4.94172 5.806355 2.476232 5.318357 2.247755 5.420892 4.877723 5.516077 1.873748 5.826942 .5588276 5.436059 6.91397 0.85 .395 -6.475774 16.35921 Carmona -8.411772 -5.968975 12.90728 15.72442 Juan Sanchez La Altagracia Los Gueneos San Antonio -11.24818 -4.670358 10.13052 18.98113 7.155386 105.6328 5.893864 94.04328 2224 . regress SBP age i.sex agesex Model 26243.5819 Residual 225050.408 3 8747.86064 ProbF 377 596.950683 R-squared Adj R-squared 0.0973 251293.99 380 661.299972 Root MSE SBP Std [95% Conf. Interval] 6298259 .1 1044211 4245053 8351466 4.919118 9.926412 -.207772 104.8213 4.944407 21.20 0.000 24.43719 1578066 0.50 0.620 -14.59895 1859243 1.12 0.264 -.5733506 95 , 09926 agesex cons

Explanation / Answer

The t test the p value indicate that The for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low p-value is likely to be a meaningful addition to your model because changes in the predictor's value are related to changes in the response variable. Therefore the t test above indicates that there no difference between batey verde and other villages An F-test in regression compares the fits of different linear models. Unlike t-tests that can assess only one regression coefficient at a time, the F-test can assess multiple coefficients simultaneously. Therefore it test the hypothesis that sbs value s does not varies across all villges

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